Microarrays have emerged as the preferred platform for high throughput gene expression analysis. Cross-hybridization among genes with high sequence similarities can be a source of error reducing the reliability of DNA microarray results..We have developed a tool called XHM (cross hybridization on microarrays) for assessment of the reliability of hybridization signals by detecting potential cross-hybridizations on DNA microarrays. This is done by comparing the sequences of the probes against an extensive database representing the transcriptome of the organism in question. XHM is available online at Using XHM with its user-adjustable parameters will enable scientists to check their lists of differentially expressed genes from microarray experiments for potential cross-hybridizations. This provides information that may be useful in the validation of the microarray results. The 40 k A clones ,18 obtaifrom NIA . In addifrom NIA  have bee• RefSeq Version 2 – RefSeq mRNA collection . Number • UniGene – UniGene Unique, build 129/123/164 (mouse/rat/human) . Number • TIGR gene index  – Only t• BeGIn – Bergen Gene Index version 1.0. Number of sequences: 100654/49285 (mouse/rat).BeGIn is an alternative database we have developed (using tools described in ), by proIn order to achieve high specificity and sensitivity, we favored the databases where the highest number of probes were represented (completeness) and multiple matches per probe were minimized (minimum redundancy). Our criteria for choosing an appropriate database for a given organism was based on the results obtained for our probe sets. The presence of alternative splice forms in either the probe or the target set may complicate analysis. We do not consider this explicitly in the current study.type A or type B matches. The XHM system presents to the user a list of matches satisfying the criteria, and in addition information on GC-content in similar regions, estimated melting temperature (Tm) and position on the sequence (proximity to 3' or 5' end). Although the estimated Tm may not be completely accurate under microarray hybridization conditions, particularly on solid surfaces, it can give a relative measure of the impact of the potential cross-hybridization detected. An expert user may use the information to assess the likelihood and importance of the cross-hybridization effect.The basis of the XHM system is a BLAST search  for a number of probes (P1...nP). The simulated targets were ensured to satisfy the criteria described in [i Twas contained in iP's hitlist.Experiments were performed to assess whether BLAST is able to identify potentially cross-hybridizing targets. The analysis was done by generating simulated targets  cutoffs on E-value in order to cover the intended targets. Part of the reason for this is that BLAST is designed to identify homologous sequences and the sequences satisfying the cross-hybridization criteria need not necessarily receive significant scores or E-values. Our conclusion was that BLAST can be used, but to achieve optimal results, non-default parameters for initial word size and mismatch penalty should be applied. This was taken into consideration in the web-server.We found that  with two entry points for accessing cross-hybridization information  and minimum length for type B hits (default is 15 bp), based on the findings described in [+ concentration (default is 0.1 M), and size of GC-clamp (default is 10). The Na+ concentration is used to calculate melting temperature, and the size of the GC-clamp is used to plot the GC-content throughout the query sequence.Some of the parameters are shared among the two main versions of the XHM system. These include minimum length and minimum percentage similarity for ribed in , Na+ conIn addition, the real-time BLAST version also contains BLAST specific user-adjustable parameters. Main parameters include threshold on E-value (default is 10), number of alignments in the output from BLAST and whether DUST (low complexity) filter should be used. The user can adjust other BLAST specific parameters including gap opening and extension penalties, initial word size, mismatch penalty and whether or not to allow gapped alignments.The output from the XHM system consists of different parts , XHM presents the name and identifier of the hit, identity tuples , start and end position on the probe, calculated Tm, percentage GC in alignment and type of hit (A or B).type A similarities, in the rat clone set the Rat Gene Index from TIGR appears to be the best choice. For the mouse clone set it appears that the BeGIn database is the best choice, and for the human clone set it seems that RefSeq or UniGene Unique may be two good choices. These evaluations were based on completeness (high number of probes represented) and minimum redundancy of the database. We used only the tentative consensuses (TCs) of the Gene Indices databases from TIGR. Using the "full" versions decreased the number of probes having zero hits, but substantially increased the number of probes having two or more hits (results not shown).Experiments using the three cDNA sets and the oligonucleotide set as input to the XHM system yielded the results shown in Table type A similarities, and 25 nucleotides for the type B similarities. For the oligonucleotides we used 70% identity over the whole oligonucleotide sequence (type A) and 20 nucleotides perfect identity (type B). Choosing the parameters is not trivial, and these thresholds, which are in the lower range of % similarities leading to cross-hybridizations, are meant as examples of a possible configuration. The XHM tool allows for full flexibility to experiment with the settings. The reason why we chose to consider a 200 nucleotides stretch for the cDNA probes is that sometimes these sequences contain errors, especially toward the ends.As a practical experiment, we tested the XHM system on the cDNA probe sets for human, mouse and rat, and the mouse oligonucleotide set from QIAGEN. For the cDNA sets we used the following thresholds: 70% over 200 nucleotides for type A hit, 809  had two or more hits, indicating potential cross-hybridization. In most cases, a single hit represents the probe sequence itself, and does not represent a potential cross-hybridization. Looking at a more complete database, the TIGR Rat Gene Index, only 700 (5.2%) probes had no hits, and 2712 (21.3% of the probes represented in the database) had two or more hits. A conclusion from this was that the number of rat probes having potential cross-hybridizing partners was between 16% and 21%.Results are shown in Table type A hits was 3101 or 34.2% of the probes represented in the database. RefSeq is relatively incomplete, so this number may be a conservative estimate on the cross-hybridization occurrence. For the BeGIn database only 725 probes had no hits (5.3%) and 5211 probes (about 40% of the probes in the database) had two or more hits.For the mouse cDNA probes the cross-hybridization numbers appeared to be higher. Using RefSeq, the number of probes having two or more Even though as many as 47.3% of the mouse probes were not found in the TIGR Gene Index, the proportion of probes found in the database that had two or more hits was 59%. This number is most likely an artifact caused by redundancy in the database.The difference in number of possible hybridizations found in mouse versus rat is most likely due to the fact that more mouse sequences are available.The human cDNA probes seems to have a lower cross-hybridization potential than the mouse probes. In UniGene Unique and RefSeq, looking only at the human probes that were found in the databases, just above 30% of them had two or more hits. The results using TIGR Human Gene Index suggested that almost 70% of the probes found in the database could cross-hybridize, but this is most likely an artifact caused by redundancy.The mouse oligonucleotide set is clearly less prone to cross-hybridization. Using the UniGene Unique database, we observed that 14% of the probes found in the database had two or more hits, whereas the corresponding number for the 15 k mouse cDNA probe set using UniGene Unique was 45%.A flexible tool for assessing the cross-hybridization potential of microarray probes has been developed and made available. Several transcriptome databases can be used for searching, and more may be added upon request. Using the XHM tool, analysis of three cDNA microarray probe sets and one oligonucleotide probe set revealed that a high proportion of the cDNA probes can potentially cross-hybridize with one or more other transcripts in the organism. As expected, compared to the cDNA probes, a smaller percentage of the mouse oligonucleotide probes showed a potential for cross-hybridization. This is because the oligonucleotide probe sequences are much shorter (69-mers), designed from specific regions of cDNAs to minimize cross-hybridizations. Despite increasing use of the more specific oligonucleotide arrays, cDNA sequences including full-length clone sets ,26 are wtype A similarities (at least 70% identity), 15–45% of the cDNA probes showed hybridization with two or more apparently different transcripts (disregarding the results using the TIGR Gene Indices). This high percentage of potentially cross-hybridizing genes suggests that it is essential to carefully validate results from microarray experiments, particularly where cDNA clones are used to prepare arrays.Depending on the completeness and redundancy levels of the transcriptome databases used, with the chosen cutoff for Although cross-hybridization is known as one of the main sources of errors of cDNA microarrays, the high proportions of cross-hybridizing genes detected in our test are likely to be overstated by the possible redundancies in the databases . Also one may argue that the 70% identity threshold is somewhat low.Whether two different genes that are candidates for cross-hybridization actually lead to erroneous results in hybridization experiments will depend on factors such as the level of sequence identity, the stringency of the hybridization and the relative abundances of the transcripts. For example, two potentially cross-hybridizing genes do not necessarily pose a problem unless both are expressed in the tissue or cell-line analyzed. Quick inspection of the hit-list produced by the XHM tool for a typical input of differentially regulated genes will help in identifying significant noise from cross-hybridizations. A main advantage of the XHM tool is that it allows the user to perform searches at various stringencies to detect potential cross-hybridizations. Candidate genes from microarray analysis that show potential cross-hybridizations using the database search may then be further checked using other methods such as RT-PCR and Northern blot.We have shown that a significant proportion of probes used in cDNA microarray analysis may show cross-hybridization with non-target sequences. We have developed a flexible tool, XHM, suitable for detecting potential cross-hybridization artifacts during microarray data analysis. The tool may also be used to select specific probes for preparation of microarrays.. Program code for academic use can be supplied upon request to the author.The XHM system is freely available at KF implemented the system and made a draft of the manuscript. FY and AL contributed with ideas and proofread the manuscript. IJ has formulated and supervised the work, and edited the manuscript. All authors have read and approved the final manuscript.


The wealth of information on protein structure has led to a variety of statistical analyses of the role played by individual amino acid types in the protein fold. In particular, the contact propensities between the various amino acids can be converted into folding energies that have proved useful in structure prediction. The present study addresses the relationship of protein folding propensities to the evolutionary relationship between residues.The contact preferences of residue types observed in a representative sample of protein structures are converted into a residue similarity matrix or inter-residue distance matrix. Remarkably, these distances correlate excellently with evolutionary substitution costs. Residue vectors are derived from the distance matrix. The residue vectors give a concrete picture of the grouping of residues into families sharing properties crucial for protein folding.Inter-residue distances have proved useful in showing the explicit relationship between contact preferences and evolutionary substitution rates. It is proposed that the distance matrix derived from structural analysis may be useful in aligning proteins where remote homologs share structural features. Residue vectors derived from the distance matrix illustrate the spatial arrangement of residues and point to ways in which they can be grouped. The first such analysis was performed by Miyazawa and Jernigan -3, wherean et al , and shoan et al . An analan et al . A more an et al . It is r, where ij Nis the number of possible pairings between residue type i and residue type j and ij Cis the number of these parings corresponding to residues in contact. Only non-neighbouring residues on the protein chain are considered and a pair of residues is defined to be in contact if any of their side chain atoms are within a given distance of each other. The difference in contact propensities for two amino acid types can be measured by their rms difference and we define  as our amino acid difference measure or distance matrix.The observation that evolutionarily close residues appear to have similar contact propensities leads us to postulate that the extent of similarity between the contact propensities corresponding to two particular amino acids is related to the ease with which these amino acids can be mutated into each other. We define the contact propensity as D(P) will be difficult to mutate into one another and vice versa. This is because the factors involved in determining mutation rates are dominated by those affecting the structural integrity of the protein. Such factors are residue hydropathy, size, charge and etc. Substitution matrices such as PAM and Blosum are determined from mutation rates in aligned protein sequences [ij Dabove.If we have really got a measure of the distances between residue types then it should follow that residues sharing physical properties are close together. More crucially, we expect that residues that are distant according to equences ,9. We ca, where S is the substitution rate matrix. We show below that D(P) is indeed strongly correlated with D(S). It must be stressed that D(P) and D(S) are independently derived, with one based on structure and the other on sequence alignment. Their strong correlation is indicative of the validity of our definition of inter-residue distance.That is, Relating amino acids through a structurally defined distance measure should provide a useful tool for aligning remotely homologous protein sequences. Also, a distance measure naturally leads us to look for a vector representation of the amino acids. In much the same way as average hydropathy plots are useful in structural analysis we expect that average vector profiles will also pick out various structural features. Given a vector for each residue type we can visualise the residues in some abstract space and look for natural groupings of residues and thereby find ways of reducing the effective number of residues.A representative set of crystal structures was compiled from the PDBselect25 database , which cD(P). If this matrix is really a measure of residue similarity then we should be able to correlate it with an equivalent matrix constructed from an evolutionary substitution rate matrix. In what follows we will take PAM250 as the substitution matrix. In Figure D(S) in the top triangle and D(P) in the bottom triangle for a contact cut-off of 4.5Å, where the pearson correlation (r) is maximal, with r = 0.82. See P and D(S). The correlation can be seen explicitly in Figure D(P) is consistently less well correlated with D(S), peaking at r = 0.64 for a cutoff of 8Å, see Figure As explained above the contact propensities can be converted to a distance matrix , where  are the residue vectors. Explicitly, the vectors are defined such that  is minimal. When these vectors are derived it becomes clear that Cysteine is quite separate from the other residues in this property space and this maybe due to the unique role played by Cysteine in stabilising folds. Though it must be made clear that the distance matrix is independent of the frequency of an amino acid contacting its own kind and therefore does not count Cysteine bridges in the structures. Without Cysteine the distance matrix can be projected onto a plane i.e. the vectors can be taken to be two-dimensional and this vector space is illustrated in Figure D(P) residues with opposite hydropathies tend to be further apart. This is consistent with hydropathy playing a pivotal role in protein folding. In contrast, the MJ energy matrix vector space is shown in Figure We have defined inter-residue distances and this implies that there must be a vector representation for the residues. In this case the distance matrix will be di are defined such that  is minimal. We find that these distance matrix derived scalars have a correlation of 0.65 with the Kyte-Doolittle hydrophobicity scale [The dominant driving force of folding, at least in defining the crude fold, is hydrophobicity and it is apparent that residues with similar hydrophobicities are grouped together. It also seems that residues of similar size tend to be close in this space. To make a direct comparison between existing residue scales and our vectors we can project the residue vectors onto a line. Here the amino acid scalars, one-dimensional vectors, ty scale  and a coty scale . It is cPij = λeiej, where λ is the principal eigenvalue and i eis the principal eigenvector, and consequently our distance matrix has a scalar representation, . It is not surprising that the eigenvector is closely related to our scalar, in fact r = 0.98. There are many hydrophobicity scales in the literature [r = 0.95 for Wertz & Scheraga scale [It is worth noting that a scalar reduction of the distance matrix can be got by a principal eigenvector analysis. In a principal eigenvector reduction of the contact propensity matrix we have terature  and somega scale . HoweverWe have generated full atom residue:residue contact propensity profiles for intra-molecular interactions from a non-redundant crystal structure database. Recasting the contact propensity matrix as a distance matrix we see that close residues are those with a low evolutionary substitution cost. The structure derived distance measures can serve as additional scores when aligning proteins where remote homologs share structural features. The distance matrix led us naturally to derive effective residue vectors. We found that residues sharing similar physical characteristics, such as hydrophobicity and volume, are grouped together. In contrast to the MJ matrix analysis, we find that a scalar representation for the residues is inadequate to capture the complexity of the propensity distance matrix. The most successful scalar representation for the amino acid residues has been the hydropathy scale. Representing a sequence as a smoothed hydropathy profile through wavelet analysis or simple averaging has resulted in many effective analytical tools, such as periodic structure prediction , remote In this study we have shown that inter-residue distance matrices and residue vectors allow us to make an explicit connection between amino acid interaction preferences observed in protein structures and amino acid evolutionary substitution costs. When problems are encountered with aligning structurally related proteins that are remote homologs then the structurally defined distance matrix may prove to be an effective supplement to existing substitution rate derived matrices. The distance matrix leads naturally to an amino acid vector representation. Projecting the vectors onto a two-dimensional plane illustrates ways in which the amino acids can be grouped and their effective number thereby reduced.i with amino acid type j, ij Nwere counted together with the number of these pairings corresponding to a pair with side chain atoms within a given distance of each other, ijC. The contact propensity matrix is given by . The residue vectors were defined such that  is minimal. The minimisation was carried out by a standard Newton-Raphson steepest descent iteration.The database used in the present study was compiled from the PDBselect25  list of Contact propensities and distance matrix derived from the structural database with contact cut-off set at 4.5Å.Click here for fileTwo dimensional residue vector components derived from the contact propensity distance matrix, the PAM250 distance matrix and the MJ distance matrix.Click here for file


Pneumocystis jiroveci pneumonia (PCP) is an important opportunistic infection among immunosuppressed patients, especially in those infected with human immunodeficiency virus (HIV). The clinical presentation of PCP in immunosuppressed patients have been well-reported in the literature. However, the clinical importance of PCP manifesting in the setting of an immunorestitution disease (IRD), defined as an acute symptomatic or paradoxical deterioration of a (presumably) preexisting infection, which is temporally related to the recovery of the immune system and is due to immunopathological damage associated with the reversal of immunosuppressive processes, has received relatively little attention until recently.10 copies/ml to 3.1 log10 copies/ml) was observed in HIV-positive patients who developed PCP. A similar upsurge in peripheral lymphocyte count was observed in our patients preceding the development of PCP, as well as in other non-HIV immunosuppressed patients reported in the literature.We aim to better define this unique clinical syndrome by reporting two cases of PCP manifesting acutely with respiratory failure during reversal of immunosuppression in non-HIV infected patients, and reviewed the relevant literature. We searched our databases for PCP cases manifesting in the context of IRD according to our predefined case definition, and reviewed the case notes retrospectively. A comprehensive search was performed using the Medline database of the National Library of Medicine for similar cases reported previously in the English literature in October 2003. A total of 28 non-HIV (excluding our present case) and 13 HIV-positive patients with PCP manifesting as immunorestitution disease (IRD) have been reported previously in the literature. During immunorestitution, a consistent rise in the median CD4 lymphocyte count (28/μL to 125/μL), with a concomitant fall in the median HIV viral load (5.5 logPCP manifesting as IRD may be more common than is generally appreciated. Serial monitoring of total lymphocyte or CD4 count could serve as a useful adjunct to facilitate the early diagnosis and pre-emptive treatment of this condition in a wide range of immunosuppressed hosts, especially in the presence of new pulmonary symptoms and/or radiographic abnormalities compatible with the diagnosis. Pneumocystis jiroveci (Pj) (previously known as Pneumocystis carinii f. sp. hominis) was first identified as a pathogen in premature infants suffering from interstitial plasma cell pneumonia in European countries during and after World War II, occasionally occurring in epidemics .-31.28-31PCP manifesting as a form of IRD is not a rare phenomenon. As shown in our previous study, it happens in 7 out of 10 (70%) of HIV-negative immunosuppressed hosts infected with Pj . HoweverRapid reduction of immunosuppressive therapy such as steroids has been implicated as a predisposing factor for the development of PCP in HIV-negative patients ,17,23,24Among HIV positive patients, PCP manifesting acutely during the initiation anti-retroviral therapy is a well-recognized phenomenon. The underlying immunopathological nature of this condition, which is reminiscent to IRD occurring in non-HIV infected patients, has been confirmed by histological examination of the lungs and transbronchial biopsy specimens, which demonstrated mixed inflammatory infiltrates including macrophages, neutrophils, lymphocytes, and plasma cells. Almost all infiltrating lymphocytes found in the tissues were of the T cell lineage, shown by immunophenotyping to be predominantly CD4 and CD8 cells . In anotIn our own experience, as well as from the review of published literature, it appears that a surge of absolute lymphocyte count, especially the CD4 lymphocyte count in HIV-positive patients, could potentially act as a surrogate marker for immunopathological damage during IRD in both HIV-negative and HIV-positive patients. In our recent publication , 7 out oFrom the result of this review, it appears that HIV-positive patients with PCP are at risk of clinical deterioration due to IRD if HAART therapy is started within 1 to 2 weeks after the initiation of treatment for PCP  declare that they have no competing interests.RAL and DSH were involved in the clinical evaluation and treatment of patients. BST and IFH helped with literature searching and review. AKW and VCC drafted and refined the manuscript. KYY conceived the study, participated in its design and coordination, and supervised the preparation of the manuscript. All authors have read and approved the final draft of the manuscript before submission.The pre-publication history for this paper can be accessed here:


PHACCS uses the contig spectrum from shotgun DNA sequence assemblies to mathematically model the structure of viral communities and make predictions about diversity.Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of uncultured viral communities . PHACCS PHACCS builds models of possible community structure using a modified Lander-Waterman algorithm to predict the underlying contig spectrum. PHACCS finds the most appropriate structure model by optimizing the model parameters until the predicted contig spectrum is as close as possible to the experimental one. This model is the basis for making estimates of uncultured viral community richness, evenness, diversity index and abundance of the most abundant genotype.PHACCS analysis of four different environmental phage communities suggests that the power law is an important rank-abundance form to describe uncultured viral community structure. The estimates support the fact that the four phage communities were extremely diverse and that phage community biodiversity and structure may be correlated with that of their hosts. By killing prokaryotes, phages can strongly impact microbial community biomass [Most environmental viruses are phages  that infect prokaryotic cells, both Bacteria and Archaea. On average there are about ten phage particles per host cell . Extrapo biomass . DespiteTraditionally, the study of environmental phage diversity, dynamics, and ecology requires growing prokaryotes on microbiology plates and infecting them with phages. However this standard technique is limited by the fact that only a small fraction of environmental microbes are readily cultured  and thatBiodiversity is composed of richness, or total number of different species , and eveq (number of sequences in the group) [A new approach to accessing natural microbial diversity is through the creation of shotgun sequence libraries from environmental metagenomes  -14, so te group) . The strIn this work, we present PHACCS (PHAge Communities from Contig Spectrum), an online computational tool to assess the diversity and structure of environmental viral communities from the contig spectrum of shotgun sequence data. The PHACCS program and its predictions are first described and then used to analyze four environmental viral communities.The standalone core mathematics for PHACCS consists of Matlab  scripts that are partly based on the previous works -12. A CGq-contigs (groups of q overlapping sequences) from the in silico assembly of random shotgun DNA fragments. Detailed information about the way to get viral metagenomes and their contig spectrum can be found in [The input for PHACCS is the contig spectrum, a vector containing the number of found in -12. Briefound in  and clonfound in -12). Thefound in . The strfound in . The numL of the genome, ii) the number N of DNA fragments studied, iii) the average size s of these fragments, and iv) the minimum overlap length o for the sequence assembly [PHACCS uses a modified version of the Lander-Waterman algorithm  to prediassembly . Given tp of an overlap: p = 1 - eNx/L -with x = s - o• Probability wq for a fragment to be part of a q-contig (overlap of q fragments):• Probability wq = qpq - 1 (1 - p)2cq that are part of a q-contig: cq = Nwq• Expected number of fragments • Contig spectrum: M different genotypes  [i.The modified Lander-Waterman algorithm is a generalization of the original algorithm to a group of mmunity) . The precq part of a q-contig:• Expected number of fragments wqi is the probability for a fragment to be part of a q-contig for the genotype i and ni is the expected number of fragments for the genotype i.In this modified algorithm, since there are several genotypes, an assumption about their underlying distribution within the community in terms of abundance has to be made.PHACCS offers six basic functional forms of relative rank-abundance for biological populations: the power law, logarithmic, exponential, broken stick, niche preemption, and lognormal distributions.The first three functional forms are empirical models that were designed to describe an asymptotic drop-off in the abundance :ni = aib -for 1 ≤ i ≤ M• Power: ni = a(log(i + 1))b -for 1 ≤ i ≤ M• Logarithmic: ni = aeib -for 1 ≤ i ≤ M• Exponential: a represents the abundance of the most abundant genotype, b is a parameter related to the evenness, and M is the number of different genotypes in the community.The parameter Two ecological models are based on a partitioning of resources between species ,19: for 1 ≤ i ≤ M• Broken stick: ni = Nk(1 - k)i - 1 and nM = N(1 - k)M - 1 for 1 ≤ i ≤ M - 1• Niche preemption: M, and assumes a random distribution of resources, whereas in the niche preemption function, each species takes only a fraction k of the remaining resources in the environment.The broken stick function has only one parameter, σ into M equal area slices and associating an abundance ni with the i-th slice by calculating an average value for the abundance within the slice. The result is:The sixth functional form is the lognormal distribution. It is the most commonly used species distribution, with numerous theoretical justifications in the literature ,21. The erf is the error function and erf1 its inverse.where b for the power law, logarithmic, exponential and lognormal distributions and k for niche preemption) are used to predict a contig spectrum using the modified Lander-Waterman algorithm. To determine the model fitness, the error between the actual and the predicted contig spectrum is calculated as the variance-weighted sum of squared deviations, L being the contig spectrum vector length and cq' the experimental number of fragments that belong to a q-contig:The PHACCS algorithm is represented in Figure The best descriptive model for a community structure is defined as the one with the smallest error. For each rank-abundance function tested, the global minimum for the error is found by optimizing the value of the function parameters.0.1 which is about 11:10 between the two models. This means that the model with the smallest error is about 10% more likely to give rise to the observed data.The values of the error can be roughly interpreted as logarithms of odds ratios of the observed contigs being seen from community distributions of the specified forms. Thus a value of 0.1 for the difference in errors between two models corresponds to an odds ratio of eS is estimated as equal to the number of different genotypes M found in the community structure model. The abundance of the most abundant genotype is also directly determined from the model as the highest rank-abundance value. The Shannon-Wiener index, which is a measure for diversity, is calculated using the relative rank-abundance values ri = ni/N of all individual genotypes i [For each rank-abundance form, the best model is used by PHACCS to assess diversity. The richness otypes i :H' (in nats): • Shannon-Wiener index H' [The evenness is derived from H' :E: E = H'/Hmax = H'/ln S• Evenness As a case study, four viral metagenomes obtained from previous studies and belonging to different ecosystems were tested. Two of these were phage community samples of near-shore surface seawater from Scripps Pier (SP) and Mission Bay (MB), San Diego, California, USA . The twoThe errors obtained from the contig spectrum analysis of the different samples are presented in Table The different diversity indicators and the rank-abundance curves obtained by using the best descriptive model for each sample are summarized in Figure  and the source code is freely available [see PHACCS is publicly accessible at able see . The bioPHACCS analyses are computer intensive. On a dual-Opteron™ server, the computation for the SP sample takes ~5 minutes. The broken stick and lognormal rank-abundance forms account for most of the computation time (data not shown). Increasing the range of genotypes to search dramatically increases the time needed to complete the analysis (data not shown).PHACCS estimations about the virus community are: i) structure – best descriptive rank-abundance form, model equation and error, and ii) diversity – richness, evenness, abundance of the most abundant genotype, and Shannon-Wiener index. Graphic representations of the community structure and of the error minimization can also be displayed. The error provides information about which model has the best fit relative to the others for a given contig spectrum. For each type of distribution, the user is informed if the best model  has not been found using the given computation parameters.Predictions by PHACCS are dependent on the quality of the contig spectrum input. The difference in error between two models can be small Table  and usinμm is collected. Larger viruses and RNA viruses are not represented in the shotgun library and in the resulting contig spectrum. The contig spectrum assembly parameters (98% identity on at least 20 bp for phages) are stringent enough to limit the number of false-contigs (contigs between DNA fragments from different genotypes), but may on the other side omit some true-contigs . Additionally, the present implementation of the Lander-Waterman algorithm assumes that all DNA fragments and all the genotypes have the same size. For these reasons, PHACCS estimates should be considered approximations.The way the contig spectrum is obtained leads to approximations of the viral diversity. In the samples analyzed here, only the DNA from viruses smaller than 0.22 The comparative analysis of the four phage communities showed that the power law seems overall to be a powerful rank-abundance distribution to model phage community structure Figure . A recenFirst, the extreme diversity of the sediment viral community may reflect the higher diversity of the microbial communities found in sediments using automated rRNA intergenic spacer analysis (ARISA)  in compaPHACCS is a web-based service that predicts community structure and diversity using the contig spectrum from metagenomic random shotgun sequence data. This methodology allows PHACCS to determine the mathematical model that most accurately reflects the underlying genotype abundance distribution  and use it to makes estimates about the diversity of the communities, . Using uncultured environmental viral samples, PHACCS has been used to confirm that phage biodiversity is higher than in any previously observed community and that the structure of viral communities may closely follow that of their hosts. PHACCS is designed for biologists to mathematically analyze their viral shotgun libraries and gain insights about viral ecology and population dynamics.Project name: PHACCS – PHAge Communities from Contig Spectrum• Project home page: • Operating system(s): Unix based system for PHACCS and its web interface. Platform independent for PHACCS core.• Programming language: Matlab (for the core scripts) and Perl• Other requirements: For the interface: CGI.pm Perl module, ppmtogif, webserver program (to use PHACCS as a web service)• License: GNU GPL• FA developed the PHACCS main program and its interface. BRB helped with the programming. BRB, DB, PMN, PS, BF, JN and JM developed the modified Lander-Waterman algorithm and implemented it with Matlab. FR and MB helped write the manuscript and provided the test datasets. All authors read and approved the final manuscript.This file contains the script files part of PHACCS. These files are either standard text or picture files.Click here for file


In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems.The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine.We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers.We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging.Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues.Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately. Solomonoff's ideas [Our aim is to place Medical Informatics in the context of other sciences and to bring coherence in its formal education . This wi's ideas , summari's ideas , contrib's ideas , a relathuman knowledge as a collection of individual theories elicited from scientific observations. The immense number of theories that comprise the collective human knowledge about every possible subject, as well as its extraordinary dynamics, have forced us to divide it into what we commonly refer to as knowledge domains, thereby reducing the contexts of our observations to smaller space-time continuums. The attempts to process with computers the knowledge in a domain have taught us that we need to recognize the reality of the "knowledge acquisition bottleneck" [knowledge spectrum.The four-dimensional space-time continuum we live in  forms the reality  of all scientific observations. The compression of the immense complexity and dynamicity of this reality in concise "theories of everything" was already demonstrated by Zuse ,6 and retleneck"  and to ntleneck"  and [10-tleneck" ). The pacomplex reality  to high-level abstractions . Therefore, it comprises increasingly lean modalities of knowledge and knowledge representations media and the relative boundaries and relationships between them. Two forces manifest on the knowledge spectrum: that of creating abstractions and that of instantiating abstractions for practical applications. The former is the theory elicitation and is synonymous to processes of context reduction and knowledge decomposition. The latter, theory application, equates to context increase and knowledge composition processes. The engines behind the two knowledge spectrum forces are the knowledge processors, natural or artificial entities able to create abstractions from data and to instantiate abstractions in order to fit reality.The knowledge spectrum Figure  spans frimplicit and explicit  is the rich, experiential, sensorial kind of knowledge that a knowledge processor acquires when immersed into an environment , or presented with detailed representations of that environment . It is very well applicable to specific instances of problems and relies on processing mechanisms such as feature selection, pattern recognition and associative memory.Explicit knowledge (E) is the abstract, symbolic type of knowledge present explicitly in documentations of knowledge such as textbooks or guidelines. It requires a representation language and the capability of a knowledge processor to construe the meaning of concepts of that language. It is applicable to both specific and generic problems and relies on explicit reasoning mechanisms.explicit knowledge in order to describe, with various degrees of precision, particular instances of problem solving and theory application. This represents the rationale for further dividing the knowledge spectrum into general and individual knowledge  is the explicit, abstract, propositional type of knowledge , well applicable to context-independent, generic problems. However, it is more difficult to use in specific contexts because of the gap between the general knowledge itself and a particular application context. This knowledge gap translates into uncertainty when a general knowledge fact is instantiated to a specific situation. For example, knowing generally that a certain drug may give allergic reactions but being uncertain whether a particular patient may or may not develop any, is an example of what we consider the uncertainty associated with general knowledge. The creation of general knowledge  is a relevance-driven process done by "stripping away irrelevancies" [vancies" . This cavancies" .Formal representations of explicit knowledge have been common in early artificial intelligence (AI) applications in the context of expert system development. They operated under the "closed world assumption" and were meant to make the representation of knowledge manageable, reproducible and clear. However this assumption also rendered the expert systems "brittle" or completely unusable when applied to real world problems . The com   1. first, that the brick is on something that is not a pyramid;   2. second, that there is nothing that the brick is on and that is on the brick as well; and   3. third, that there is nothing that is not a brick and the same thing as the brick."This definition could have the predicate calculus representation:This representation shows that an intelligent agent who has no implicit knowledge of the hypothetical physical world and no capacity of generalization or analogy making, must be explicitly provided with all knowledge necessary to reason about "bricks" in that limited reality. Such approaches are known to suffer from a fundamental shortcoming, the "frame problem."Daniel Dennett was the first philosopher of science who clearly articulated the "frame problem" and promoted it as one of the central problems of artificial intelligence   ignoring some thousands of implications it has determined to be irrelevant" and its batteries were therefore lost in the inevitable explosion.• The third robot failed because it was "The frame problem can therefore be recast as a problem of relevance [only because in the real world we do have time constraints(see preface), which is compounded by time constraints. It demonstrates that relevance judgment mechanisms based on general knowledge are time consuming and cause the failure to solve time-constrained decision problems. It is a problem .elevance  or instance specific knowledge, on the other hand, is a knowledge modality very well applicable to real problems, because it identifies uniquely and matches precisely an application context. The knowledge gap and uncertainty are reduced but still exist because of our changing reality (time dimension) which may render individual knowledge about a patient collected in the past , less applicable in the present or future. Because it preserves context , individual knowledge has a higher complexity than general knowledge and hence is more difficult to manage . For example, knowing the drugs and the precise description  of the allergic reactions that they caused in a certain person, as well as many other particular knowledge facts about individual, is what we call individual knowledge. The uncertainty and knowledge gap related to the application of such knowledge to future instances of decision making involving that individual are reduced: individual knowledge is supposed to fit very well the application context where it was originally captured.Individual knowledge captured from a very specific context  can be extrapolated to similar contexts. The higher the similarity between contexts, the smaller the knowledge gap and instantiation uncertainty and the higher the chances for a successful solution to a new problem. For this reason, individual knowledge processing has become increasingly important for artificial intelligence applications and is defined as the approach to solving new problems based on the solutions of similar past problems ,19-21. IThere are four steps (the four "RE") that a case-based reasoner must perform ,20,21:1. RETRIEVE: the retrieval from memory of the cases which are appropriate for the problem at hand; this task involves processes of analogy-making or case pattern matching;2. REUSE: the decomposition of the retrieved cases in order to make them applicable to the problem at hand;3. REVISE: the compositional adaptation and application of the knowledge encoded in the retrieved cases to the new problem; and4. RETAIN: the addition of the current problem together with its resolution to the case base, for future use.CBR entails that an expert system has a rich collection of past problem-solving cases stored together with their resolutions. CBR also hinges on a proper management of the case base and on appropriate mechanisms for the matching, retrieval and adaptation of the knowledge stored in the cases relevant to a new problem. Ideally, the individual knowledge in a case-base will progress asymptotically towards an exhaustive knowledge base, which represents the "holy grail" of knowledge engineers. From a learning systems point of view, similarly to artificial neural networks ,23 and iThe absolute positions and shapes of boundaries between the four knowledge modalities, although admittedly not as precise as drawn on the knowledge spectrum in Figure • Individual knowledge has a higher complexity than the explicit knowledge elicited from the same context. This is equivalent to stating that, for example, the picture of a person encodes more knowledge than the textual description of that person's appearance.• Implicit knowledge is a subset of the individual knowledge.• General knowledge is a subset of the explicit knowledge.• The set of individual knowledge represented explicitly formed by the intersection of individual knowledge with explicit knowledge is a nonempty set. This is equivalent to stating that it is possible, for example, for an explicit textual description to identify a context uniquely .The meta-level overview of sciences and the definitions and properties of the knowledge spectrum and knowledge modalities enable us to draw some fundamental differences between theoretical sciences and applied sciences such as Medicine  and Medi• Make use of observations which are highly abstract symbolisms and create far more limited contexts of application of their theories, when compared to the complexity of the human body or of any social or biological system,general knowledge comprising valid, powerful theories which explain precisely and completely the observations, and therefore,• Have as a primary purpose the creation of • Include a relatively limited number of precise theories which are evaluated primarily by their power of explaining experimental observations, elegance, generality, andindividual knowledge required by the practical implementation and by the application of results to real world problems.• Are less concerned with the acquisition of the Applied sciences such as Medicine and Medical Informatics, on the other hand:individual knowledge) from very complex systems [• Gather extensively data and observations ,• Have as a primary purpose not only distilling data and observations into • May lack the incentive to refine existing theories which are objectively wrong as long as practical success is achieved ,general knowledge) that can solve individual problems completely or explain and predict accurately [• Contain very few simple, "elegant" theories  ,25,• Abound in general theories  which are "lossy"  and which are evaluated statistically by their practical success relative to existing ones ,knowledge gap between general knowledge and the reality where knowledge is applied, by employing experienced clinicians who require extensive training and information technology , and, in addition,• Attempt to make up for the • Are compounded by time-constrained circumstances and largely unsolved ethical issues .individual knowledge. By doing so, Medical Informatics will provide a solution to the problems that arise during the use of general knowledge and, in the same time, will enable clinical research as well as advanced decision support and education of both healthcare providers and patientsMedical Informatics should complement the traditional quest for general biomedical knowledge with the advance of acquisition, storage, communication and use of .Given the special circumstances of our applied science in the context of other sciences and the increasing recognition of the importance of knowledge processing to Medical Informatics , we propIf EHR are to be used in a CBR context, the issues pertinent to the design of case-bases automatically become pertinent to the EHR design, and the CBR paradigm becomes important to Medical Informatics.Individual knowledge processing equates to a case-based reasoning (CBR) approach that employs collections of patient cases. Currently, such collections are the focus of research on Electronic Health Records (EHR). Envisioned as "womb to tomb" collections of patient-specific data, EHR contain a wealth of data that could be used to support case-based decisions. knowledge processing capacity of healthcare systems can be thought to be distributed between two sources: human resources  and information technology . An ideal CBR approach would increase this knowledge processing capacity by allowing for the automatic processing  of individual knowledge present in increasingly rich knowledge media such as natural language artifacts, images, videos and computer simulations of reality  research is a very important component of Medical Informatics, required to advance the organization and processing of individual knowledge in reusable case-bases. Further, the goal to advance processing of increasingly complex knowledge representations  and create intelligent machines that can hear, see, think, adapt and make decisions, brings Informatics even closer to what traditionally was the concern of Artificial Intelligence (AI).The overall y Figure . The stomoving towards the ideal of individual knowledge processing, no matter how slowly, may also have ethical implications because it proves that medical informaticians are trying to do everything they can in order to serve the interest of the individual.Finally, because the knowledge processing capacity of human resources tends to remain relatively constant, In order to support our thesis, the following discussion will focus initially on fundamental aspects of medical decision-making and biomedical knowledge creation from the standpoint of the knowledge spectrum. This will lead to a discussion of fundamental knowledge representation and processing principles and the proposal of a CBR perspective on EHR, including challenges and potential solutions.Medicine is a knowledge intensive domain where time-constrained decisions based on uncertain observations are commonplace. In order to successfully cope with such situations, health professionals go through a tedious learning process in which they gain the necessary domain knowledge to evolve from novices to experts. As experts, health professionals have attained, among other things, two important, highly interrelated abilities:determining efficiently what is relevant for solving a problem in a particular situation, and,• To be able to reduce knowledge complexity by reduce the knowledge gap between knowledge facts and reality which translates into being able to reduce the uncertainty of knowledge instantiation to a particular context.• To be able to For example, both the presence and the absence of a past appendectomy are relevant and contribute  to reducing the uncertainty of instantiation of the biomedical knowledge of an expert to a particular context of a patient with right lower abdominal pain. Fundamental to decision making, relevance judgments and uncertainty reduction seem both closely connected with the quality and quantity of knowledge available for solving a problem as well as with the nature of knowledge processing mechanisms. Studies of expert-novice differences in medicine  have shoThe discussion around the amount of problem solving experience of a decision maker becomes critical in time-constrained decision circumstances. The exhaustiveness of the knowledge base and the efficiency of retrieval mechanisms now become paramount to the decision speed. Empirical evidence that shows the existence of "systematic changes of cognitive processes" related to time stress, comes from the studies on the psychology of decision-making under time constraints . AlthougSince humans are able to make sound relevance judgments and reduce instantiation uncertainty of knowledge most of the times, the following questions arise: What is their strategy for increasing the exhaustiveness of their knowledge base while managing its exponential complexity? How do they represent and organize their knowledge and how do they manage time-constrained situations? At least some of these questions have been under intense scrutiny that has resulted in important empirical work on naturalistic decision-making ,33,36,37From the perspective of the knowledge spectrum, it seems reasonable to associate expert decision makers with individual knowledge and novices with the more abstract general knowledge about a subject, available in explicit knowledge artifacts . It is also conceivable that mental models of experts span a great length of the knowledge spectrum, causing them to efficiently perform implicit processing  and also just-in-time explicit reasoning Figure . The abiNovices, on the other hand, have limited mental models of reality situated towards the abstract region of the spectrum. This causes them to have difficulties with construing appropriate meanings of concepts due to the increased knowledge gaps between their mental models and reality. Novices are therefore unable to make sound relevance judgments and limited in their ability of interpreting data and creating abstractions. They are also usually overwhelmed by the explicit, general knowledge present in textbooks and guidelines and unable to fully construe the meanings of concepts present in such knowledge artifacts.In conclusion, in information and knowledge intensive domains such as medicine, explicit reasoning is important but individual knowledge acquisition  and processing  are crucial for decision-making. Because the nature of expertise seems largely connected with individual knowledge processing, it follows that the evolution of novices into experts is unattainable only by the provision of extensive general knowledge. In addition, not only the individual learning but also the collective sharing of experiences  between individuals and between generations, contribute to the way humans deal with decision problems.The major driving force of science is universally applicable knowledge . While creating and communicating new knowledge, scientists move across the knowledge spectrum from the data that captures the reality of their experiments and observations towards abstract representations that allow them to communicate their theories. In biomedical research, such an example is the randomized controlled trial (RCT), currently regarded as the gold standard for knowledge creation. The correct design of an RCT is crucial for the validity of the medical evidence obtained. A correct randomization process in RCTs will limit the bias and increase the chance for applicability of the evidence obtained, to a specifically selected group of patients . However, at the same time, the randomization process removes the circumstances of individual cases and creates a knowledge gap between the RCT evidence and future application instances. As with any statistical approach, the RCT-based evidence is best applicable at the population level rather than at the individual level.This depersonalization of medical knowledge and evidence was also noted by others ,40 and cOthers  have alsindividual knowledge and personalized health care, the general knowledge gained through populational studies  targets the ideal of universal applicability , providing the decision makers have access to it and are able to apply it to specific situations. However, general knowledge is unlikely to be used as such in many naturalistic decision-making processes, because it does not support the way expert decision makers think. The knowledge gap and inherent instantiation uncertainty manifested in the application of general knowledge does not fully enable the education of providers and patients which would require additional knowledge about individual contexts of successful or unsuccessful application instances. Informatics, on the other hand, by advancing individual knowledge processing, provides an alternative solution to the problems that arise from the use of general knowledge that targets universal applicability. An integral part of individual knowledge, genomic data is already recognized [as being of extreme importance for a solution to the problems of general knowledge.cognized ,40as beiTherefore, intelligent agent must be endowed with efficient mechanisms for determining the relevance of particular facts for a decision.The application of formal knowledge representations to real problems suffers from a fundamental shortcoming: the frame problem. As explained above (see "The frame problem"), the frame problem can be recast as a problem of relevance. Given the capability of relatively effortless human relevance judgments, the frame problem seems a rather "artificial" creation, difficult to grasp and which usually goes unnoticed. In order to circumvent its abstract nature, Dennett uses a story-telling approach. However, the frame problem also applies to and could be illustrated from the perspective of humans, who in their first years of life, learn and can easily and efficiently reason about the side effects and the implicit changes of the complex four-dimensional spatio-temporal physical world in which they live. As this learning gradually becomes common sense knowledge, it causes us to efficiently determine the relevant implicit changes while ignoring the non-relevant ones for a given situation. For example, such facts as that the clothes we are wearing are moving with us while walking or traveling are most of the times irrelevant given the context of a planned trip. However, if the trip involves some rapid movement through the air such as riding a motorbike, suddenly wearing a sombrero becomes a relevant fact. As experts at managing our physical world, we are able, through an effortless but powerful mental simulation, to determine the relevance of such a particular fact. The recall of our personal experiences of moving fast through the air and of the dragging force of the air becomes paramount. We suggest that what made the robots vulnerable was their creators' choice for knowledge representation and reasoning: the robots did not have quick access to implicit knowledge about the relevance of particular facts  but only to explicit facts in frames which had to be employed in time-consuming, immense number of explicit relevance judgments about the effects of particular actions. Although they were supposed to be experts at their task, the robots were behaving like novices. The frame problem is not a problem of the knowledge representation per se, but a problem of the choice for representation of knowledge needed to solve time-constrained decisions. In other words, formal representations and logic reasoning work, but not in time constrained, complex situations.intended meaning of concepts: a retrieved document is considered to be relevant to a query if the intended meanings of the authors of a document are relevant to the intended meaning of that query. We propose that "meaning," a property that characterizes all concepts present in explicit knowledge, is intimately connected  with the notion of context. According to this rather paradoxical view, meaning, a property which characterizes the abstract side of the knowledge spectrum, is strongly coupled with context which, by definition, is a feature of the reality side of the knowledge spectrum. Therefore, in order to construe meaning appropriately one needs to be able to efficiently move from abstractions towards richer representations of reality. This movement on the knowledge spectrum is necessary in order to fill the knowledge gap between abstract concepts and the richer mental representations required for construing their meaning.From the perspective of our knowledge spectrum, explicit, formal representations sit on the abstract side of the spectrum Figure . The retExplicit, formal representations attempt to capture general truth and generally applicable problem solving strategies, but become too abstract in nature. Through the abstraction process, which is essentially a reduction driven by the relevance judgments of knowledge creators, the context of a problem is lost. Losing context creates difficulties with construing meaning (which is context-dependent by definition) and widens the knowledge gap between the representation itself and the reality of a future problem-solving instance. The knowledge gap translates into the instantiation uncertainty that characterizes the application of general knowledge to specific problems. Making up for the knowledge gap through explicit relevance reasoning becomes time consuming and consequently takes its toll on the applicability of the representation. In sensitive applications such as medical decision-making and health research, general knowledge may potentially be harmful . In addition, abstractions and general methods and theories of problem solving and decision making  do not fully enable the education of individuals and the learning from successes and mistakes.Knowledge representation approaches must therefore preserve to the extent possible, the context of a problem-solving instance. By efficiently recalling similar past instances of problem solving and their contexts, intelligent agents are immediately provided with implicit knowledge about relevance, encoded in the retrieved contexts and, in the same time, with more possibilities to reduce the instantiation uncertainty of general knowledge when applied to specific problems. To enable this, informatics research must advance the processing of rich representations of the knowledge encoded in past problem solving cases: this is the definition of CBR research.Similar to formal specifications  natural language uses abstractions, i.e., concepts. Its richness and power of expression place it in the knowledge spectrum to the left side of formal specifications but to the right side of rich descriptions consisting of images, sounds, video-clips and simulations of reality Figure . NaturalIn order to illustrate our point about ambiguity we direct the reader to the previous, natural language definition of the concept of "a brick." Although the definition may look unequivocal, there are subtle ambiguities that make a difference in the predicate calculus representation. The first condition of an object to be "a brick"  is an ambiguous natural language construction and could have slightly different formal representations:In (2) this condition has been interpreted as: "the brick being on something IMPLIES that that something is not a pyramid" and was therefore represented as "for all Y, if X is on Y, this implies that Y is not a pyramid." In (3), which is identical to (1) but is repeated to the benefit of the reader, this condition was interpreted as "the brick MUST BE  on something that is not a pyramid" and that was represented as "there exists Y such as X is on Y and Y is not a pyramid."The first definition is therefore more "relaxed" as it allows the possibility that a brick sits on nothing. The second definition is more restrictive, because it requires the brick to be on something that is "not a pyramid" or otherwise X is not a brick anymore. Therefore, the first definition is more general and defines the concept of "a brick" in such a way that the definition would be true even in a world with no gravity . In addition, definition (3) does not reject the possibility that an object sits on both another brick and a pyramid, at the same time Figure .The point is that, most often, humans receive and transmit knowledge without the deep understanding and completeness required by an exact mathematical representation of the knowledge to be transmitted. This shallowness has also been recognized by others  who are If possessing the necessary knowledge, humans are able to effortlessly fill the knowledge gaps between natural language representations and their richer representations of reality , and to easily construe the appropriate meaning of potentially ambiguous concepts. Although current technology allows for its storage, knowledge present in richer media  is currently very difficult to process  using today's technology.Because natural languages are used by people universally and allow rich representations that no other language specification can attain, natural language processing (NLP) research is a first step that Informatics should take in order to advance the organization and processing of individual knowledge in case-bases that can be reused. The insights gained will advance knowledge processing towards richer knowledge representation media, will reduce the knowledge processing gap and consequently increase the knowledge processing capacity currently supported largely by human knowledge processors.One of the main features of information processing systems is their memory. It is accepted that storage and manipulation of information are necessary for complex cognitive activities in humans . Memory From a computational point of view, one could easily argue that without a random access memory structure there can be no effective processing. In the context of "the computational architecture of creativity," this argument is clearly outlined in . It is bRecent natural language processing (NLP) research stresses the importance of memorization of individual natural language examples . The impThe tradeoff between learning effort and communication efficiency seems to be biased naturally towards memorization rather than towards logical reasoning. The processing complexity of natural language might therefore not be an intrinsic quality of the algorithms, but rather a function of the memorization capabilities of the language processor, given the sparseness of natural language pattern space. By analogy, the advanced knowledge processing in humans might not be the result of very sophisticated reasoning strategies, but rather the utilization of a limited reasoning apparatus on a huge knowledge base, consisting of rich representations of one's experience. The limitations in reasoning are balanced by complex spatio-temporal pattern recognition capabilities operating on a case base built from years of experience. This case base includes common-sense knowledge.Furthermore, people and computers memorize information differently. Both have a short term, working memory and long-term memory for storing data and information. However, the memory access is carried out in different ways. Computers can reliably store large streams of data, which most of the times have a very well defined spatial and temporal structure . In contrast, people can only store information and knowledge rather than data and their storage is unreliable, temporally fragmented and spatially incomplete. Computers have very reliable memories capable of error checking at the bit level while the human memory supports only a high-level semantic consistency check. Finally, computers access their memory in a random seek fashion, being able to position their "reading heads" at any position in the data streams in order to extract a certain block of data. People can access their memory by content, by being provided with an incomplete description of a potentially complex, spatio-temporal pattern serving as a retrieval key. Therefore, one of the main differences between computers and humans is that computers have address-based random access memories, while humans possess content-addressable memories.In conclusion, from a case-based reasoning perspective, humans seem to be naturally endowed with the necessary structures for efficient case base acquisition, organization and retrieval while computers do not directly support this way of processing information and knowledge.Pattern recognition is an undisputed feature of human cognitive abilities and a research area in its own right. However, it does not seem to be as pervasive as it should, in the information processing systems in current use. Natural language, as a product of human cognition, offers compelling evidence that people are naturally inclined toward processing information using pattern recognition and similarity principles. This evidence is supported by the widespread use of language devices such as the simile and the metaphor. These are examples of comparison and analogy making that humans perform without effort, in contrast to the difficulty of implementing them in the artificial information processing systems . AnalogyThe patterns and analogies that humans are able to handle are often represented by complex spatio-temporal events with a potentially multi-sensorial impact. For example, while humans have no difficulty in understanding a metaphor like "the computer swallowed the disk," an artificial information processing system that has no visual input sensors and which lacks the capability of image understanding, would probably never be able to perceive this particular analogy with the same speed, because of the extensive reasoning and amount of explicit knowledge needed to bring the swallowing process, as it occurs in living things, close to the action of inserting a disk into a computer's disk drive.In addition to operating on high dimensional, spatio-temporal complex patterns, analogy making in humans may also possess a dynamic component that could yield different relevance judgment outcomes, depending of context. A very illustrative example is given by French and Labiouse in , using tPolyhierarchy and multiple inheritance are indisputable desiderata of terminology systems . HoweverThe dynamicity of concept meanings and relevance judgments may offer at least one of the reasons why fixed classification schemes, controlled terminology systems or open domain ontologies have not turned out satisfactory. It may also explain why existing lexical databases based on carefully handcrafted knowledge such as WordNet  often coFrom a case-based reasoning perspective, humans are naturally equipped with powerful pattern matching and classification capabilities which allow them to cope with complex, time-constrained relevance judgments, to easily construe meaning of concepts and to tolerate the ambiguity of natural language. Only relatively recently have computers come close to this functionality with the introduction of data mining and machine learning techniques such as self organizing maps and clustering algorithms based on similarity metrics [feature selection equates to a problems of relevance. metrics . In suchIatrogenic causes are said to be important causes of death in the US . The repOne very effective form of medical education is the retrospective analysis of case records where health professionals, both experienced or novices, learn from their own and from others' successes and failures . ProvidiMedical errors are also connected with the complex human cognitive task of planning . CBR appProviding that the privacy and confidentiality issues, which are even more stringent in this case, are dealt with appropriately, opening EHR to patients could benefit them . It is pMedicine has always and will always be a case oriented profession. Medical Informatics has recognized this early through the works of various researchers who pioneered the area of decision support systems . Relevan• The case base, the memory of past problem-solving instances• The case matching or pattern matching procedure which retrieves the relevant cases for a certain problemWhile humans seem to possess a natural support for these two components, there is still work to be done in order to make the computer support this kind of knowledge acquisition and processing. We envision four important challenges in advancing towards CBR enabled EHRs:1. Case record comprehensiveness2. Organization on similarity and associative principles (associative memory) and development of advance data visualization techniques3. Development of pattern recognition and similarity measures between heterogeneous records4. Solving ethical issues and provision of privacy and confidentiality measuresEHR comprehensiveness is required because the exhaustiveness of a case base is not only a function of the number of records but also of the richness of each case record. Current knowledge processing technology limits the acquisition and especially the processing of comprehensive EHR records which incorporate structured data, images, video-clips, bio-signals, genomic data, unstructured textual data covering clinical findings, detailed patient history, etc. However, as knowledge processing technology advances and knowledge acquisition bottlenecks are overcome, it might be possible to overcome the heterogeneity and sparseness of EHR and allow the creation of representative case-bases and the organization of knowledge on principles that facilitate similarity based retrieval.Temporal knowledge is also a good example of a heterogeneously represented type of knowledge in the form of potentially non-interoperable standards for dates and times and temporal knowledge of various degrees of precision, embedded in knowledge facts such as "soon after receiving the drug, the patient developed a rash." Currently, for many people, the problem may seems to boil down to devising yet another standard which encompasses all the different temporal representations of dates, times and temporal concepts into a unified, common representation. From a knowledge engineering standpoint, and again currently for many researchers, this may equate to the creation of a comprehensive ontology of temporal knowledge. However, the problem of representing time starts to look like a somewhat limited version of another burning problem of Medical Informatics: that of medical terminologies. The fact that all these issues remain largely unsolved, can only help the case for CBR and for adaptive, empirical methods and approaches to knowledge processing. We believe that such approaches have the potential to cope and overcome the problems with redundant, possibly ambiguous representations, which have arbitrary degrees of precision. Thereby we are specifying a goal towards which the development of EHR should proceed.Similarity based retrieval is difficult with current database technology. For example, queries to retrieve cases which are similar to a textual description of a given case are difficult to answer. The comprehensiveness of EHR must be complemented with the possibility of indexing its records on similarity principles. Conceptually, the functionality of EHR will be that of an associative memory of cases that will enable the CBR paradigm. The organization of a case-base must be complemented by the development of advanced data visualization techniques that comply with the principles of organization of information by similarity. One example of such data visualization techniques are self-organizing maps . These mCBR relies on the proper management of the case base and on appropriate mechanisms for matching and retrieval of these case records. All similarity retrieval mechanisms are based on some sort of distance calculation between the problem at hand and the records in the case base, followed by the retrieval of the most relevant ones. Clinical narratives and other EHR components containing unrestricted text represent a particularly difficult challenge for semantic similarity measures. The development of terminology systems based on less structured  and data-driven approaches will provide the semantic richness, dynamicity and reusability needed for such complex tasks.Ayercillin, an item unknown to the system and hence not appearing in Figure A concrete example for the potential feasibility of such approaches, is the automated knowledge induction based on contextual similarity modeling ranging from morphological to sentential context  Figure . An expeHowever, despite such proof-of-concept applications and other progress in data mining and knowledge extraction from heterogeneous databases, case matching remains largely an open research question.We discuss this challenge last, not because it is less important but, on the contrary, because of its potential to become the most important obstacle to individual knowledge processing. The very fact that individual knowledge has the potential to contribute to solving future problems instances, raises the important ethical issue whether such knowledge should be made available to decision makers and researchers. Because the definition of individual knowledge implies the possibility to match it in time and space with an application context, i.e., with a patient, sharing individual knowledge is counterbalanced by the need for privacy and confidentiality. In addition, to further complicate matters, it may turn out that some of the most useful records for future instances of decision making are instances of medical errors or other unexpected events that are unique in their course of events and therefore easily identifiable together with their contexts of development .The high complexity of individual knowledge renders explicit, manually controlled access to individual knowledge cases and their components unfeasible. The only solution to this problem seems to be of technological nature. Current privacy and confidentiality measures which include de-identification, de-nominalization and scrambling of the unique personal identifiers automated or semi-automated seem insufficient to counteract the potential to identify patients from unique, individual knowledge patterns.As a general approach, we propose that the accurate measuring of similarity of individual knowledge could form the basis of a confidentiality risk assessment. This could be intuitively understood by considering that:• very similar individual knowledge patterns which are in great numbers are a very low threat to the privacy and confidentiality infringement, and, at the other extreme,• stand-alone patterns which possess unique features or combinations of features, are at high risk of privacy and confidentiality breaches.knowledge about the proper use  of individual knowledge. This potentially complex, particular case of meta-knowledge processing could be implemented and managed using the principles of CBR paradigm itself, by building case-bases with examples of both proper and improper  individual knowledge accesses and that can be compared with future access instances.In addition, the provision of privacy and confidentiality could be regarded as a special case of knowledge processing, which involves Overcoming this very important challenge hinges on the possibility to effectively measure the similarity between heterogeneous records and on the advancement of knowledge processing on CBR principles.If successful, CBR research might therefore fulfill a longstanding need for intelligent information processing and advance informatics towards the ideal of individual knowledge processing. This calls for further investigation of information processing models that are, similarly to human experts, capable to efficiently move across the knowledge spectrum. One class of such models is represented by artificial neural networks ,23, whicCBR provides potential solutions to important problems that, among other, stymy the usefulness of EHR. The natural integration of learning with reasoning and the CBR resemblance to the cognitive models of human decision-making hold the promise to overcome the "brittleness" and "knowledge acquisition bottleneck" of classical expert systems. The CBR applications to the medical field have the potential to offering the training and decision support needed by health professionals and the means towards a true patient-centered healthcare.With a CBR theoretical foundation still in its infancy and with limited medical applications in existence, more research is needed for providing proofs of the feasibility of practical CBR-EHR applications. Challenges in the way to accomplish this include the increasing complexity, ethical issues as well as the paradigm shift that our current computing devices must undergo in order to support the CBR principles of knowledge processing.1. Science is twofold and is driven by two opposite forces: that of creating theories , and that of applying theories to practical applications (applied sciences). Medical Informatics is fundamentally an applied science that should be committed to advancing patient-centred medicine through individual knowledge processing.2. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing that could be integrated with the Electronic Health Records.3. Medicine is an information and knowledge intensive domain where time-constrained decision problems can only be solved effectively based on the recollection of similar problems and their solutions . The collective sharing of experiences is important for making future decisions as well as for learning how to make decisions.4. Unlike computers, human decision makers possess the components necessary to perform case-based reasoning naturally .5. Applying the CBR approach to EHR might be a way to overcome the important obstacles of EHR acceptance and use, providing that technical challenges and ethical issues arising are addressed appropriately.AI Artificial intelligenceAIT Algorithmic Information TheoryCBR Case Based ReasoningE Explicit knowledgeEHR Electronic Health RecordsG General knowledgeLSA Latent Semantic IndexingI Individual knowledgeNDM Naturalistic Decision MakingRCT Randomized Controlled TrialRPD Recognition-Primed DecisionU Implicit (Unobvious) knowledgeThe author(s) declare that they have no competing interests.SP researched the paper and provided a first draft. JA, JM critically revised the manuscript three times each and provided their own additions to the initial draft. JA provided more feedback on the cognitive aspects and decision-making as well as writing style and missing references. JM additions were with regard to the writing style, clarity, missing references and the overall organization of the paper.SP and JM worked on the responses to reviewers' comments. SP wrote a first revision of the paper. JM provided extensive feedback as well as new references and suggested a major revision that includes recent ideas. JA also commented and made suggestions on the knowledge spectrum model and on the meta-level view on Medical Informatics. SP overhauled the entire paper. JM revised the new version and provided feedback. SP operated the changes and proposed new modifications. JM revised the second draft. JA provided feedback on the second draft of the paper with regard to fundamental aspects of knowledge modalities.SP and JM incorporated the minor changes suggested by the last review.All authors read and approved the final version of the paper.The pre-publication history for this paper can be accessed here: