970544
Machine learning of functional class from phenotype data
MOTIVATION
Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We modified the algorithm C4.5 to deal with these problems.


RESULTS
Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 ORFs of unknown function at an estimated accuracy of > or = 80%.
9141928
Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines
SummaryOne of the largest available data sets for developing a quantitative structure-activity relationship (QSAR) — the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives — has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
6269772
PM - protein music
We present the program PM for the analysis of protein sequence information using audification. Audification is the technique of using the sense of hearing to analyse data (Jackson, 1993). The advantage of audification over visualisation in data analysis is that sound has the property that when different notes are played together they can still be individually heard: in vision colours blend to form new colours. This distinction can be very useful when studying multivariate data. The one dimensional structure of DNA maps naturally onto musical sequences (Hayashi and Munakata, 1984; Ohno and Ohno, 1986). The mapping algorithm in PM is designed to translate coding sequences into sound in a way that is more harmonious and more open to analysis than previous methods. The PM algorithm uses the DNA nucleotide sequence to form the musical top line, and the properties of the translated amino-acids to form the bass line. The notes are in the scale of C Major and there are three beats to the bar. Each codon corresponds to a bar of music and the notes are played on the beat in order of nucleotide sequence. The mapping of the top line to musical notes is as follows: A4 submediant E3 mediant C3 tonic G3 dominant The mapping of the bass line to notes is more complicated and is based on the designation of aminoacid physico-chemical properties of Taylor (1983). Each of the properties has a note assigned to it. The mapping is as follows: polar hydrophobic charged positive aliphatic aromatic tiny
594009
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
The Predictive Toxicology Evaluation (or PTE) Challenge provided Machine Learning techniques with the opportunity to compete against specialised techniques for toxicology prediction. Toxicity models that used findings from ILP programs have performed creditably in the PTE-2 experiment proposed under this challenge. We report here on an assessment of such models along scales of: (1) quantitative performance, in comparison to models developed with expert collaboration; and (2) potential explanatory value for toxicology. Results appear to suggest the following: (a) across of range of class distributions and error costs, some explicit models constructed with ILP-assistance appear closer to optimal than most expert-assisted ones. Given the paucity of test-data, this is to be interpreted cautiously; (b) a combined use of propositional and ILP techniques appears to yield models that contain unusual combinations of structural and biological features; and (c) significant effort was required to interpret the output, strongly indicating the need to invest greater effort in transforming the output into a "toxicologist-friendly" form. Based on the lessons learnt from these results, we propose a new predictive toxicology evaluation experiment - PTE-3 - which will address some important shortcomings of the previous study.
18628984
Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines
SummaryNeural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.
13898721
An Automated ILP Server in the Field of Bioinformatics
The identification of evolutionary related (homologous) proteins is a key problem in molecular biology. Here we present a inductive logic programming based method, Homology Induction (HI), which acts as a filter for existing sequence similarity searches to improve their performance in the detection of remote protein homologies. HI performs a PSI-BLAST search to generate positive, negative, and uncertain examples, and collects descriptions of these examples. It then learns rules to discriminate the positive and negative examples. The rules are used to filter the uncertain examples in the "twilight zone". HI uses a multitable database of 51,430,710 pre-fabricated facts from a variety of biological sources, and the inductive logic programming system Aleph to induce rules. Hi was tested on an independent set of protein sequences with equal or less than 40 per cent sequence similarity (PDB40D). ROC analysis is performed showing that HI can significantly improve existing similarity searches. The method is automated and can be used via a web/mail interface.
7168849
Constraint-based hypertext for argumentation
In this paper we describe a hypertext system we are developing for the support of reasoned argumentation: the EUCLID project. We use the project to address two general problems arising with hypertext: the problems of controlling user/document interaction, and the problem of controlling the screen. We suggest that guiding users' interaction with hypertext is difficult because of the unique form of discourse that hypertext represents, and that structuring user/document interaction can be achieved through specializing to a particular type of material and designing the hypertext system to respect the particular discourse structure characteristic of that material. EUCLID's design is tuned to the structure of reasoned discourse. The problem of screen management in EUCLID is a serious one, because our presentation of complex arguments requires mapping the complex logical relations between parts of realistic arguments onto complex spatial relations between items in the display. We describe a general system we are developing which provides this high degree of control for hypertext screen management. This system represents a constraint-based approach to hypertext, in which the items from the underlying database that are to be displayed may each contribute a number of constraints on the layout; a general constraint-satisfier then computes a screen layout that simultaneously satisfies these constraints. Each time an item is to be added to or deleted from the screen, the constraint set is adjusted and the screen layout is recomputed; thus the spatial relationships on the screen provide at all times a veridical representation of the underlying relations between displayed database items. This kind of strong screen control is demanded by hypertext applications which, like ours, are fine grained: the number of nodes and links being displayed number in the hundreds.
15503339
Generating data integration mediators that use materialization
This paper presents a framework for data integration that is based on using “Squirrel integration mediators” that use materialization to support integrated views over multiple databases. These mediators generalize techniques from active databases to provide incremental propagation of updates to the materialized views. A framework based on “View Decomposition Plans” for optimizing the support of materialized integrated views is introduced. The paper describes the Squirrel mediator generator currently under development, which can generate the mediators based on high-level specifications.The integration of information by Squirrel mediators is expressed primarily through an extended version of a standard query language, that can refer to data from multiple information sources. In addition to materializing an integrated view of data, these mediators can monitor conditions that span multiple sources. The Squirrel framework also provides efficient support for the problem of “object matching”, that is, determining when object representations (e.g., OIDs) in different databases correspond to the same object-in-the-world, even if a universal key is not available.To establish a context for the research, the paper presents a taxonomy that surveys a broad variety of approaches to supporting and maintaining integrated views.
27924441
Fluorescent color factor calculation using dBASE-II
A software system utilizing dBASE-II operating on a dual-drive Apple II+ computer is described. Color factors and retention times for 15 amino acids and epsilon-(gamma-glutamyl)lysine dipeptide are calculated following high performance liquid chromatography. The software package produces a listing of acceptable limits for these parameters calculated as plus and minus 2 standard deviations of the mean. The code is distributed in source form.
17042004
Case study on statistically estimating minimum makespan for flow line scheduling problems
Abstract Lower bounds are typically used to evaluate the performance of heuristics for solving combinatorial minimization problems. In the absence of tight analytical lower bounds, optimal objective-function values may be estimated statistically. In this paper, extreme value theory is used to construct confidence-interval estimates of the minimum makespan achievable when scheduling nonsimilar groups of jobs on a two-stage flow line. Experimental results based on randomly sampled solutions to each of 180 randomly generated test problems revealed that (i) least-squares parameter estimators outperformed standard analytical estimators for the Weibull approximation to the distribution of the sample minimum makespan; (ii) to evaluate each Weibull fit reliably, both the Anderson–Darling and Kolmogorov–Smirnov goodness-of-fit tests should be used; and (iii) applying a local improvement procedure to a large sample of randomly generated initial solutions improved the probability that the resulting Weibull fit yielded a confidence interval covering the minimum makespan.