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
1973513
A quantum mechanics based representation of molecules for machine inference
8863872
The Predictive Toxicology Evaluation Challenge
Can an AI program contribute to scientific discovery? An area where this gauntlet has been thrown is that of understanding the mechanisms of chemical carcinogenesis. One approach is to obtain Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity. Vital to this are the rodent carcinogenicity tests conducted within the US National Toxicology Program by the National Institute of Environmental Health Sciences (NIEHS). This has resulted in a large database of compounds classified as carcinogens or otherwise. The Predictive-Toxicology Evaluation project of the NIEHS provides the opportunity to compare carcinogenicity predictions on previously untested chemicals. This presents a formidable challenge for programs concerned with knowledge discovery. Desirable features of this problem are: (1) involvement in genuine scientific discovery; (2) availability of a large database with expert-certified classifications; (3) strong competition from methods used by chemists; and (4) participation in true blind trials, with results available by next IJCAI. We describe the materials and methods constituting this challenge, and provide some initial benchmarks. These show the Inductive Logic Programming tool Progol to be competitive with current state-of-the-art. The challenge described here is aimed at encouraging AI programs to avail themselves the opportunity of contributing to an enterprise with immediate scientific value.
5798265
Genome scale prediction of protein functional class from sequence using data mining
The ability to predict protein function from amino acid sequence is a central research goal of molecular biology. Such a capability would greatly aid the biological interpretation of the genomic data and accelerate its medical exploitation. For the existing sequenced genomes function can be assigned to typically only between 40-60% of the genes [4,8,12,7]. The new science of functional genomics is dedicated to discovering the function of these genes, and to further detailing gene function [10,27,17,6]. Here we present a novel data-mining [24,18] approach to predicting protein functional class from sequence. We demonstrate the effectiveness of this approach on the Mycobacterium tuberculosis [8] genome. Biologically interpretable rules are identified that can predict protein function even in the absence of identifiable sequence homology. These rules predict 65% of the genes with no previous assigned function in Mycobacterium tuberculosis (the bacteria which causes TB) with an estimated accuracy of 60-80% (depending on the level of functional assignment). The rules give insight into the evolutionary history of the organism. Categories and Subject Database Applications, Learning, Life and Medical Sciences General Terms Data mining, Concept learning, Biology and genetics.
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.
15572728
Applications of inductive logic programming
Some applications of Inductive Logic Programming (ILP) are presented. Those applications are chosen that specifically benefit from relational descriptions generated by ILP programs, and from ILP's ability to accommodate background knowledge. Applications included are: drug design, predicting the secondary structure of proteins, and design of finite-element meshes. Some other applications are briefly described. The practical advantages and disadvantages of ILP learning are discussed.
15579646
BLID: An Application of Logical Information Systems to Bioinformatics
BLID (Bio-Logical Intelligent Database) is a bioinformatic system designed to help biologists extract new knowledge from raw genome data by providing high-level facilities for both data browsing and analysis. We describe BLID’s novel data browsing system which is based on the idea of Logical Information Systems. This enables combined querying and navigation of data in BLID (extracted from public bioinformatic repositories). The browsing language is a logic especially designed for bioinformatics. It currently includes sequence motifs, taxonomies, and macromolecule structures, and it is designed to be easily extensible, as it is composed of reusable components. Navigation is tightly combined with this logic, and assists users in browsing a genome through a form of human-computer dialog.
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.
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.
16104731
A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics
Many application domains make use of specific data structures such as sequences and graphs to represent knowledge. These data structures are ill-fitted to the standard representations used in machine learning and data-mining algorithms: propositional representations are not expressive enough, and first order ones are not efficient enough. In order to efficiently represent and reason on these data structures, and the complex patterns that are related to them, we use domain-specific logics. We show these logics can be built by the composition of logical components that model elementary data structures. The standard strategies of top-down and bottom-up search are ill-suited to some of these logics, and lack flexibility. We therefore introduce a dichotomic search strategy, that is analogous to a dichotomic search in an ordered array. We prove this provides more flexibility in the search, while retaining completeness and non-redundancy. We present a novel algorithm for learning using domain specific logics and dichotomic search, and analyse its complexity. We also describe two applications which illustrates the search for motifs in sequences; where these motifs have arbitrary length and length-constrained gaps. In the first application sequences represent the trains of the East-West challenge; in the second application they represent the secondary structure of Yeast proteins for the discrimination of their biological functions.
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.