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.
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.
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.
2075186
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge
Constructing "good" models for chemical carcinogenesis was identified in IJCAI-97 as providing a substantial challenge to "knowledge discovery" programs. Attention was drawn to a comparative exercise which called for predictions on the outcome of 30 rodent carcinogenicity bioassays. This-the Predictive Toxicology Evaluation (or PTE) Challenge - was seen to provide AI programs with an opportunity to participate in an enterprise of scientific merit, and a yardstick for comparison against strong competition. Here we provide an assessment of the machine learning (ML) submissions made. Models submitted are assessed on: (1) their accuracy, in comparison to models developed with expert collaboration; and (2) their explanatory value for toxicology. The principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, results from MLderived models were at least as good as those with expert-derived techniques. This was surprising; (b) the combined use of structural and biological features by ML-derived models was unusual, and suggested new avenues for toxicology modelling. This was also unexpected; and (c) significant effort was required to interpret the output of even the most "symbolic" of ML-derived models. Much of this could have been alleviated with measures for converting the results into a more "toxicology-friendly" form. As it stands, their absence is sufficient to prevent a whole-hearted acceptance of these promising methods by toxicologists. This suggests that ML techniques have been able to respond -not fully, but nevertheless substantially -to the PTE Challenge.
14415508
A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network
The aim of the Stat Log project is to compare the performance of statistical, machine learning, and neural network algorithms, on large real world problems. This paper describes the completed work on classification in the StatLog project. Classification is here defined to be the problem, given a set of multivariate data with assigned classes, of estimating the probability from a set of attributes describing a new example sampled from the same source that it has a pre-defined class. We gathered together a representative collection of algorithms from statistics (Naive Bayes, K-nearest Neighbour, Kernel density, Linear discriminant, Quadratic discriminant, Logistic regression, Projection pursuit, Bayesian networks), machine learning (CART, C4.5, NewID, AC2, CAL5, CN2, ITrule — only propositional symbolic algorithms were considered), and neural networks (Backpropagation, Radial basis functions, Kohonen). We then applied these algorithms to eight large real world classification problems: four
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.
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.
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.