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.
2389020
Drug Design by Machine Learning
16795044
Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes
The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days.
15982740
Carcinogenesis Predictions Using ILP
Obtaining accurate structural alerts for the causes of chemical cancers is a problem of great scientific and humanitarian value. This paper follows up on earlier research that demonstrated the use of Inductive Logic Programming (ILP) for predictions for the related problem of mutagenic activity amongst nitroaromatic molecules. Here we are concerned with predicting carcinogenic activity in rodent bioassays using data from the U.S. National Toxicology Program conducted by the National Institute of Environmental Health Sciences. The 330 chemicals used here are significantly more diverse than the previous study, and form the basis for obtaining Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity in rodents. We describe the use of the ILP system Progol to obtain SARs from this data. The rules obtained from Progol are comparable in accuracy to those from expert chemists, and more accurate than most state-of-the-art toxicity prediction methods. The rules can also be interpreted to give clues about the biological and chemical mechanisms of carcinogenesis, and make use of those learnt by Progol for mutagenesis. Finally, we present details of, and predictions for, an ongoing international blind trial aimed specifically at comparing prediction methods. This trial provides ILP algorithms an opportunity to participate at the leading-edge of scientific discovery.
16914350
Biochemical Knowledge Discovery Using Inductive Logic Programming
Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structuralel ements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structuralc hemistry. This paper describes an experiment in which the ILP system Progolw as used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol's structurally-oriented hypotheses are preferable to those of other machine learning algorithms.
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.
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.
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.
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.