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Predicting functional upstream open reading frames in Saccharomyces cerevisiae

BMC BIOINFORMATICS 2009年第1期10卷 1-10页

作者： Selpi Bryant, Christopher H. Kemp, Graham J. L. Sarv, Janeli Kristiansson, Erik Sunnerhagen, Per Chalmers Dept Appl Mech SE-41296 Gothenburg Sweden Univ Salford Sch Comp Sci & Engn Salford M5 4WT Lancs England Chalmers Dept Comp Sci & Engn SE-41296 Gothenburg Sweden Chalmers Dept Math Sci SE-41296 Gothenburg Sweden Univ Gothenburg SE-41296 Gothenburg Sweden Univ Gothenburg Dept Zool SE-40530 Gothenburg Sweden Univ Gothenburg Dept Cell & Mol Biol Lundberg Lab SE-40530 Gothenburg Sweden

Background: Some upstream open reading frames (uORFs) regulate gene expression (i.e., they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not yet fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of experimentally verified functional uORFs are needed. Unfortunately, wet-experiments to verify that uORFs are functional are expensive. Results: In this paper, a new computational approach to predicting functional uORFs in the yeast Saccharomyces cerevisiae is presented. Our approach is based on inductive logic programming and makes use of a novel combination of knowledge about biological conservation, Gene Ontology annotations and genes' responses to different conditions. Our method results in a set of simple and informative hypotheses with an estimated sensitivity of 76%. The hypotheses predict 301 further genes to have 398 novel functional uORFs. Three (RPC11, TPK1, and FOL1) of these 301 genes have been hypothesised, following wet-experiments, by a related study to have functional uORFs. A comparison with another related study suggests that eleven of the predicted functional uORFs from genes LDB17, HEM3, CIN8, BCK2, PMC1, FAS1, APP1, ACC1, CKA2, SUR1, and ATH1 are strong candidates for wet-lab experimental studies. Conclusions: Learning based prediction of functional uORFs can be done with a high sensitivity. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help to elucidate the regulatory roles of uORFs.

关键词： inductive logic programming Gene Ontology Annotation Certainty Factor Saccharomyces Genome Database Upstream Open Reading Frame

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Numerical reasoning with an ILP system capable of lazy evaluation and customised search

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JOURNAL OF logic programming 1999年第2-3期40卷 185-213页

作者： Srinivasan, A Camacho, R Univ Oxford Comp Lab Oxford OX1 3QD England CIUP LIACC P-4150 Porto Portugal

Using problem-specific background knowledge, computer programs developed within the framework of inductive logic programming (ILP) have been used to construct restricted first-order logic solutions to scientific problems. However, their approach to the analysis of data with substantial numerical content has been largely limited to constructing clauses that: (a) provide qualitative descriptions ("high", "low" etc.) of the values of response variables;and (b) contain simple inequalities restricting the ranges of predictor variables. This has precluded the application of such techniques to scientific and engineering problems requiring a more sophisticated approach. A number of specialised methods have been suggested to remedy this. In contrast, we have chosen to take advantage of the fact that the existing theoretical framework for ILP places very few restrictions of the nature of the background knowledge. We describe two issues of implementation that make it possible to use background predicates that implement well-established statistical and numerical analysis procedures. Any improvements in analytical sophistication that result are evaluated empirically using artificial and real-life data. Experiments utilising artificial data are concerned with extracting constraints for response variables in the text-book problem of balancing a pole on a cart. They illustrate the use of clausal definitions of arithmetic and trigonometric functions, inequalities, multiple linear regression, and numerical derivatives. A non-trivial problem concerning the prediction of mutagenic activity of nitroaromatic molecules is also examined. In this case, expert chemists have been unable to devise a model for explaining the data. The result demonstrates the combined use by an ILP program of logical and numerical capabilities to achieve an analysis that includes linear modelling, clustering and classification. In all experiments, the predictions obtained compare favourably against benchmarks se

关键词： inductive logic programming numerical reasoning lazy evaluation customised search

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PYTHIA-II: A knowledge/database system for managing performance data and recommending scientific software

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2000年第2期26卷 227-253页

作者： Houstis, EN Catlin, AC Rice, JR Verykios, VS Ramakrishnan, N Houstis, CE Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Univ Crete Dept Comp Sci Iraklion Greece

Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHLA-II-a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the user's problem, (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.

关键词： algorithms experimentation data mining inductive logic programming knowledge-based systems knowledge discovery in databases performance evaluation recommender systems scientific software

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Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning

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NEW GENERATION COMPUTING 2019年第2期37卷 203-217页

作者： Muggleton, Stephen H. Hocquette, Celine Imperial Coll London Dept Comp 180 Queens Gate London England

Recently, world-class human players have been outperformed in a number of complex two-person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, the data efficiency of the learning systems is unclear given that they appear to require far more training games to achieve such performance than any human player might experience in a lifetime. In addition, the resulting learned strategies are not in a form which can be communicated to human players. This contrasts to earlier research in Behavioural Cloning in which single-agent skills were machine learned in a symbolic language, facilitating their being taught to human beings. In this paper, we consider Machine Discovery of human-comprehensible strategies for simple two-person games (Noughts-and-Crosses and Hexapawn). One advantage of considering simple games is that there is a tractable approach to calculating minimax regret. We use these games to compare Cumulative Minimax Regret for variants of both standard and deep reinforcement learning against two variants of a new Meta-interpretive Learning system called MIGO. In our experiments, tested variants of both normal and deep reinforcement learning have consistently worse performance (higher cumulative minimax regret) than both variants of MIGO on Noughts-and-Crosses and Hexapawn. In addition, MIGO's learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning in both directions between Noughts-and-Crosses and Hexapawn.

关键词： inductive logic programming Reinforcement learning Games strategies

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Strategies in combined learning via logic programs

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MACHINE LEARNING 2000年第1-2期38卷 63-87页

作者： Lamma, E Riguzzi, F Pereira, LM Univ Bologna DEIS I-40136 Bologna Italy Univ Nova Lisboa Fac Ciencias & Tecnol Dept Informat Ctr Inteligencia Artificial P-2825 Monte De Caparica Portugal

We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt Extended logic Programs (ELP) under a Well-Founded Semantics with explicit negation (WFSX) as the representation formalism for learning, and show how ELPs can be used to specify combinations of strategies in a declarative way also coping with contradiction and exceptions. Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept. Standard inductive logic programming techniques are employed to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels for positive and negative concepts. In the paper, we also handle the issue of strategic combination of possibly contradictory learnt definitions of a predicate and its explicit negation. All in all, we show that extended logic programs under well-founded semantics with explicit negation add expressivity to learning tasks, and allow the tackling of a number of representation and strategic issues in a principled way. Our techniques have been implemented and exam

关键词： inductive logic programming non-monotonic learning multi-strategy learning explicit negation contradiction handling

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Understanding the crucial role of attribute interaction in data mining

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ARTIFICIAL INTELLIGENCE REVIEW 2001年第3期16卷 177-199页

作者： Freitas, AA Pontificia Univ Catolica Parana Postgrad Program Comp Sci BR-80215901 Curitiba Parana Brazil

This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of problem in data mining, such as attribute construction, coping with small disjuncts, induction of first-order logic rules, detection of Simpson's paradox, and finding several types of interesting rules. Hence, a better understanding of attribute interaction can lead to a better understanding of the relationship between these kinds of problems, which are usually studied separately from each other. Secondly, we draw attention to the fact that most rule induction algorithms are based on a greedy search which does not cope well with the problem of attribute interaction, and point out some alternative kinds of rule discovery methods which tend to cope better with this problem. Thirdly, we discussed several algorithms and methods for discovering interesting knowledge that, implicitly or explicitly, are based on the concept of attribute interaction.

关键词： attribute interaction classification constructive induction data mining evolutionary algorithms inductive logic programming rule induction rule interestingness Simpson's paradox small disjuncts

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Experiments in predicting biodegradability

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APPLIED ARTIFICIAL INTELLIGENCE 2004年第2期18卷 157-181页

作者： Blockeel, H Dzeroski, S Kompare, B Kramer, S Pfahringer, B Van Laer, W Katholieke Univ Leuven Dept Comp Sci B-3001 Louvain Belgium Jozef Stefan Inst Dept Intelligent Syst Ljubljana Slovenia Univ Ljubljana Fac Civil Engn & Geodesy Ljubljana Slovenia Tech Univ Munich Dept Comp Sci D-8000 Munich Germany Univ Waikato Dept Comp Sci Hamilton New Zealand

This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. The definition of substructures is used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalizations of the problem. We also experiment with a prediction technique that consists of merging upper and lower bound predictions into one prediction. Some conclusions are drawn concerning the applicability of machine learning systems and the merging technique in this domain and the evaluation of hypotheses.

关键词： inductive logic programming chemical compounds Substructures Half Life merger object variable Forecasting techniques Artificial Intelligence

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Efficient algorithm for glitch power reduction

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ELECTRONICS LETTERS 1999年第13期35卷 1040-1041页

作者： Kim, S Kim, J Hwang, SY Sogang Univ Dept Comp Sci Seoul 100611 South Korea Sogang Univ Dept Elect Engn Seoul 100611 South Korea

An efficient algorithm is proposed for reducing glitch power dissipation in CMOS logic circuits. The proposed algorithm takes a path balancing approach that is achieved using gate sizing and buffer insertion methods. The gate sizing technique reduces not only glitches but also the effective circuit capacitance. After gate sizing, buffers are inserted into the remaining unbalanced paths which have not been subjected to gate sizing. ILP has been employed to determine the location of inserted buffers. The proposed algorithm has been tested on LGSynth91 benchmark circuits. Experimental results show that 61.5% of glitches are reduced on average.

关键词： circuit CAD capacitance inductive logic programming integrated circuit design Optimisation gate sizing Digital circuit design, modelling and testing glitch power dissipation circuit capacitance reduction CMOS logic circuits CMOS integrated circuits buffer insertion logic circuits glitch power reduction logic and switching circuits Computer-aided circuit analysis and design Computer-aided logic design logic programming Electronic engineering computing Optimisation techniques path balancing ILP circuit optimisation low-power electronics logic CAD

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Relational inductive learning with a hybrid evolutionary algorithm

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AI COMMUNICATIONS 2005年第1期18卷 67-69页

作者： Divina, F Free Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands

inductive learning in First-Order logic (FOL) is a hard task due to both the prohibitive size of the search space and the computational cost of evaluating hypotheses. This paper describes an evolutionary algorithm for concept learning in (a fragment of) FOL. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. Experimental results show that ECL works well in practice.

关键词： supervised learning Evolutionary Algorithms inductive logic programming

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Constructing simply recursive programs from a finite set of good examples

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INFORMATION PROCESSING LETTERS 1997年第5期64卷 225-230页

作者： Hirata, K Kyushu Inst Technol Dept Arificial Intelligence Iizuka Fukuoka 820 Japan

关键词： learning from good examples inductive logic programming finite identification

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