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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Sang Hyun Lee Haris Vikalo Sriram Vishwanath Department of Electrical and Computer Engineering University of Texas at Austin Austin TX USA

ISBN: (纸本)9781424442959;9781424442966

A new class of message-passing algorithms for motif finding is presented. Motif finding is the problem of identifying a collection of common subsequences within a given set of DNA sequences. It can be cast as an integer linear program (ILP). Message-passing techniques are a computationally efficient alternative to the often infeasible combinatorial solutions to the ILP. We introduce a new graphical representation of the ILP formulation of the problem, and use it to develop new message-passing algorithms for motif finding. Simulation results demonstrate that the new algorithms have better performance and convergence properties than the previously proposed solutions.

关键词： message passing motif finding message passing inductive logic programming algorithms FINDINGS New classes

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Research on logic Rules for Refinement Learning

Research on Logic Rules for Refinement Learning

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2010 International Colloquium on Computing,Communication, Control, and Management (CCCM2010)

作者： Zhenguo Shi~(1,2) School of Computer Science and Technology Nantong University Nantong,226019,China Zongtian Liu~2,DaSheng Feng~1 2 School of Computer Engineering and Science Shanghai University Shanghai,200072,China

Formal Concept Analysis(FCA),inductive logic programming(ILP) and Genetic programming(GP) have received increasing interest since them can be applied to many areas *** their formalisms are so different,these three approaches cannot be integrated easily though they share many common or similar goals and functionalities.A fusion will greatly enhance their problem solving *** this paper,a framework to combine FCA,ILP and GP is *** framework is based on a formalism of logic rules for refinement learning that can include concept and program both induction and evolution using FCA,ILP and *** experiment illustrates that our learner based on the framework is promising by compareing the performance with other learner.

关键词： logic Rules Refinement Learning Formal Concept Analysise inductive logic programming Genetic programming

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Methodology to derive context adaptable architectures for FPGAs

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IET COMPUTERS AND DIGITAL TECHNIQUES 2009年第1期3卷 124-141页

作者： Phillips, J. Sudarsanam, A. Samala, H. Kallam, R. Carver, J. Dasu, A. Utah State Univ Dept Elect & Comp Engn Logan UT 84321 USA

The configurable nature of field-programmable gate arrays (FPGAs) has allowed designers to take advantage of various data flow characteristics in application kernels to create custom architecture implementations, by optimising instruction level paralleism (ILP) and pipelining at the register transfer level. However, not all applications are composed of pure data flow kernels. Intermingling of control and data flows in applications offers more interesting challenges in creating custom architectures. The authors present one possible way to take advantage of correlations that may be present among data flow graphs (DFGs) embedded in control flow graphs. In certain cases, where there is sufficient correlation and ILP, the proposed context adaptable architecture (CAA) design methodology results in an interesting and useful custom architecture for such embedded DFGs. Certain other application characteristics may demand the use of alternative methodologies such as partial and dynamic reconfiguration (PDR) and a mixture of PDR and common sub-graph methods (PDR-CSG). The authors present a rigorous analysis, combined with some benchmarking efforts to showcase the differences, advantages and disadvantages of the CAA methodology with other methodologies. The authors also present an analysis of how the core underlying algorithm in our methodology compares with other published algorithms and the differences in resulting designs on an FPGA for a sample set of test cases.

关键词： ARCHITECTURE Building construction CASING INSPECTION Clean Air Act Pipeline processing AURAL ATRESIA, CONGENITAL Customs inductive logic programming control flow diagram methodology Field programmable gate arrays data flow EASE OF APPLICATION PDR gene Customization

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Brave induction: a logical framework for learning from incomplete information

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MACHINE LEARNING 2009年第1期76卷 3-35页

作者： Sakama, Chiaki Inoue, Katsumi Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

This paper introduces a novel logical framework for concept-learning called brave induction. Brave induction uses brave inference for induction and is useful for learning from incomplete information. Brave induction is weaker than explanatory induction which is normally used in inductive logic programming, and is stronger than learning from satisfiability, a general setting of concept-learning in clausal logic. We first investigate formal properties of brave induction, then develop an algorithm for computing hypotheses in full clausal theories. Next we extend the framework to induction in nonmonotonic logic programs. We analyze computational complexity of decision problems for induction on propositional theories. Further, we provide examples of problem solving by brave induction in systems biology, requirement engineering, and multiagent negotiation.

关键词： Brave induction inductive logic programming Nonmonotonic logic programming

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Using abduction and induction for operational requirements elaboration

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JOURNAL OF APPLIED logic 2009年第3期7卷 275-288页

作者： Alrajeh, D. Ray, O. Russo, A. Uchitel, S. Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Univ Bristol Dept Comp Sci Bristol BS8 1UB Avon England Univ Buenos Aires Dept Computac RA-2160 Buenos Aires DF Argentina

Requirements Engineering involves the elicitation of high-level stakeholder goals and their refinement into operational system requirements. A key difficulty is that stakeholders typically convey their goals indirectly through intuitive narrative-style scenarios of desirable and undesirable system behaviour, whereas goal refinement methods usually require goals to be expressed declaratively using, for instance, a temporal logic. In actual software engineering practice, the extraction of formal requirements from scenario-based descriptions is a tedious and error-prone process that would benefit from automated tool support. This paper presents an inductive logic programming method for inferring operational requirements from a set of example scenarios and an initial but incomplete requirements specification. The approach is based on translating the specification and the scenarios into an event-based logic programming formalism and using a non-monotonic reasoning system, called eXtended Hybrid Abductive inductive Learning, to automatically infer a set of event pre-conditions and trigger-conditions that cover all desirable scenarios and reject all undesirable ones. This learning task is a novel application of logic programming to requirements engineering that also demonstrates the utility of non-monotonic learning capturing pre-conditions and trigger-conditions. (C) 2008 Elsevier B.V. All rights reserved.

关键词： inductive logic programming Abductive reasoning Goal-oriented requirements engineering Scenario-based specification Linear temporal logic Event Calculus

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Robust Process Discovery with Artificial Negative Events

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JOURNAL OF MACHINE LEARNING RESEARCH 2009年第6期10卷 1305-1340页

作者： Goedertier, Stijn Martens, David Vanthienen, Jan Baesens, Bart Katholieke Univ Leuven Fac Business & Econ Louvain Belgium Assoc Ghent Univ Univ Coll Ghent Dept Business Adm & Publ Management Ghent Belgium Univ Southampton Sch Management Southampton SO9 5NH Hants England

Process discovery is the automated construction of structured process models from information system event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems such as expressiveness, noise, incomplete event logs, or the inclusion of prior knowledge. In this paper, we present a configurable technique that deals with these challenges by representing process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). This problem formulation allows using learning algorithms and evaluation techniques that are well-know in the machine learning community. Moreover, it allows users to have a declarative control over the inductive bias and language bias.

关键词： graph pattern discovery inductive logic programming Petri net process discovery positive data only

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Predicting functional upstream open reading frames in Saccharomyces cerevisiae

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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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Intralesional laser treatment of voluminous vascular lesions in the oral cavity

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ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY AND ENDODONTOLOGY 2009年第2期107卷 164-172页

作者： Miyazaki, Hidetaka Kato, Junji Watanabe, Hisashi Harada, Hiroyuki Kakizaki, Hirohiko Tetsumura, Akemi Sato, Akio Omura, Ken Tokyo Med & Dent Univ Grad Sch Dept Oral Restitut Div Oral Hlth SciBunkyo Ku Tokyo 1138549 Japan Tokyo Dent Coll Dept Operat Dent Tokyo Japan Tokyo Med & Dent Univ Grad Sch Dept Hard Tissue Engn Tokyo 1138549 Japan Aichi Med Univ Dept Ophthalmol Aichi Japan Tokyo Mem Clin Hirayama Tokyo Japan

Objective. In laser treatment of voluminous vascular lesions, there are many cases in which submucosally located angioma remnants cannot be reached by noncontact superficial laser application. To diminish these remnants we used intralesional photocoagulation (ILP) in treatment of oral vascular lesions, because this approach is effective in treatment of voluminous vascular lesions of the skin. Study design. Four cases of voluminous vascular malformation in the oral cavity were treated by ILP using a potassium-titanyl-phosphate (KTP) laser. In 1 case, treatment was carried out under ultrasound and manual control. Results. All lesions showed more than 70% regression after the first ILP session, and the treatment outcome was satisfactory. There were no serious complications, such as bleeding or invasive infection. Ultrasonography was useful for guiding laser treatment in the oral cavity. Conclusion. Intralesional photocoagulation treatment with a KTP laser is effective and safe for treatment of a vascular lesion in the oral cavity. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2009;107: 164-172)

关键词： therapeutic aspects Lesion NOS Blood vascular Lasers 激光 Light Coagulation inductive logic programming vascular lesion Treatment Outcome Potassium Titanyl Phosphate Lasers Lesions Mouth Laser Laser Therapy NOT OTHERWISE SPECIFIED

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Fuzzy inductive logic programming: Learning fuzzy rules with their implication

Fuzzy inductive logic programming: Learning fuzzy rules with...

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IEEE International Conference on Fuzzy Systems

作者： Serrurier, M Sudkamp, T Dubois, D Prade, H Univ Toulouse 3 IRIT F-31062 Toulouse France

ISBN: (纸本)0780391586

inductive logic programming (ILP) is a generic tool aiming at learning rules from relational databases. Introducing fuzzy sets and fuzzy implication connectives in this framework allows us to increase the expressive power of the induced rules while keeping the readability of the rules. Moreover, fuzzy sets facilitate the handling of numerical attributes by avoiding crisp and arbitrary transitions between classes. In this paper, the meaning of a fuzzy rule is encoded by its implication operator, which is to be determined in the learning process. An algorithm is proposed for inducing first order rules having fuzzy predicates, together with the most appropriate implication operator. The benefits of introducing fuzzy logic in ILP and the validation process of what has been learnt are discussed and illustrated on a benchmark.

关键词： Implication inductive logic programming Fuzzy rules Fuzzy Learning

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Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds

Drugs and Drug-Like Compounds: Discriminating Approved Pharm...

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4th International Conference Pattern Recognition in Bioinformatics

作者： Schierz, Amanda C. King, Ross D. Bournemouth Univ Software Syst Res Grp Poole HouseTalbot Campus Poole BH12 5BB Dorset England Aberystwyth Univ Computat Biol Res Grp Aberystwyth SY23 3DB Dyfed Wales

ISBN: (纸本)9783642040306

Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from "drug-like" compounds. This method uses both structural features and molecular properties for discrimination. The method has an estimated accuracy of 91% in discriminating between the Maybridge Hit-Finder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. These results show that Lipinski's Rule of 5 for oral absorption is not Sufficient to describe "drug-likeness" and be the main basis of screening-library design.

关键词： inductive logic programming drug-likeness machine learning Rule of 5 compound screening library

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