Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work,...
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Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://***/ kd4v/.
The gRS-ILP model (generic Rough Set inductive logic programming model) provides a framework for inductive logic programming when the setting is imprecise and any induced logic program will not be able to distinguish ...
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ISBN:
(纸本)3540666451
The gRS-ILP model (generic Rough Set inductive logic programming model) provides a framework for inductive logic programming when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. However, in this rough setting, where it is inherently not possible to describe the entire data with 100% accuracy, it is possible to definitively describe part of the data with 100% accuracy. The gRS-ILP model is extended in this paper to motifs in strings. An illustrative experiment is presented using the ILP system Progol on transmembrane domains in amino acid sequences.
inductive logic programming (ILP) is a form of machine learning that induces rules from data using the language and syntax of logicprogramming. A rule construction algorithm forms rules that summarize data sets. Thes...
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ISBN:
(纸本)0780362624
inductive logic programming (ILP) is a form of machine learning that induces rules from data using the language and syntax of logicprogramming. A rule construction algorithm forms rules that summarize data sets. These rules can be used in a large spectrum of data mining activities. In ILP, the rules are constructed with a target predicate as the consequent, or head, of the rule, and with high-ranking literals forming the antecedent, or body, of the rule. The predicate rankings are obtained by applying predicate ranking algorithms to a domain (background) knowledge base. In this work, we present three new predicate ranking algorithms for the inductive logic programming system, INDED (pronounced "indeed"). The algorithms use a grouping technique employing basic set theoretic operations to generate the rankings. We also present results of applying the ranking algorithms to several problem domains, some of which are universal like the classical genealogy problem, and others, not so common. In particular, diagnosis is the main thread of many of our experiments. Here, although our experimentation relates to medical diagnosis in diabetes and Lyme disease, many of the same techniques and methodologies can be applied to other forms of diagnosis including system failure, sensor detection, and trouble-shooting.
Model transformation in the context of Model-Driven Data Warehouse is ensured by human experts. It generates an exorbitant cost and requires high proficiency. We propose in this paper a machine learning approach to re...
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ISBN:
(纸本)9780769545967
Model transformation in the context of Model-Driven Data Warehouse is ensured by human experts. It generates an exorbitant cost and requires high proficiency. We propose in this paper a machine learning approach to reduce the expert contribution in the transformation process. We propose to express the model transformation problem as an inductive logic programming one and to use existing project traces to find the best business transformation rules. We used the Aleph ILP system to learn such rules. Obtained results show that found rules are close to expert ones. Within our application context, we need to deal with several dependent concepts. Taking into account work in Layered Learning, we propose a new methodology that automatically updates the background knowledge of the concepts to be learned. Experimental results support the conclusion that this approach is suitable to solve this kind of problem.
Model transformation by example [18] is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which descr...
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ISBN:
(纸本)9781595934802
Model transformation by example [18] is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming [14] which aims at the inductive construction of first-order clausal theories from examples and background knowledge.
We describe an inductive logic programming (ILP) approach to learning descriptions in Description logics (DL) under uncertainty. The approach is based on implementing many-valued DL proofs as propositionalizations of ...
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ISBN:
(纸本)9781424469208
We describe an inductive logic programming (ILP) approach to learning descriptions in Description logics (DL) under uncertainty. The approach is based on implementing many-valued DL proofs as propositionalizations of the elementary DL constructs and then providing this implementation as background predicates for ILP. The proposed methodology is tested on a many-valued variation of eastbound-trains and Iris, two well known and studied Machine Learning datasets.
Here, we propose a technique to acquire knowledge for baseball digest video production using an inductive inference approach. We integrated the concept of inductive logic programming (ILP) and baseball game metadata t...
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ISBN:
(纸本)9781424440894
Here, we propose a technique to acquire knowledge for baseball digest video production using an inductive inference approach. We integrated the concept of inductive logic programming (ILP) and baseball game metadata to enable learning of the highlight scene definition from digest video produced by a TV director ILP is a learning method formed at the intersection of machine learning and logicprogramming, and ILP processor can acquire the highlight scene definition by inductive learning from scenes that are selected as highlights in sports news. This technique makes it possible to generate a semantic digest automatically, which includes not only score scenes hut also attractive scenes reflecting the director's intention.
Relevant information extraction from text and web pages in particular is an intensive and time-consuming task that needs important semantic resources. Thus, to be efficient, automatic information extraction systems ha...
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ISBN:
(纸本)9781479929719
Relevant information extraction from text and web pages in particular is an intensive and time-consuming task that needs important semantic resources. Thus, to be efficient, automatic information extraction systems have to exploit semantic resources (or ontologies) and employ machine-learning techniques to make them more adaptive. This paper presents an Ontology-based Information Extraction method using inductive logic programming that allows inducing symbolic predicates expressed in Horn clausal logic that subsume information extraction rules. Such rules allow the system to extract class and relation instances from English corpora for ontology population purposes. Several experiments were conducted and preliminary experimental results are promising, showing that the proposed approach improves previous work over extracting instances of classes and relations, either separately or altogether.
inductive logic programming is a subfield of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In its core form, it deals with the problem of finding a hypothesis th...
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ISBN:
(纸本)9783642253232;9783642253249
inductive logic programming is a subfield of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In its core form, it deals with the problem of finding a hypothesis that covers all positive examples and excludes all negative examples. The coverage test and the method to obtain a hypothesis from a given template have been efficiently implemented using constraint satisfaction techniques. In this paper we suggest a method how to efficiently generate the template by remembering a history of generated templates and using this history when adding predicates to a new candidate template. This method significantly outperforms the existing method based on brute-force incremental extension of the template.
inductive logic programming (ILP) deals with the problem of finding a hypothesis covering all positive examples and excluding negative examples. One of the sub-problems is specifying the structure of the hypothesis, t...
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ISBN:
(纸本)9783642154300
inductive logic programming (ILP) deals with the problem of finding a hypothesis covering all positive examples and excluding negative examples. One of the sub-problems is specifying the structure of the hypothesis, that is, the choice of atoms and position of variables in the atoms. In this paper we suggest using constraint satisfaction to describe which variables are unified in the hypotheses. This corresponds to finding the position of variables in atoms. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.
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