Gastric cancer biomarkers a systems biology approach. Mult mai mult decât documente.

Supliment II JTMR | Proteomics | Biomarker

I also work on combining various molecular biology and genetics resources, such as gene expression data, DNA copy number data, transcription factor binding data [ 38 ], gene ontologies and literature abstracts for elucidating the structure of gene networks. In this context, I have devised an improvement of constraint-based probabilistic network structure inference algorithms regarding the determination of the direction of causal influences [ 262728 ].

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I also studied sparse factorizations such as Nonnegative Matrix Factorization NMF as clustering methods for gene expression data allowing for overlapping clusters [ 313234 ]. The stability of clustering with nonnegative factorizations was addressed using an original meta-clustering approach based on Positive Tensor Factorization PTF [ 323742 ].

A nontrivial generalization of hierarchical clustering dendrograms to biclustering was developed in [ 44 ] and applied to the genomic subclassification of colon cancer.

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A combined use of microarray gene expression data, functional annotations in terms of the Gene Gastric cancer biomarkers a systems biology approach as well as an inductive learner based on Inductive Logic Programming have allowed us to automatically obtain functional descriptions discriminating genes differentially gastric cancer biomarkers a systems biology approach in two types of lung adenocarcinoma [ 24 ]. We are currently developing an ontology-based system for indexing, querying and text mining the biomedical literature Biomine.

The system uses the Flora2 implementation of F-logic. The SILK architecture contains a meta-model of the components to be integrated which is used by a specilized mediator for planning and splitting user queries into queries that can be dealt with by the components [ 2023 ].

In order to be able cancer pulmonar bases medicina integrate complex applications with side-effects, we have developed an original partial order planning algorithm dealing with dependent fluents [ 22 ].

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Description Logics, Inductive Logic Programming, et al. In the past, I have been involved in several Artificial Intelligence projects in the fields of knowledge representation, computational logic, constraint logic programming, machine learning especially inductive logic programminggenetic algorithms, AI planning, intelligent information integration and bioinformatics.

In the field of description logics DLsI have developed efficient inference algorithms for expressive description logics such as those with the transitive closure of relations [ 8 ].

gastric cancer biomarkers a systems biology approach

Taking into account the correspondence with various extensions of the propositional dynamic logic PDLthe obtained results are also applicable to modal, temporal and dynamic logics, as well as to modeling intelligent agents.

I have also introduced the notion of concept reification in description logics [ 9 ]. In the area of machine learning, more precisely Inductive Logic Programming, I have developed a so-called perfect refinement operator that eliminates annoying problems occurring in all theories and implemented ILP systems [ 13 ].

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In [ 15 ], I have shown that the advantages of completeness, non-redundancy and flexibility can be combined by constructing a perfect refinement operator that is "flexible". This should enable a more flexible traversal of the hypotheses space of an ILP system.

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Refining complete clausal theories has hanorac parazitii de vanzare investigated in [ 21 ]. In a different line of work I have constructed refinement operators for description logics DLs which are useful for developing learning systems in DL languages. Additionally, I have considered the application of ILP in the domain of learning trading rules [ 19 ].

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This application is interesting since it involves learning strategies in a domain in which there are no or - in any case - very few regularities in the historical data. It also leads in a natural way to dealing with the problem of learning from disjunctive examples similar to multiple-instance learning.

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In this framework, I have developed and implemented a logic-based language called ExClaim having a meta-level architecture and supporting non-determinism for describing and executing KADS models [ 14 ]. AI, molecular biology and genomics I am extremely interested in applications of AI in molecular biology and genomics, especially in the area of using symbolic machine learning inductive logic programmingconstraint programming and knowledge representation techniques for representing and reasoning about biological function [ 25 ].

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Publications [47] Liviu Badea. Multirelational Consensus Clustering with Nonnegative Decompositions.

Supliment II JTMR

Unsupervised analysis of leukemia and normal hematopoiesis by joint clustering of gene expression data. ISBN: Hepato-Gastroenterology ; 58pp. Generalized Clustergrams for Overlapping Gastric cancer biomarkers a systems biology approach.