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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 11th International M Clelia DI Serio,Pietro Liò,Roberto Tagliaferr

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樓主: 嚴厲
31#
發(fā)表于 2025-3-26 22:38:14 | 只看該作者
32#
發(fā)表于 2025-3-27 03:02:28 | 只看該作者
Detecting Overlapping Protein Communities in Disease Networksative and the semantic information, that we call . method. We applied . in analyzing Protein-protein interactions (.) networks of . infection and Leukemia in Homo sapiens. . found significant overlapping biological communities. In particular, it found a strong relationship between . and Leukemia as
33#
發(fā)表于 2025-3-27 08:30:39 | 只看該作者
Approximate Abelian Periods to Find Motifs in Biological Sequences it is a sequence of permutations of a length–. string. In this paper, we define an approximate variant of Abelian periods which allows variations between adjacent elements of the sequence. Particularly, we compare two adjacent elements in the sequence using .– and .– metrics. We develop an algorith
34#
發(fā)表于 2025-3-27 12:16:45 | 只看該作者
Sem Best Shortest Paths for the Characterization of Differentially Expressed Genesf gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, f
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發(fā)表于 2025-3-27 15:18:09 | 只看該作者
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發(fā)表于 2025-3-27 19:28:42 | 只看該作者
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發(fā)表于 2025-3-28 00:47:28 | 只看該作者
38#
發(fā)表于 2025-3-28 05:18:10 | 只看該作者
39#
發(fā)表于 2025-3-28 06:56:59 | 只看該作者
https://doi.org/10.1007/978-3-8348-9110-5e rules with low IC. This paper presents a methodology for extracting Weighted Association Rules from GO implemented in a tool named GO-WAR (Gene Ontology-based Weighted Association Rules). It is able to extract association rules with a high level of IC without loss of Support and Confidence from a
40#
發(fā)表于 2025-3-28 13:48:19 | 只看該作者
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