Results 271 to 280 of about 191,482 (312)

TMPA-HC: a two-stage heterogeneous multi-population algorithm with cooperative search for high-dimensional feature selection. [PDF]

Sci Rep
Chen A, Pan P, Quan N, Zhou B.
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Enhanced Detection of BRCA Copy Number Alterations Within a Commercial HRD Assay: Implications for Precision Oncology in Ovarian Cancer. [PDF]

Int J Mol Sci
De Bonis M, Iapicca P, De Paolis E, Brisighelli F, Evangelista J, Perrucci A, Ricciardi Tenore C, Maneri G, Concolino P, Piermattei A, Mozzetta I, Pasciuto T, Preziosi A, Giacò L, Duranti S, Nero C, Fagotti A, Minucci A.   +17 more
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F-statistics algorithm for gene clustering evaluation

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, 2010
An enormous amount of microarray data has been generated and archived for a large variety of biological studies such as gene expression. In order to analyze gene expression data, many clustering algorithms have been proposed, but very few techniques have been developed to evaluate those clustering algorithms.
Mohamad Qayoom, Qi Zhang, Christopher Taylor   +2 more
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A new genetic algorithm with statistical gene evaluation

NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Wo, 2002
A new genetic algorithm is proposed to approximately evaluate the gene rather than the chromosome using statistical techniques. Simple statistical quantities are used to find out the influence of individual gene during the evolution and suggest better choices for a gene.
E.C. Yeh, null Yaw-Yu Shyu
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Evaluation of Gene-Finding Algorithms by a Content- Balancing Accuracy Index

Journal of Biomolecular Structure and Dynamics, 2002
A content-balancing accuracy index, called q(9), to evaluate gene-finding algorithms has been proposed. Here the concept of content-balancing means that the evaluation by this index is independent of the coding and non-coding composition of the sequence being evaluated.
Chun-Ting, Zhang, Ren, Zhang
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Biological evaluation of biclustering algorithms using Gene Ontology and chIP-chip data

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
In this paper, we propose a new framework for assessing the biological significance of the outputs of any biclustering algorithm. The framework relies on the p-value computed by a Fisher's exact test on a 2x2 contingency table derived from gene ontology (GO) enrichment level and chromatin immunoprecipitation (ChIP) data enrichment level.
Alain B. Tchagang, Ahmed H. Tewfik, Panayiotis V. Benos   +2 more
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