Results 181 to 190 of about 2,692,793 (237)

Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification. [PDF]

Diagnostics (Basel)
Alkamli S, Alshamlan H.
europepmc   +1 more source

How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO. [PDF]

Brief Bioinform
Escobedo-Muñoz AS, Carmona-Campos D, Trapaga AGG, Freyre-González JA.   +3 more
europepmc   +1 more source

Integrated Single-Cell Analysis Reveals the Heterogeneity of Tumor-Associated Macrophages and Their Implications for Immunotherapy in Colorectal Cancer. [PDF]

J Inflamm Res
Xu G, Fang B, Tang X, Wei Q, Li J.
europepmc   +1 more source

ACG-SFE: Adaptive cluster-guided simple, fast, and efficient feature selection for high-dimensional microarray data in binary classification. [PDF]

PLoS One
Tye YW, Chew X, Yusof UK, Tulpar S.
europepmc   +1 more source

A workflow for correlative <i>in situ</i> nanochip liquid cell transmission electron microscopy and atom probe tomography enabled by cryogenic plasma focused ion beam.

Nanoscale Horiz
Mulcahy N, Douglas JO, Jannat SR, Worch L, Topore G, Gault B, Ryan MP, Conroy MS.   +7 more
europepmc   +1 more source

Fuzzy set-based microarray data analysis techniques for interesting block identification

2009 IEEE International Conference on Fuzzy Systems, 2009
Microarrays are one of biotechnology products which enable to measure the expression level of thousands of genes simultaneously. It is sometimes crucial to identify some interesting blocks from microarray data for further investigation. Due to the massive volume of data, it is desirable to get assistance of software tools to handle this task.
Keon Myung Lee, Kyung Soon Hwang, Chan Hee Lee   +2 more
semanticscholar   +3 more sources

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Analysis techniques for microarray time-series data

Proceedings of the fifth annual international conference on Computational biology, 2001
We address possible limitations of publicly available data sets of yeast gene expression. We study the predictability of known regulators via time-series analysis, and show that less than 20% of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets.
Vladimir, Filkov, Steven, Skiena, Jizu, Zhi   +2 more
openaire   +3 more sources

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012
A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics.
Lazar, Cosmin, Taminau, Jonatan, Meganck, Stijn, Steenhoff, David, Nowe, Ann, Coletta, Alain, Molter, Colin, De Schaetzen, Virginie, Duque, Robin, Bersini, Hugues   +9 more
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Pattern Recognition Techniques in Microarray Data Analysis

Annals of the New York Academy of Sciences, 2002
Abstract:Recent development of technologies (e.g., microarray technology) that are capable of producing massive amounts of genetic data has highlighted the need for new pattern recognition techniques that can mine and discoverbiologically meaningfulknowledge in large data sets.
F. Valafar
openaire   +3 more sources