Results 251 to 260 of about 1,986,261 (302)

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data. [PDF]

Cell Rep Methods
Nwizu C, Hughes M, Ramseier ML, Navia AW, Shalek AK, Fusi N, Raghavan S, Winter PS, Amini AP, Crawford L.   +9 more
europepmc   +1 more source

Generating explainable hypotheses for drug repurposing with graph neural networks. [PDF]

Sci Rep
Perdomo-Quinteiro P, Guney E, Belmonte-Hernández A.   +2 more
europepmc   +1 more source

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Extending meta-learning framework for clustering gene expression data with component-based algorithm design and internal evaluation measures

International Journal of Data Mining and Bioinformatics, 2016
Class retrieval in gene expression microarray data analysis is highly challenging task. Because of high class imbalance, highly dimensional feature space and small number of samples most of the algorithms fail to capture real complex structures in data 'golden standard'.
M. Vukicevic, S. Radovanović, Boris Delibasic, Milija Suknović   +3 more
semanticscholar   +3 more sources

Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm

International Journal of Data Mining and Bioinformatics, 2017
Laith Abualigah, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar   +2 more
exaly   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source