An algorithm evaluation for discovering classification rules with gene expression programming [PDF]
International Journal of Computational Intelligence Systems, 2016 In recent years, evolutionary algorithms have been used for classification tasks. However, only a limited number of comparisons exist between classification genetic rule-based systems and gene expression programming rule-based systems.
Alain Guerrero-Enamorado +3 more
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A realistic assessment of methods for extracting gene/protein interactions from free text [PDF]
BMC Bioinformatics, 2009 Background The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research.
Shepherd Adrian J +2 more
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Evaluating the reproducibility of single-cell gene regulatory network inference algorithms [PDF]
Frontiers in Genetics, 2020 Abstract Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of ...
Yoonjee Kang +2 more
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Evaluation of clustering algorithms for gene expression data [PDF]
BMC Bioinformatics, 2006 Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature.
Datta Somnath, Datta Susmita
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Benchmarker: An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms [PDF]
The American Journal of Human Genetics, 2018 Genome-wide association studies (GWAS) are valuable for understanding human biology, but associated loci typically contain multiple associated variants and genes. Thus, algorithms that prioritize likely causal genes and variants for a given phenotype can provide biological interpretations of association data.
Fine, Rebecca S. +4 more
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A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data [PDF]
BioData Mining, 2012 Abstract Background Several biclustering algorithms have been proposed to identify biclusters, in which genes share similar expression patterns across a number of conditions. However, different algorithms would yield different biclusters and further lead to distinct conclusions. Therefore, some testing and comparisons
Li Li +5 more
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Techniques for clustering gene expression data [PDF]
, 2008 Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate
Kerr, Gráinne +7 more
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Partial mixture model for tight clustering of gene expression time-course [PDF]
, 2008 Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters.
Li Chang-Tsun +8 more
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Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes [PDF]
BMC Bioinformatics, 2006 A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of ...
Datta Somnath, Datta Susmita
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New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile
IEEE Access, 2019 Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy.
Nada Almugren, Hala M. Alshamlan
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