Results 31 to 40 of about 191,482 (312)

A hierarchical multi-label classification ant colony algorithm for protein function prediction [PDF]

, 2010
This paper proposes a novel ant colony optimisation (ACO) algorithm tailored for the hierarchical multi-label classification problem of protein function prediction.
Otero, Fernando E.B., Colin G. Johnson, Johnson, Colin G., Alex A. Freitas, Freitas, Alex A., Fernando E. B. Otero   +5 more
core   +1 more source

Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels [PDF]

Asian Pacific Journal of Cancer Prevention, 2016
Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor ...
Maxim D, Podolsky, Anton A, Barchuk, Vladimir I, Kuznetcov, Natalia F, Gusarova, Vadim S, Gaidukov, Segrey A, Tarakanov   +5 more
openaire   +2 more sources

Wigwams : identifying gene modules co-regulated across multiple biological conditions [PDF]

, 2014
Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g.
Rhodes, Johanna, Jenkins, Dafyd J, Jironkin, Aleksey, Kiddle, Steven J., Beynon, Jim, Buchanan-Wollaston, Vicky, Kiddle, Steven J, Hills, Claire E., Denby, Katherine J, Denby, Katherine J., Hill, Claire, Jenkins, Dafyd J., Zhang, Peijun, Polański, Krzysztof, Kiddle, Steven, Ott, Sascha, Rhodes, Johanna; id_orcid   +16 more
core   +1 more source

Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources [PDF]

, 2008
Background: Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict ...
Hofstede, K.J., Oscar P Kuipers, Hijum, Sacha A.F.T. van,, Breitling, Rainer, Blom, E.J., Breitling, R., Breitling, Rainer,, Blom Evert, Roerdink, J.B.T.M., Hofstede Klaas, Breitling Rainer, Jos BTM Roerdink, Blom, Evert Jan, Klaas Hofstede, Roerdink, J.B.T.M.; id_orcid, Kuipers, Oscar P.,, van Hijum, S.A F T, Kuipers, O.P., Hofstede, Klaas Jan,, Roerdink, Jos B T M, van Hijum Sacha AFT, Roerdink Jos BTM, Kuipers, Oscar P., Rainer Breitling, van Hijum, S.A.F.T., Kuipers Oscar P, Hofstede, Klaas Jan, van Hijum, Sacha A F T, Blom, Evert Jan,, Sacha AFT van Hijum, Roerdink, Jos B.T.M.,, Evert Blom, Kuipers, O.P.; id_orcid   +32 more
core   +1 more source

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. [PDF]

PLoS ONE, 2011
RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently.
Marco Grimaldi, Roberto Visintainer, Giuseppe Jurman   +2 more
doaj   +1 more source

The Performance Evaluation of The Random Forest Algorithm for A Gene Selection in Identifying Genes Associated with Resectable Pancreatic Cancer in Microarray Dataset: A Retrospective Study.

Cell journal, 2023
In microarray datasets, hundreds and thousands of genes are measured in a small number of samples, and sometimes due to problems that occur during the experiment, the expression value of some genes is recorded as missing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes.
Rabiei, Niloofar, Soltanian, Ali Reza, Farhadian, Maryam, Bahreini, Fatemeh   +3 more
openaire   +3 more sources

Exploratory and Inferential Analysis of Gene Cluster Neighborhood Graphs [PDF]

, 2009
Many different cluster methods are frequently used in gene expression data analysis to find groups of co–expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred.
Voglhuber Ingo, Scharl Theresa, Leisch, Friedrich, Theresa Scharl, Leisch Friedrich, Friedrich Leisch, Voglhuber, Ingo, Ingo Voglhuber, Scharl, Theresa   +8 more
core   +1 more source

Computation of significance scores of unweighted Gene Set Enrichment Analyses

BMC Bioinformatics, 2007
Background Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any ...
Lenhof Hans-Peter, Backes Christina, Keller Andreas   +2 more
doaj   +1 more source

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection

BMC Bioinformatics, 2023
Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer.
Rajul Mahto, Saboor Uddin Ahmed, Rizwan ur Rahman, Rabia Musheer Aziz, Priyanka Roy, Saurav Mallik, Aimin Li, Mohd Asif Shah   +7 more
doaj   +1 more source

A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm

BMC Medical Informatics and Decision Making, 2021
Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples.
Saeid Azadifar, Ali Ahmadi
doaj   +1 more source