Results 41 to 50 of about 2,170,661 (326)
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 +7 more
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Fast training of self organizing maps for the visual exploration of molecular compounds [PDF]
, 2007 Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data.
Di Fatta, Giuseppe +4 more
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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 +2 more
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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
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Machine Learning and Integrative Analysis of Biomedical Big Data. [PDF]
, 2019 Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome)
Choi, Howard +5 more
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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 +3 more
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Evaluation of variable selection methods for random forests and omics data sets
Briefings Bioinform., 2017 Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power.
F. Degenhardt, S. Seifert, S. Szymczak
semanticscholar +1 more source
Big Data and Cognitive Computing, 2022 Every year, biomedical data is increasing at an alarming rate and is being collected from many different sources, such as hospitals (clinical Big Data), laboratories (genomic and proteomic Big Data), and the internet (online Big Data).
Changwon Yoo +3 more
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A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection [PDF]
PeerJ Computer Science, 2023 Background Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors.
Junjian Liu +6 more
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Learning short multivariate time series models through evolutionary and sparse matrix computation [PDF]
, 2006 Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities.
Kok, J, Liu, X, Swift, S
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