A L1-regularized feature selection method for local dimension reduction on microarray data
Sign Up to like & getrecommendations! Published in 2017 at "Computational biology and chemistry"
DOI: 10.1016/j.compbiolchem.2016.12.010
Abstract: Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for… read more here.
Keywords: dimension reduction; local dimension; microarray; microarray data ... See more keywords
Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.
Sign Up to like & getrecommendations! Published in 2020 at "Medical hypotheses"
DOI: 10.1016/j.mehy.2020.109577
Abstract: Machine learning and deep learning methods aims to discover patterns out of datasets such as, microarray data and medical data. In recent years, the importance of producing microarray data from tissue and cell samples and… read more here.
Keywords: classification; diagnosis classification; dataset; dimension reduction ... See more keywords
Gene selection for microarray data classification via dual latent representation learning
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.07.047
Abstract: Abstract With the rapid development of genetic sequencing and DNA microarray technologies, a large number of gene expression data has been generated, which provides an important reference for tumor diagnosis. However, it is challenging to… read more here.
Keywords: representation; latent representation; microarray data; gene ... See more keywords
An estimation of the prevalence of genomic disorders using chromosomal microarray data
Sign Up to like & getrecommendations! Published in 2018 at "Journal of human genetics"
DOI: 10.1038/s10038-018-0451-x
Abstract: Multiple genomic disorders result from recurrent deletions or duplications between low copy repeat (LCR) clusters, mediated by nonallelic homologous recombination. These copy number variants (CNVs) often exhibit variable expressivity and/or incomplete penetrance. However, the population… read more here.
Keywords: genomic disorders; chromosomal microarray; genetics; prevalence ... See more keywords
Clustering by fast search and merge of local density peaks for gene expression microarray data
Sign Up to like & getrecommendations! Published in 2017 at "Scientific Reports"
DOI: 10.1038/srep45602
Abstract: Clustering is an unsupervised approach to classify elements based on their similarity, and it is used to find the intrinsic patterns of data. There are enormous applications of clustering in bioinformatics, pattern recognition, and astronomy.… read more here.
Keywords: local density; expression microarray; gene expression; density ... See more keywords
Factors contributing to variability of glycan microarray binding profiles.
Sign Up to like & getrecommendations! Published in 2019 at "Faraday discussions"
DOI: 10.1039/c9fd00021f
Abstract: Protein-carbohydrate interactions play significant roles in a wide variety of biological systems. Glycan microarrays are commonly utilized to interrogate the selectivity, sensitivity, and breadth of these complex protein-carbohydrate interactions. During the past two decades, numerous… read more here.
Keywords: microarray; carbohydrate interactions; factors contributing; glycan microarray ... See more keywords
CarbArrayART: a new software tool for carbohydrate microarray data storage, processing, presentation, and reporting
Sign Up to like & getrecommendations! Published in 2022 at "Glycobiology"
DOI: 10.1093/glycob/cwac018
Abstract: Abstract Glycan microarrays are essential tools in glycobiology and are being widely used for assignment of glycan ligands in diverse glycan recognition systems. We have developed a new software, called Carbohydrate microArray Analysis and Reporting… read more here.
Keywords: new software; geometry; microarray; microarray data ... See more keywords
ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data
Sign Up to like & getrecommendations! Published in 2021 at "BMC Bioinformatics"
DOI: 10.1186/s12859-021-04443-7
Abstract: Background Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great… read more here.
Keywords: improved regularization; feature selection; ilrc hybrid; microarray data ... See more keywords
The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization
Sign Up to like & getrecommendations! Published in 2020 at "PLoS ONE"
DOI: 10.1371/journal.pone.0232955
Abstract: Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results… read more here.
Keywords: microarray; sensitivity transcriptomics; microarray data; normalization ... See more keywords
slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Software"
DOI: 10.18637/jss.v090.i09
Abstract: The development of simulation-based methods, such as Markov chain Monte Carlo (MCMC), has contributed to an increased interest in the Bayesian framework as an alternative to deal with factor models. Many studies have used Bayesian… read more here.
Keywords: factor analysis; microarray data; factor; slfm package ... See more keywords
Learning Differentially Expressed Gene Pairs in Microarray Data.
Sign Up to like & getrecommendations! Published in 2017 at "Studies in health technology and informatics"
DOI: 10.3233/978-1-61499-753-5-191
Abstract: To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers… read more here.
Keywords: expressed gene; microarray data; differentially expressed; gene pairs ... See more keywords