AIMC Topic: Oligonucleotide Array Sequence Analysis
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Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.
Bioinformatics (Oxford, England)
Jun 1, 2018
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...
Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have propose...
Humans
Precision Medicine
Mice, Inbred DBA
Mice, Inbred NZB
Gene Expression Profiling
Databases, Genetic
Gene Regulatory Networks
Mice, Inbred C57BL
Diet, High-Fat
Mice
Female
Mice, Inbred Strains
Gene Ontology
Computational Biology
Animals
Mice, 129 Strain
Oligonucleotide Array Sequence Analysis
Male
Rectified factor networks for biclustering of omics data.
Bioinformatics (Oxford, England)
Jul 15, 2017
MOTIVATION: Biclustering has become a major tool for analyzing large datasets given as matrix of samples times features and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. actor nal...
Planning bioinformatics workflows using an expert system.
Bioinformatics (Oxford, England)
Apr 15, 2017
MOTIVATION: Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines...
A Gene Selection Method for Microarray Data Based on Binary PSO Encoding Gene-to-Class Sensitivity Information.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2017
Traditional gene selection methods for microarray data mainly considered the features' relevance by evaluating their utility for achieving accurate predication or exploiting data variance and distribution, and the selected genes were usually poorly e...
Gene-Category Analysis.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Gene-category analysis is one important knowledge integration approach in biomedical sciences that combines knowledge bases such as Gene Ontology with lists of genes or their products, which are often the result of high-throughput experiments, gained...
Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
In this paper, a new approach based on Binary Black Hole Algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach, BBHA is used to perform gene sel...
ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2016
In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigati...
Using Semantic Similarities and csbl.go for Analyzing Microarray Data.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2016
Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challe...
[Study of FN1 and ZNF438 gene expression negatively regulated by androgen receptor in androgen-independent LNCaP cells].
Zhonghua yi xue za zhi
Dec 19, 2015
OBJECTIVE: To verify whether FN1 and ZNF438 are the androgen receptor(AR) target genes in LNCaP-AI cells and investigate the effects of AR exerts on FN1 and ZNF438 gene expression.
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