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Microarray Analysis

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A L1-regularized feature selection method for local dimension reduction on microarray data.

Computational biology and chemistry
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 classification on mic...

Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scient...

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

BioMed research international
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innova...

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Genetics and molecular research : GMR
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning tech...

Multimodal probabilistic generative models for time-course gene expression data and Gene Ontology (GO) tags.

Mathematical biosciences
We propose four probabilistic generative models for simultaneously modeling gene expression levels and Gene Ontology (GO) tags. Unlike previous approaches for using GO tags, the joint modeling framework allows the two sources of information to comple...

Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.

BMC bioinformatics
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for ident...

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.

Technology and health care : official journal of the European Society for Engineering and Medicine
Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate...

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme.

Computers in biology and medicine
Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be incl...

Brief isoflurane anaesthesia affects differential gene expression, gene ontology and gene networks in rat brain.

Behavioural brain research
Much is still unknown about the mechanisms of effects of even brief anaesthesia on the brain and previous studies have simply compared differential expression profiles with and without anaesthesia. We hypothesised that network analysis, in addition t...