AIMC Topic: Gene Expression Profiling

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Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks.

Molecules (Basel, Switzerland)
The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of ...

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

Biochimica et biophysica acta. Molecular basis of disease
Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene ide...

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine.

Biochimica et biophysica acta. Molecular basis of disease
Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to...

Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein-protein interaction network.

Biochimica et biophysica acta. Molecular basis of disease
Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are...

Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.

Molecular medicine reports
The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus da...

Comparative analysis of weighted gene co-expression networks in human and mouse.

PloS one
The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-...

A deep auto-encoder model for gene expression prediction.

BMC genomics
BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expressio...

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.

PLoS computational biology
Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series ...

Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

Comparative biochemistry and physiology. Part D, Genomics & proteomics
In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present ...

A Bi-Objective RNN Model to Reconstruct Gene Regulatory Network: A Modified Multi-Objective Simulated Annealing Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Gene Regulatory Network (GRN) is a virtual network in a cellular context of an organism, comprising a set of genes and their internal relationships to regulate protein production rate (gene expression level) of each other through coded proteins. Comp...