AIMC Topic: Gene Regulatory Networks

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Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

Identification of disease-associated loci using machine learning for genotype and network data integration.

Bioinformatics (Oxford, England)
MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wid...

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Carcinogenesis
Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the under...

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predic...

Correlation Network Analysis Provides Important Modules and Pathways for Human Hyperlipidemia.

Critical reviews in eukaryotic gene expression
Hyperlipidemia casts great threats to humans around the world. The systemic co-expression and function enrichment analysis for this disease is limited to date. This study was to identify co-expression modules to explore hyperlipidemia-associated func...

Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer.

Screening key lncRNAs for human lung adenocarcinoma based on machine learning and weighted gene co-expression network analysis.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Lung adenocarcinoma (LUAD) accounts for a significant proportion of lung cancer and there have been few diagnostic and therapeutic targets for LUAD due to the lack of specific biomarker. The aim of this study was to identify key long non-...

Quantitative Modelling of the Waddington Epigenetic Landscape.

Methods in molecular biology (Clifton, N.J.)
C.H. Waddington introduced the epigenetic landscape as a metaphor to represent cellular decision-making during development. Like a population of balls rolling down a rough hillside, developing cells follow specific trajectories (valleys) and eventual...

Unsupervised GRN Ensemble.

Methods in molecular biology (Clifton, N.J.)
Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different m...