AIMC Topic: Gene Regulatory Networks

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State of the art of fuzzy methods for gene regulatory networks inference.

TheScientificWorldJournal
To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protei...

Ontology application and use at the ENCODE DCC.

Database : the journal of biological databases and curation
The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to create a catalog of genomic annotations. To date, the project has generated over 4000 experiments across more than 350 cell lines and tissues using a wide array o...

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data.

International journal of medical microbiology : IJMM
Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the...

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.

BMC bioinformatics
BACKGROUND: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the li...

EcoliNet: a database of cofunctional gene network for Escherichia coli.

Database : the journal of biological databases and curation
During the past several decades, Escherichia coli has been a treasure chest for molecular biology. The molecular mechanisms of many fundamental cellular processes have been discovered through research on this bacterium. Although much basic research n...

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...

Unraveling risk factors and transcriptomic signatures in liver cancer progression and mortality through machine learning and bioinformatics.

Briefings in functional genomics
Liver cancer (LC) is the second leading cause of cancer-related deaths globally, yet the molecular mechanisms linking its progression with associated risk factors (RFs) remain poorly understood. To address this, we developed an integrative multi-stag...

Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.

Genetics
Gene expression patterns are determined to a large extent by transcription factor (TF) binding to noncoding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because nonfu...

Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.

Artificial cells, nanomedicine, and biotechnology
Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore m...