AIMC Topic: Gene Expression Regulation, Bacterial

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Detecting operons in bacterial genomes via visual representation learning.

Scientific reports
Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups ...

Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important to understand the differences between planktonic and biofilm growth forms in the pathogen since conventional treatments targeting the planktonic form...

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

Nature communications
The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcrip...

Molecules Autoinducer 2 and cjA and Their Impact on Gene Expression in Campylobacter jejuni.

Journal of molecular microbiology and biotechnology
Quorum sensing is a widespread form of cell-to-cell communication, which is based on the production of signaling molecules known as autoinducers (AIs). The first group contains highly species-specific N-acyl homoserine lactones (N-AHLs), generally kn...

Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities.

Physical biology
Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating gene expression. However, the biology of QS remains incompletely ...

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

Journal of bioinformatics and computational biology
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the mo...

A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data.

PloS one
Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribut...

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

Computers in biology and medicine
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments i...

Improving prediction of bacterial sRNA regulatory targets with expression data.

NAR genomics and bioinformatics
Small regulatory RNAs (sRNAs) are widespread in bacteria. However, characterizing the targets of sRNA regulation in a way that scales with the increasing number of identified sRNAs has proven challenging. Computational methods offer one means for eff...

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Nucleic acids research
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...