AIMC Topic: Software

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Sitetack: a deep learning model that improves PTM prediction by using known PTMs.

Bioinformatics (Oxford, England)
MOTIVATION: Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their ana...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

Bioinformatics (Oxford, England)
SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation ...

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Briefings in bioinformatics
Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical...

Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.

Briefings in bioinformatics
RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identification of Nm sites is challenging and resource-intensive. While multiple computational tools have been de...

AIGen: an artificial intelligence software for complex genetic data analysis.

Briefings in bioinformatics
The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized many fields. While DNN plays a central role in modern AI technology, it has rarely been used in gene...

DeepCheck: multitask learning aids in assessing microbial genome quality.

Briefings in bioinformatics
Metagenomic analyses facilitate the exploration of the microbial world, advancing our understanding of microbial roles in ecological and biological processes. A pivotal aspect of metagenomic analysis involves assessing the quality of metagenome-assem...

m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

Briefings in bioinformatics
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing ...

MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.

Briefings in bioinformatics
Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we ex...

MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Briefings in bioinformatics
Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellu...

NucleoFind: a deep-learning network for interpreting nucleic acid electron density.

Nucleic acids research
Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to re...