AIMC Topic: Computational Biology

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Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.

PloS one
Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions s...

Amino acid sequence-based IDR classification using ensemble machine learning and quantum neural networks.

Computational biology and chemistry
Biologically traditional methods, such as the Uversky plot, which rely on hydrophobicity and net charge, have inherent limitations in accurately distinguishing intrinsically disordered regions (IDRs) from ordered protein regions. To overcome these co...

Pato: prediction of probiotic bacteria using metabolic features.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, pl...

A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...

DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

BMC biology
BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experim...

Repetitive training enhances the pattern recognition capability of cultured neural networks.

PLoS computational biology
Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neu...

scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.

IET systems biology
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often fa...

Computational models for prediction of m6A sites using deep learning.

Methods (San Diego, Calif.)
RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modificat...

The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning.

International journal of biological macromolecules
Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the ...