AIMC Topic: Computational Biology
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Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.
RNA biology
May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.
International journal of molecular sciences
May 16, 2024
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determina...
Deciphering the Genetic Links between Psychological Stress, Autophagy, and Dermatological Health: Insights from Bioinformatics, Single-Cell Analysis, and Machine Learning in Psoriasis and Anxiety Disorders.
International journal of molecular sciences
May 15, 2024
The relationship between psychological stress, altered skin immunity, and autophagy-related genes (ATGs) is currently unclear. Psoriasis is a chronic skin inflammation of unclear etiology that is characterized by persistence and recurrence. Immune dy...
Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome.
Omics : a journal of integrative biology
May 15, 2024
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline tha...
Artificial neural networks for model identification and parameter estimation in computational cognitive models.
PLoS computational biology
May 15, 2024
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to id...
CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction.
Computers in biology and medicine
May 14, 2024
Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of ther...
Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.
STAR protocols
May 14, 2024
The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal...
Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.
PloS one
May 14, 2024
To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combin...
Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence.
Current opinion in structural biology
May 13, 2024
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods c...
Deep neural network model for enhancing disease prediction using auto encoder based broad learning.
SLAS technology
May 13, 2024
Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ...
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