AIMC Topic: Computational Biology

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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.

Frontiers in endocrinology
BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathog...

PlantPathoPPI: An Ensemble-based Machine Learning Architecture for Prediction of Protein-Protein Interactions between Plants and Pathogens.

Journal of molecular biology
This study aimed to develop a machine learning-based tool for predicting protein-protein interactions (PPIs) between plant-pathogen systems, addressing the challenges of experimental PPI identification. Identifying PPIs in plant-pathogen interactions...

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.

Biomolecules
Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy...

A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

Scientific reports
There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico tool predictions. In this study, we developed a seven-tier classification system for MEFV missense variants of unknown significance and recommended a...

Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.

PLoS computational biology
Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phe...

Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

PLoS computational biology
Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, wit...

Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.

PLoS computational biology
Neutrophils, an essential innate immune cell type with a short lifespan, rely on continuous replenishment from bone marrow (BM) precursors. Although it is established that neutrophils are derived from the granulocyte-macrophage progenitor (GMP), the ...

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes.

PLoS computational biology
The ability to process visual stimuli rich with motion represents an essential skill for animal survival and is largely already present at the onset of vision. Although the exact mechanisms underlying its maturation remain elusive, spontaneous activi...

A critical address to advancements and challenges in computational strategies for structural prediction of protein in recent past.

Computational biology and chemistry
Protein structure prediction has undergone significant advancements, driven by the limitations of experimental techniques like X-ray crystallography, NMR, and cryo-EM, which are costly and time-consuming. To bridge the gap between protein sequences a...