AIMC Topic: Computational Biology
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Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance.
Computers in biology and medicine
Feb 22, 2025
Peptides are gaining significant attention in diverse fields such as the pharmaceutical market has seen a steady rise in peptide-based therapeutics over the past six decades. Peptides have been utilized in the development of distinct applications inc...
Identification of crucial genes for polycystic ovary syndrome and atherosclerosis through comprehensive bioinformatics analysis and machine learning.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Feb 21, 2025
OBJECTIVE: To identify potential biomarkers in patients with polycystic ovary syndrome (PCOS) and atherosclerosis, and to explore the common pathologic mechanisms between these two diseases in response to the increased risk of cardiovascular diseases...
Machine learning-based characterization of PANoptosis-related biomarkers and immune infiltration in ulcerative colitis: A comprehensive bioinformatics analysis and experimental validation.
International immunopharmacology
Feb 21, 2025
Ulcerative colitis (UC) is a heterogeneous autoimmune condition. PANoptosis, a new form of programmed cell death, plays a role in inflammatory diseases. This study aimed to identify differentially expressed PANoptosis-related genes (PRGs) involved in...
Teaching AI to speak protein.
Current opinion in structural biology
Feb 21, 2025
Large Language Models for proteins, namely protein Language Models (pLMs), have begun to provide an important alternative to capturing the information encoded in a protein sequence in computers. Arguably, pLMs have advanced importantly to understandi...
EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.
Journal of chemical information and modeling
Feb 21, 2025
Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine lea...
An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.
BMC biology
Feb 21, 2025
BACKGROUND: Long non-coding RNA (LncRNA) play pivotal roles in various cellular processes, and elucidating their subcellular localization can offer crucial insights into their functional significance. Accurate prediction of lncRNA subcellular localiz...
RNA structure prediction using deep learning - A comprehensive review.
Computers in biology and medicine
Feb 20, 2025
In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led ...
Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction.
Neural networks : the official journal of the International Neural Network Society
Feb 19, 2025
Accurate identification of molecular interactions is crucial for biological network analysis, which can provide valuable insights into fundamental regulatory mechanisms. Despite considerable progress driven by computational advancements, existing met...
Hyperbolic multivariate feature learning in higher-order heterogeneous networks for drug-disease prediction.
Artificial intelligence in medicine
Feb 19, 2025
New drug discovery has always been a costly, time-consuming process with a high failure rate. Repurposing existing drugs offers a valuable alternative and reduces the risks associated with developing new drugs. Various experimental methods have been ...
Compression-enabled interpretability of voxelwise encoding models.
PLoS computational biology
Feb 19, 2025
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models ...
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