AIMC Topic: Computational Biology
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Biochemical Characterization of Disease-Associated Variants of Human Ornithine Transcarbamylase.
ACS chemical biology
Mar 10, 2025
Human ornithine transcarbamylase deficiency (OTCD) is the most common ureagenesis disorder in the world. OTCD is an X-linked genetic deficiency in which patients experience hyperammonemia to varying degrees depending on the severity of the genetic mu...
Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.
International journal of molecular sciences
Mar 10, 2025
In recent years, many approved drugs have been discovered using phenotypic screening, which elaborates the exact mechanisms of action or molecular targets of drugs. Drug susceptibility prediction is an important type of phenotypic screening. Large-sc...
Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.
Scientific reports
Mar 10, 2025
Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KCTD10, a protein implicated in a variety of biological pro...
Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.
Frontiers in immunology
Mar 10, 2025
INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfunction. Despite medical advancements, the critical need for research into dependable diagnostic markers ...
BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.
NAR genomics and bioinformatics
Mar 8, 2025
A terminator is a DNA region that ends the transcription process. Currently, multiple computational tools are available for predicting bacterial terminators. However, these methods are specialized for certain bacteria or terminator type (i.e. intrins...
Popfinder: A Highly Effective Artificial Neural Network Package for Genetic Population Assignment.
Molecular ecology resources
Mar 7, 2025
The ability to assign biological samples to source populations with high accuracy and precision based on genetic variation is important for numerous applications from ecological studies through wildlife conservation to epidemiology. However, populati...
DeepMVD: A Novel Multiview Dynamic Feature Fusion Model for Accurate Protein Function Prediction.
Journal of chemical information and modeling
Mar 7, 2025
Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among the...
DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.
Microbiome
Mar 7, 2025
BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefi...
DHUpredET: A comparative computational approach for identification of dihydrouridine modification sites in RNA sequence.
Analytical biochemistry
Mar 6, 2025
Laboratory-based detection of D sites is laborious and expensive. In this study, we developed effective machine learning models employing efficient feature encoding methods to identify D sites. Initially, we explored various state-of-the-art feature ...
EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.
International journal of biological macromolecules
Mar 6, 2025
Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fra...
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