AIMC Topic: Computational Biology

Clear Filters Showing 3301 to 3310 of 4583 articles

A generic pipeline for CADD score generation: chickenCADD and turkeyCADD.

G3 (Bethesda, Md.)
Combined Annotation Dependent Depletion (CADD) is a machine learning approach used to predict the deleteriousness of genetic variants across a genome. By integrating diverse genomic features, CADD assigns a PHRED-like rank score to each potential var...

Speaking Mathematical Models into Existence.

Cancer research
Mathematical and computational modeling enables in silico testing of hypotheses, experimental design, and interventional strategies. However, building, sharing, and applying complex models require technical skills and software development knowledge t...

A Roadmap for the Future of Systems Biology in Cancer Research.

Cancer research
Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as adva...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Computational biology and chemistry
Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for gliomas of different grades and molecular types. However, current glioma molecular subtyping systems ...

An integrated bioinformatics and machine learning-based approach to depict key immunological players associated with candidemia during immunodeficiency.

Computational biology and chemistry
It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign yeast form to an invasive hyphal form which leads to systemic candidiasis with high mortality rate. H...

Unveiling the dark matter of the metabolome: A narrative review of bioinformatics tools for LC-HRMS-based compound annotation.

Talanta
Compound annotation, including the unveiling of dark matter in the metabolomics study represents a pivotal undertaking within the metabolomics field, serving as the linchpin for unraveling the identities and attributes of chemical entities. This narr...

Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.

Renal failure
Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerulonephritis. Oxidative stress plays a critical role in the development and progression of ANCA-associ...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

Virulence
() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infection. In the present study, our goal is to leverage the whole-genome data of to develop practical an...

Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.

Artificial cells, nanomedicine, and biotechnology
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets an...