AIMC Topic: Computational Biology
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Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool.
Experimental biology and medicine (Maywood, N.J.)
Dec 23, 2024
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic m...
Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.
PloS one
Dec 23, 2024
Essential genes are those whose presence is vital for a cell's survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically mod...
Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.
The Journal of pathology
Dec 22, 2024
Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conv...
Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.
International journal of molecular sciences
Dec 21, 2024
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus family with high infectivity and pathogenicity and is the primary pathogen causing the global pandemic of coronavirus disease 2019 (COVID-19). Phosphory...
Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.
BMC pregnancy and childbirth
Dec 21, 2024
PURPOSE: This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.
Domain adaptation in small-scale and heterogeneous biological datasets.
Science advances
Dec 20, 2024
Machine-learning models are key to modern biology, yet models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories due to both technical and biological differences. Domain adaptation, a type of t...
Identification and validation of susceptibility modules and hub genes in polyarticular juvenile idiopathic arthritis using WGCNA and machine learning.
Autoimmunity
Dec 19, 2024
BACKGROUND: Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflam...
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.
Journal of chemical information and modeling
Dec 19, 2024
Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines trans...
Geometric deep learning improves generalizability of MHC-bound peptide predictions.
Communications biology
Dec 19, 2024
The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immunity. Recent advances in cancer immunotherapies demand for more accurate computational prediction of ...
Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.
Frontiers in immunology
Dec 19, 2024
INTRODUCTION: Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabe...
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