AIMC Topic: Machine Learning

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Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification.

Nature communications
Glycosylation changes are closely related to various diseases, including cancer. The quantitative analysis of site-specific glycans at proteomics scale remains challenging due to low glycopeptide spectra interpretation. Here, we present GlyPep-Quant,...

Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity.

Nature communications
Innovative identification technologies for hematopoietic stem cells (HSCs) have expanded the scope of stem cell biology. Clinically, the functional quality of HSCs critically influences the safety and therapeutic efficacy of stem cell therapies. Howe...

Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study.

Journal of medical Internet research
BACKGROUND: Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). However, individual LLMs often exhibit varying performance across different medical QA...

Robustness evaluation against corruptions for Optical Diffraction Tomography-based classifiers.

Computers in biology and medicine
Optical Diffraction Tomography (ODT) is a promising technique for three-dimensional imaging, but practical use demands rigorous robustness testing due to real-world noise factors. Despite the growing importance of machine learning safety, robustness ...

CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.

Journal of computer-aided molecular design
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection...

Decoding depression: Event related potential dynamics and predictive neural signatures of depression severity.

Journal of affective disorders
Depression is a heterogeneous disorder marked by disruptions in cognitive and affective processing. While self-reported measures and clinical interviews remain the diagnostic standard, integrating objective neurophysiological markers could enhance as...

Comparative machine learning analysis for predicting organ tropism in breast cancer and identifying key gene signatures.

Computers in biology and medicine
BACKGROUND: Breast cancer metastasis (BCM) metastasizes preferentially to certain organs. Important genetic markers can be used for early detection and treatment. Machine learning (ML) can efficiently handle gene expression data to enhance metastasis...

The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review.

Acta tropica
Dengue fever remains a significant global health threat, particularly in tropical and subtropical regions, where rapid urbanization and climate variability exacerbate its spread. Traditional surveillance and control systems often struggle with delaye...

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus.

Scientific reports
An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms remain unclear. This study integrates epidemiological and bioinformatics approaches to investigate thei...