AIMC Topic: Machine Learning

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Human essential gene identification based on feature fusion and feature screening.

IET systems biology
Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. H...

Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding.

Journal of translational medicine
BACKGROUND: The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) infection is a challenge in the...

Multimodal machine learning for language and speech markers identification in mental health.

BMC medical informatics and decision making
BACKGROUND: There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variet...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

BMC medical research methodology
OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effec...

A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses.

Scientific reports
Decreasing speed and stride length over successive races have been shown to be associated with musculoskeletal injury (MSI) in racehorses, demonstrating the potential for early detection of MSI through longitudinal monitoring of changes in stride cha...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Scientific reports
Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunc...

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.

Scientific reports
This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential t...

Development and Validation of a Machine Learning-Based Early Warning Model for Lichenoid Vulvar Disease: Prediction Model Development Study.

Journal of medical Internet research
BACKGROUND: Given the complexity and diversity of lichenoid vulvar disease (LVD) risk factors, it is crucial to actively explore these factors and construct personalized warning models using relevant clinical variables to assess disease risk in patie...

Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation.

JMIR medical informatics
BACKGROUND: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of mor...

Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.

eLife
Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study...