AIMC Topic: Machine Learning

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AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Journal of chemical information and modeling
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...

A first step towards a machine learning-based framework for bloodstain classification in forensic science.

Forensic science international
Bloodstains found at a crime scene can help estimate the events that occurred during the crime. Reconstructing the crime scene by analyzing the bloodstain pattern contributes to understanding the bloody event. Therefore, it is essential to classify b...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

International journal of biological macromolecules
Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors, particularly diffuse large B-cell lymphoma (DLBCL). However, the thorough characterization of ferroptosis-related genes in DLBCL remains inadequatel...

Machine learning accelerates the discovery of epitope-based dual-bioactive peptides against skin infections.

International journal of antimicrobial agents
OBJECTIVES: Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold s...

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

International journal of biological macromolecules
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functi...

Using machine learning approaches to develop a fast and easy-to-perform diagnostic tool for patients with light chain amyloidosis: a retrospective real-world study.

Annals of hematology
Immunoglobulin light chain (AL) amyloidosis is a severe disorder caused by the accumulation of amyloid fibrils, leading to organ failure. Early diagnosis is crucial to prevent irreversible damage, yet it remains a challenge due to nonspecific symptom...

Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

Journal of translational medicine
BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urba...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

Communications biology
The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, cu...

Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).

Scientific reports
Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to pre...

Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.

Frontiers in immunology
BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.