AIMC Topic: Machine Learning

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Molecular docking-QSAR-Kronecker-regularized least squares-based multiple machine learning for assessment and prediction of PFAS-protein binding interactions.

Journal of hazardous materials
Ubiquitous per- and poly-fluoroalkyl substances (PFAS) threaten human's health and attract worldwide attention. PFAS-mediated toxicity involves adverse effects of PFAS on proteins, and assessment of PFAS-protein binding interactions helps to explain ...

A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data.

Analytical biochemistry
O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postope...

Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.

Medical image analysis
We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique interactive and spatially heterogeneous nature of this application to develop a registration algori...

Multiomics evaluation and machine learning optimize molecular classification, prediction of prognosis and immunotherapy response for ovarian cancer.

Pathology, research and practice
BACKGROUND: Ovarian cancer (OC), owing to its substantial heterogeneity and high invasiveness, has historically been devoid of precise, individualized treatment options. This study aimed to establish integrated consensus subtypes of OC using differen...

Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.

Preventive medicine
OBJECTIVES: To evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

Computers in biology and medicine
In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supporting the early diagnosis of Alzheimer's disease and predicting its progression. Traditional approaches represent handwriting tasks using a single fe...

Rapid and accurate identification of foodborne bacteria: a combined approach using confocal Raman micro-spectroscopy and explainable machine learning.

Analytical and bioanalytical chemistry
This study proposes a rapid identification method for foodborne pathogens by combining Raman spectroscopy with explainable machine learning. Spectral data of nine common foodborne pathogens are collected using a laser confocal Raman spectrometer, and...

Machine Learning-Driven Quantum Sequencing of Natural and Chemically Modified DNA.

ACS applied materials & interfaces
Simultaneous identification of natural and chemically modified DNA nucleotides at molecular resolution remains a pivotal challenge in genomic science. Despite significant advances in current sequencing technologies, the ability to identify subtle cha...

AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.

Medicina (Kaunas, Lithuania)
Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring sys...

Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of Machine Learning Algorithms with Microfluidic Technologies.

Biosensors
Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant...