AIMC Topic: Machine Learning

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Machine Learning-Driven discovery of immunogenic cell Death-Related biomarkers and molecular classification for diabetic ulcers.

Gene
In this study, we redefine the diagnostic landscape of diabetic ulcers (DUs), a major diabetes complication. Our research uncovers new biomarkers linked to immunogenic cell death (ICD) in DUs by utilizing RNA-sequencing data of Gene Expression Omnibu...

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

Journal of chemical information and modeling
The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the ...

Automated linguistic analysis in youth at clinical high risk for psychosis.

Schizophrenia research
Identifying individuals at clinical high risk for psychosis (CHRP) is crucial for preventing psychosis and improving the prognosis for schizophrenia. Individuals at CHR-P may exhibit mild forms of formal thought disorder (FTD), making it possible to ...

Application of machine learning algorithms to predict postoperative surgical site infections and surgical site occurrences following inguinal hernia surgery.

Hernia : the journal of hernias and abdominal wall surgery
PURPOSE: This study aimed to develop, validate, and evaluate machine learning (ML) algorithms for predicting Surgical site infections (SSI) and surgical site occurrences (SSO) after elective open inguinal hernia surgery.

Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins.

Molecular pharmaceutics
Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and ch...

CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques.

Computers in biology and medicine
Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the comp...

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine ...

In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.

Journal of chemical information and modeling
TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and an...

Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.

Journal of chemical information and modeling
In the quest for advanced superconducting materials, the accurate prediction of critical temperatures () poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and structural cha...

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

Journal of chemical theory and computation
The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and m...