AIMC Topic: Machine Learning

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Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.

Proceedings of the National Academy of Sciences of the United States of America
Histological images offer a wealth of data. Mining these data holds significant potential for enhancing disease diagnosis and prognosis, though challenges remain, especially in noncancer contexts. In this study, we developed a statistical framework t...

Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning.

Water research
The widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic environments, thereby demanding effective treatment strategies for shock loads from industrial discharge...

Machine Learning-Assisted Fe-N-C Single-Atom Nanozyme Rapid Screening Platform for Acetylcholinesterase Inhibitors.

Analytical chemistry
Traditional screening methods for acetylcholinesterase inhibitors (AChEIs) encounter significant challenges due to two primary factors: subjective errors in colorimetric analysis and reliance on laboratory instruments. To overcome these limitations, ...

Low-Cost, High-Accuracy Reactivity Modeling: Integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations.

Journal of chemical information and modeling
Accurate prediction of Gibbs activation energies (Δ) for Diels-Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy ...

Structure-Based Classification of CRISPR/Cas9 Proteins: A Machine Learning Approach to Elucidating Cas9 Allostery.

Journal of molecular biology
The CRISPR/Cas9 system is a powerful gene-editing tool. Its specificity and stability rely on complex allosteric regulation. Understanding these allosteric regulations is essential for developing high-fidelity Cas9 variants with reduced off-target ef...

Targeted inhibition of gastric adenocarcinoma by nano-curcumin liposomes: Insights from combined machine learning and experimental analyses into the mechanisms of cuproptosis and metabolic reprogramming.

International journal of pharmaceutics
PURPOSE: Gastric adenocarcinoma is a highly aggressive malignancy characterized by a complex tumor microenvironment. Nano-curcumin liposomes hold great potential in inhibiting tumor growth and survival, as well as inducing cuproptosis and oxidative s...

Identifying Structure-Activity Relationships for Cyanine-Derived Antibiotics Using Machine Learning and Commercial Large Language Models.

Journal of chemical information and modeling
Understanding the structure-activity relationship (SAR) of antibiotic scaffolds is crucial for the development of antibiotics to counter the growing crisis of antimicrobial resistant bacteria. However, an overwhelming space of structural features imp...

OmniCLIC: A Unified Omics Contrastive Learning Framework for Effective Integration and Classification of Multiomics Data.

Journal of chemical information and modeling
Integrating multiomics data for cancer subtype classification remains a critical yet challenging task due to the high dimensionality, heterogeneity, and limited interpretability of omics features. To address these limitations, we propose OmniCLIC, a ...

Global Meta-Analysis Integrated with Machine Learning Assesses Context-Dependent Microplastic Effects on Soil Microbial Biomass Carbon and Nitrogen.

Environmental science & technology
Microplastics (MPs) in soil can paradoxically stimulate microbial biomass in a highly context-dependent manner, potentially inducing decomposition and affecting carbon and nitrogen cycles. We conducted a global meta-analysis with 90 studies (710 obse...

"Sweet Nanosheet": An Antibody Mimic for Machine Learning-Assisted Ultra-Sensitive Immunochromatographic Assay for Pathogens.

Analytical chemistry
The bacterial surface is rich in diverse molecular features, and fully exploiting these natural recognition mechanisms provides innovative avenues for multimechanism detection of foodborne pathogens. Here, we developed a label-free, dual-modal LFIA p...