AIMC Topic: Machine Learning

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Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Journal of chemical information and modeling
Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we pr...

Mitigating Grand Challenges in Life Cycle Inventory Modeling through the Applications of Large Language Models.

Environmental science & technology
The accuracy of life cycle assessment (LCA) studies is often questioned due to the two grand challenges of life cycle inventory (LCI) modeling: (1) missing foreground flow data and (2) inconsistency in background data matching. Traditional mechanisti...

How does green manufacturing enhance corporate ESG performance? - Empirical evidence from machine learning and text analysis.

Journal of environmental management
Green manufacturing, widely recognized as a crucial avenue for companies to achieve sustainable competitive advantages, exerts significant spillover effects on both environmental protection and social responsibility. Accordingly, this can significant...

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing.

Journal of environmental management
Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages rem...

Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of ch...

ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records.

Progress in cardiovascular diseases
BACKGROUND: Natural language processing (NLP) can facilitate research utilizing data from electronic health records (EHRs). Large language models can potentially improve NLP applications leveraging EHR notes. The objective of this study was to assess...

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

PloS one
There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validati...

A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs.

Journal of veterinary internal medicine
BACKGROUND: The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty.

The Journal of arthroplasty
BACKGROUND: While predictive capabilities of machine learning (ML) algorithms for hip and knee total joint arthroplasty (TJA) have been demonstrated in previous studies, their performance in racial and ethnic minority patients has not been investigat...