Results 41 to 50 of about 7,680 (217)

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

A Comparative Study of Hyperparameter Optimization for CatBoost: Random Search, Optuna, and Successive Halving

Journal of Applied Informatics and Computing
This study aims to evaluate the effectiveness of three hyperparameter optimization approaches Random Search, Successive Halving, and Optuna in the CatBoost algorithm for modeling individual income using the 2024 SAKERNAS data.
Claudian Tikulimbong Tangdilomban, Kusman Sadik, Aji Hamim Wigena   +2 more
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data

Journal of Applied Informatics and Computing
Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively ...
Muhammad Sidik Latuconsina, Majid Rahardi   +1 more
doaj   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Augmenting heart disease prediction with explainable AI: A study of classification models

Computational and Mathematical Biophysics
Although heart disease stands as a prominent contributor to worldwide deaths, not all individuals affected by it ultimately fall prey to its effects. Timely diagnosis and effective treatment can offer those with heart conditions a high-quality life in ...
Titti Raja Rani, Pukkella Shalini, Radhika Tantravahi Sai Lakshmi   +2 more
doaj   +1 more source

Interpretable Machine Learning for Taxi Tipping Behavior Analysis in Urban Mobility Systems [PDF]

E3S Web of Conferences
Taxi tipping behavior provides a useful case for studying passenger payment decisions in urban mobility services. Using New York City Yellow Taxi trip records from January to March 2025, this study examines two related tasks: predicting tipping ...
He Hui, Wang Kefeng
doaj   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Integrating machine learning, deep learning, and image analysis for seed species classification

Applications in Plant Sciences, EarlyView.
Abstract Premise The growing demand for wildflower seeds in ecological restoration requires reliable species identification, yet current market products often contain heterogeneous species. As seed identification is labor‐intensive and requires advanced botanical knowledge, we evaluated multiple segmentation and classification approaches to determine ...
Jonathan Ashworth, Eloise Barrett, Rebonto Haque, Ben Wildey, Anthony Hall, Felix Shaw, Jose J. De Vega   +6 more
wiley   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source