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The relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS weight for a homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic tance-assisted PRS mbination Pipeline for erse Genetic ncestrie ( ) to interpolate a harmonized PRS for diverse, especially admixed, ancestries, leveraging multiple PRS weights fine-tuned within single-ancestry samples and genetic distance. DiscoDivas treats ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries.
View Article and Find Full Text PDFPost-processing enhances protein secondary structure prediction with second order deep learning and embeddings.
Comput Struct Biotechnol J
January 2025
University of Cyprus, Department of Computer Science, Nicosia, Cyprus.
Protein Secondary Structure Prediction (PSSP) is regarded as a challenging task in bioinformatics, and numerous approaches to achieve a more accurate prediction have been proposed. Accurate PSSP can be instrumental in inferring protein tertiary structure and their functions. Machine Learning and in particular Deep Learning approaches show promising results for the PSSP problem.
View Article and Find Full Text PDFDeep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.
ACS Omega
January 2025
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.
View Article and Find Full Text PDFLWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification.
Front Plant Sci
January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis.
MethodsX
June 2025
Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning.
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