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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.
PLoS Comput Biol
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Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
View Article and Find Full Text PDFA data-driven model of disability progression in progressive multiple sclerosis.
Brain Commun
December 2024
Department of Health Sciences (DISSAL), Università di Genova, 16132 Genoa, Italy.
This study applies the Gaussian process progression model, a Bayesian data-driven disease progression model, to analyse the evolution of primary progressive multiple sclerosis. Utilizing data from 1521 primary progressive multiple sclerosis participants collected within the International Progressive Multiple Sclerosis Alliance Project, the analysis includes 18 581 longitudinal time-points (average follow-up time: 28.2 months) of disability assessments including the expanded disability status scale, symbol digit modalities, timed 25-foot-walk, 9-hole-peg test and of MRI metrics such as T1 and T2 lesion volume and normalized brain volume.
View Article and Find Full Text PDFNext-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.
PLoS One
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Department of Information Systems, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Al-Ahsa, Kingdom of Saudi Arabia.
Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute to various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection of diets tailored to individual nutritional needs is challenging because of the intricate nature of foods and the abundance of dietary sources.
View Article and Find Full Text PDFThe bcc coating of Lennard-Jones crystal nuclei vanishes with a change of local structure detection algorithm.
J Chem Phys
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Soft Condensed Matter & Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands.
Since the influential work of ten Wolde, Ruiz-Montero, and Frenkel [Phys. Rev. Lett.
View Article and Find Full Text PDFIntegrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
Viruses
November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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