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| http://dx.doi.org/10.1007/s10460-020-10111-y | DOI Listing |
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Integrating 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.
View Article and Find Full Text PDFExploring Resources and Reasoning Practices in Socioscientific System Modeling for Justice-Centered Science Education.
CBE Life Sci Educ
March 2025
School of Education, University of North Carolina, Chapel Hill, NC 27514.
Integrating science education with social justice is vital for preparing students to critically address significant societal issues like climate change and pandemics. This study examines the effectiveness of socioscientific system modeling as a tool within Justice-Centered Science Pedagogy (JCSP) to enhance middle school students' understanding of social justice science issues. It focuses on how system modeling can scaffold students' reasoning about complex social systems, informed by their lived experiences, cultural backgrounds, and social identities.
View Article and Find Full Text PDFAgenda-setting for iron deficiency anaemia prevention and control policies in Iran by multiple streams framework.
Health Res Policy Syst
December 2024
Department of Community Nutrition, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: According to the WHO report and evaluations, Iran has a moderate prevalence of iron deficiency anaemia (IDA). The present study evaluates Iran's IDA prevention and control policies by analysing the agenda-setting policy using the multiple streams framework (MSF).
Methods: The present study used Kingdon's MSF model to analyse policies related to IDA in Iran qualitatively.
Targeting peptide antigens using a multiallelic MHC I-binding system.
Nat Biotechnol
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Department of Chemistry, Stanford University, Stanford, CA, USA.
Identifying highly specific T cell receptors (TCRs) or antibodies against epitopic peptides presented by class I major histocompatibility complex (MHC I) proteins remains a bottleneck in the development of targeted therapeutics. Here, we introduce targeted recognition of antigen-MHC complex reporter for MHC I (TRACeR-I), a generalizable platform for targeting peptides on polymorphic HLA-A*, HLA-B* and HLA-C* allotypes while overcoming the cross-reactivity challenges of TCRs. Our TRACeR-MHC I co-crystal structure reveals a unique antigen recognition mechanism, with TRACeR forming extensive contacts across the entire peptide length to confer single-residue specificity at the accessible positions.
View Article and Find Full Text PDFcidalsDB: an AI-empowered platform for anti-pathogen therapeutics research.
J Cheminform
November 2024
Laboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, 13, Place Pasteur, 1002, Tunis, Tunisia.
Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted DD against infectious pathogens, namely Leishmania parasites and Coronaviruses.
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