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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

ACS nano 2025年第23期19卷 2025 Jun 3页

作者： Joseph Cave Anne Christiono Carmine Schiavone Henry J Pownall Vittorio Cristini Daniela I Staquicini Renata Pasqualini Wadih Arap C Jeffrey Brinker Matthew Campen Zhihui Wang Hien Van Nguyen Achraf Noureddine Prashant Dogra Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston Texas 77030 United States. Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York New York 10065 United States. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 United States. Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy. Department of Medicine Houston Methodist Houston Texas 77030 United States. Department of Medicine Weill Cornell Medicine New York New York 10065 United States. Neal Cancer Center Houston Methodist Research Institute Houston Texas 77030 United States. Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston Texas 77030 United States. Rutgers Cancer Institute Newark New Jersey 08901 United States. Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Department of Chemical and Biological Engineering University of New Mexico Albuquerque New Mexico 87106 United States. College of Pharmacy University of New Mexico Albuquerque New Mexico 87106 United States. Department of Physiology and Biophysics Weill Cornell Medical College New York New York 10065 United States. Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 United States.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for settings. To enable organ-specific toxicity predictions , we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

关键词： PBPK artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity

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IoT-Based Architecture for Data Analytics of Arboviruses in Smart Cities

IoT-Based Architecture for Data Analytics of Arboviruses in ...

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IEEE Symposium on computers and Communications

作者： Priscylla da S. Tavares Emanuel B. Rodrigues Group of Computer Networks Software Engineering and Systems (GREat) Graduate Program in Computer Science (MDCC) Federal University of Ceará (UFC) Fortaleza Brazil

With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.

关键词： Internet of Things Data analytics Health care Smart city

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Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

arXiv

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arXiv 2021年

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Department of Radiology School of Medicine University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States College of Computer National University of Defense Technology Changsha410073 China Department of Bioengineering Swanson School of Engineering University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Department of Radiology University of Pittsburgh University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh 4401 Penn Ave PittsburghPA15224 United States Magee-Womens Hospital University of Pittsburgh Medical Center 300 Halket St PittsburghPA15213 United States Department of Biomedical Informatics University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Intelligent Systems Program University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States

Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model’s performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional "middle" classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. © 2021, CC BY.

关键词： Image classification

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Proposal of an integrated decision support system for Tuberculosis based on Semantic Web

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Procedia computer Science 2019年 164卷 552-558页

作者： Vinícius Lima Felipe Pellison Filipe Bernardi Isabelle Carvalho Rui Rijo Domingos Alves Bioengineering Postgraduate Program University of Sao Paulo Sao Carlos Brazil Institute of Mathematical and Computer Sciences University of Sao Paulo Sao Carlos Brazil School of Technology and Management Polytechnic Institute of Leiria Leiria Portugal Centre for Research in Health Technologies and Information Systems (CINTESIS) University of Porto Porto Portugal Institute for Systems and Computers Engineering at Coimbra (INESCC) Coimbra Portugal Ribeirão Preto Medical School University of Sao Paulo Ribeirao Preto Brazil

Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models.

关键词： Tuberculosis Decision Support System Health Information System Health Information Management Semantic Web

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

引用

arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

PurposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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Democratising Knowledge Representation with BioCypher

arXiv

引用

arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Odyssey process reuse: A component-based software process line approach 20

Odyssey process reuse: A component-based software process li...

引用

20th International Conference on Enterprise Information systems, ICEIS 2018

作者： Teixeira, Eldânae Nogueira Vasconcelos, Aline Werner, Cláudia Systems Engineering and Computer Science Program COPPE/UFRJ ZIP Code: 21945-970 P.O. Box 68511 Rio de Janeiro RJ Brazil Federal Fluminense Institute of Education Science and Technology Campos dos Goytacazes Goytacazes RJ Brazil

ISBN: (纸本)9789897582981

It is expected that managing process variations and organizing process domain knowledge in a reusable way can provide support to handle complexity in software process definition. In this context, the purpose of this paper is to describe a systematic software process reuse methodology, by combining process reuse techniques, such as Software Process Line and Component Based Process Definition, aiming to increase reuse possibilities. SPrL approach manages the variability aspect inherent to software process domain and CBPD focuses on modularizing the domain process information into process components. The proposed SPrL modelling metamodel and notation address reusable process elements, explicitly representing the variability concept in both process domain structure and behaviour. Based on the results of the evaluation studies, it was possible to get evidences of the approach feasibility, with a higher expressiveness when using the process variability notation proposed, which allow that more semantic concepts inherent to SPrL scenarios can be graphically described. Also, the set of heuristics to support mappings among artefacts in distinct abstraction levels was considered useful to keep the traceability of variability properties, relationships and restrictions. Further research is being conducted to explore ways to support project managers during the decision-making in new software process definitions. © 2018 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Decision making

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Edy Sulistiyo Vindy Nizirwan Anwar Diana Teresia Spits Warnars Setya Chendra Wibawa Goenawan Brotosaputro Dedy Prasetya Kristiadi Harco Leslie Hendric Spits Warnars Kiyota Hashimoto Electrical Engineering Department Universitas Negeri Surabaya Jawa Timur Indonesia Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department Bina Nusantara University Jakarta Indonesia Informatics Department Universitas Negeri Surabaya Jawa Timur Indonesia Information System Department Universitas Budi Luhur Jakarta Indonesia Computer Science department Raharja University Tangerang Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince Songkla University Phuket Campus Thailand

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

关键词： Games Land mobile radio Education computer science Operating systems Information systems Media

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