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IEEE Potentials 2004年第1期23卷 24-27页

作者： Doom, Travis Raymer, Michael Krane, Dan IEEE Wright State University United States Department of Computer Science and Engineering Graduate Faculty in Biomedical Sciences Dayton OH United States WSU Biomedical Sciences Ph.D. Program Forensic Bioinformatics Inc. United States Bioinformatics Research Group Computer Science Department Wright State University United States

No abstract available

关键词： bioinformatics Biology computing Biological information theory Data analysis Computational biology Proteins Biochemistry Sequences Genomics Large-scale systems

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Spectral characteristics of microcirculatory signals response to muscle stretching among diabetic smokers 17

Spectral characteristics of microcirculatory signals respons...

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4th International Conference on biomedical and bioinformatics engineering, ICBBE 2017

作者： Bau, Jian-Guo Lin, Yue-Der Huang, Bo-Wen Department of Biomedical Engineering Hungkuang University Taichung43302 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master's Program of Bioinformatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9781450354844

Cigarette smoking is reported to be the major risk factor for endothelial dysfunction. The variations of the spectral compositions of microcirculatory perfusion signal could provide the information related to the regulatory function of microvasculature. The aim of the present study was to compare the spectral characteristics response to muscle stretching between diabetic smokers and non-smokers, and investigate the influence of cigarette smoking on microvascular function. Twenty four diabetic patients (12 smokers v.s. 12 non-smokers) and twelve normal non-smoking subjects were recruited. We observed that the diabetic smokers had a significantly reduced endothelial activity. The mechanisms for the reduction of the endothelial activity remain to be elucidated. The spectral analysis might be used to evaluate endothelial function for research, for diagnostic purposes, and maybe also to assess effects of therapy in cardiovascular diseases. © 2017 Association for Computing Machinery.

关键词： Spectrum analysis

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Comparative analysis of gene regulation in single cells using Compass

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Cell Reports Methods 2025年第5期5卷 101035页

作者： Wan, Changxin Qu, Yilong Ye, Zhiyou Zhang, Tianbei Ma, Huifang Chen, Ming Hou, Wenpin Ji, Zhicheng Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC United States Program of Computational Biology and Bioinformatics Duke University School of Medicine Durham NC United States Department of Biomedical Engineering Pratt School of Engineering Duke University Durham NC United States Department of Pathology Duke University School of Medicine Durham NC United States Duke Cancer Institute Duke University Durham NC United States Department of Biostatistics Columbia University Mailman School of Public Health New York City NY United States

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples. © 2025 The Author(s)

关键词： cis-regulatory elements CP: Systems biology gene regulation single-cell ATAC-seq single-cell multi-omics

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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A novel label-free electrochemical immunosensor for the detection of heat shock protein 70 of lung adenocarcinoma cell line following paclitaxel treatment using l-cysteine-functionalized Au@MnO2/MoO3 nanocomposites

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RSC Advances 2023年第43期13卷 29847-29861页

作者： Chen, Yi-An Shie, Ming-You Ho, Chia-Che Ye, Sheng-Wen Chen, I.-Wen Peter Shih, Yu-Yin Shen, Yu-Fang Chen, Yi-Wen x-Dimension Center for Medical Research and Translation China Medical University Hospital Taichung City404332 Taiwan The Master Program for Biomedical Engineering China Medical University Taichung City406040 Taiwan Department of Biomedical Engineering China Medical University Taichung City40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung City41354 Taiwan Department of Chemistry National Cheng Kung University Tainan70101 Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung City40447 Taiwan

The future trend in achieving precision medicine involves the development of non-invasive cancer biomarker sensors that offer high accuracy, low cost, and time-saving benefits for risk clarification, early detection, disease detection, and therapeutic monitoring. A facile approach for the synthesis of MoO3 nanosheets was developed by thermally oxidizing MoS2 nanosheets in air followed by thermal annealing. Subsequently, Au@MnO2 nanocomposites were prepared using a combined hydrothermal process and in situ chemical synthesis. In this study, we present a novel immunosensor design strategy involving the immobilization of antiHSP70 antibodies on Au@MnO2/MoO3 nanocomposites modified on a screen-printed electrode (SPE) using EDC/NHS chemistry. This study establishes HSP70 as a potential biomarker for monitoring therapeutic response during anticancer therapy. Impedance measurements of HSP70 on the Au@MnO2/MoO3/SPE immunosensor using EIS showed an increase in impedance with an increase in HSP70 concentration. The electrochemical immunosensor demonstrated a good linear response in the range of 0.001 to 1000 ng mL−1 with a detection limit of 0.17 pg mL−1 under optimal conditions. Moreover, the immunosensor was effective in detecting HSP70 at low concentrations in a lung adenocarcinoma cell line following Paclitaxel treatment, indicating its potential for early detection of the HSP70 biomarker in organ-on-a-chip and clinical applications. © 2023 The Royal Society of Chemistry.

关键词： Nanosheets

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Circuits of the cell

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Drug Discovery Today: BIOSILICO 2004年第3期2卷 120-126页

作者： Mellor, Joseph C. Delisi, Charles Bioinformatics Graduate Program Boston University Boston MA 02215 44 Cummington Street United States Department of Biomedical Engineering Boston University Boston MA 02215 44 Cummington Street United States

Networks of gene regulation control many aspects of cellular biology, ranging from development and differentiation to the cell cycle and apoptosis. However, the precise control of genetic regulation is poorly understood for whole cells and large systems. Technical achievements in the genome-wide measurement of DNA, mRNA and proteins - and their assorted interactions - have enabled systematic approaches to regulation in molecular biology, which promise to shed new light on the complex architecture of gene regulatory networks. Detailed knowledge of such systems is crucial for our understanding of molecular phenotypes;similarly, our ability to treat and cure many diseases will depend on our understanding of molecular disease at its regulatory and mechanistic roots.

关键词： bioinformatics biological networks genetic networks interactions regulatory models

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A virtual positioning system for external beam radiotherapy

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Virtual Reality 2023年第3期27卷 2569-2582页

作者： Sung, Wen-Hsu Jiang, Ching-Fen Su, Tai-Sin Sun, Shuh-Ping Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung Taiwan Department of Digital Media Design I-Shou University Kaohsiung Taiwan

In radiation therapy, positioning patients to ensure the accuracy of the setup of a procedure is a routine and labour-intensive process that substantially determines the efficacy of treatment. In this study, we propose a virtual positioning system that can simulate the positioning process with a visible beam path under the broad view of a life-like patient-positioning platform to obviate problems with excessively narrow view. This system integrates image processing, computer graphics, and virtual reality to encompass a 3D treatment target reconstructed from medical images of different modalities in a virtual scene. An innovative evaluation method is further proposed to verify the efficacy of the system using a full-scale 3D solid anthropometric model, and a treatment target showed an accuracy of 99% in calibration and a mean compatibility of 95% with an actual measurement. The proposed virtual positioning system provides a training and education platform for radiation therapy. The methodology through which the system was developed is also disclosed as a promising approach to improve the efficiency and safety of the position verification process for existing systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Radiotherapy

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Hybrid modelling in biochemical systems theory by means of functional petri nets

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Journal of bioinformatics and Computational Biology 2009年第1期7卷 107-134页

作者： Wu, Jialing Voit, Eberhard Department of Mathematics Bioinformatics Program Georgia Institute of Technology Atlanta GA 30332 United States Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 United States

Many biological systems are genuinely hybrids consisting of interacting discrete and continuous components and processes that often operate at different time scales. It is therefore desirable to create modeling frameworks capable of combining differently structured processes and permitting their analysis over multiple time horizons. During the past 40 years, Biochemical Systems Theory (BST) has been a very successful approach to elucidating metabolic, gene regulatory, and signaling systems. However, its foundation in ordinary differential equations has precluded BST from directly addressing problems containing switches, delays, and stochastic effects. In this study, we extend BST to hybrid modeling within the framework of Hybrid Functional Petri Nets (HFPN). First, we show how the canonical GMA and S-system models in BST can be directly implemented in a standard Petri Net framework. In a second step we demonstrate how to account for different types of time delays as well as for discrete, stochastic, and switching effects. Using representative test cases, we validate the hybrid modeling approach through comparative analyses and simulations with other approaches and highlight the feasibility, quality, and efficiency of the hybrid method. © 2009 Imperial College Press.

关键词： Biochemical systems theory BST Delay GMA system HFPN Hybrid Functional Petri Nets Hybrid modeling S-system Stochastic modeling

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Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli

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Nature Communications 2025年第1期16卷 1-16页

作者： Lally, Patrick Gómez-Romero, Laura Tierrafría, Víctor H. Aquino, Patricia Rioualen, Claire Zhang, Xiaoman Kim, Sunyoung Baniulyte, Gabriele Plitnick, Jonathan Smith, Carol Babu, Mohan Collado-Vides, Julio Wade, Joseph T. Galagan, James E. Department of Biomedical Engineering Boston University 44 Cummington Mall Boston MA United States Instituto Nacional de Medicina Genómica Periférico Sur 4809 Arenal Tepepan Ciudad de México México Mexico Escuela de Medicina y Ciencias de la Salud Tecnológico de Monterrey Ciudad de México México Mexico Centro de Ciencias Genómicas Universidad Nacional Autónoma de México Avenida Universidad s/n Cuernavaca Morelos Mexico Department of Biochemistry University of Regina Regina Saskatchewan SK Canada Wadsworth Center New York State Department of Health Albany NY United States Centre for Genomic Regulation (CRG) The Barcelona Institute of Science and Technology Dr. Aiguader 88 Barcelona 08003 Universitat Pompeu Fabra (UPF) Barcelona Spain Department of Biomedical Sciences University at Albany SUNY Albany NY United States Bioinformatics Program Boston University 24 Cummington Mall Boston MA United States

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We use these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We use BoltzNet to quantitatively design novel binding sites, which we validate with biophysical experiments on purified protein. We generate models for 124 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks. © The Author(s) 2025.

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A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps to Reliable Predictions and New Knowledge

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Program in Bioinformatics Department of Computer Science Boston University United States

AI Safety has become a vital front-line concern of many scientists within and outside the AI community. There are many immediate and long term anticipated risks that range from existential risk to human existence to deep fakes and bias in machine learning systems [1-5]. In this paper, we reduce the full scope and immense complexity of AI safety concerns to a trilogy of three important but tractable opportunities for advances that have the short-term potential to improve AI safety and reliability without reducing AI innovation in critical domains. In this perspective, we discuss this vision based on several case studies that already produced proofs of concept in critical ML applications in biomedical science. © 2024, CC BY-NC-ND.

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