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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

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Journal of Proteomics 2025年 317卷

作者： Zheng, Ping Audain, Enrique Webel, Henry Dai, Chengxin Klein, Joshua Hitz, Marc-Phillip Sachsenberg, Timo Bai, Mingze Perez-Riverol, Yasset Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Institute of Medical Genetics University Medicine Oldenburg Carl von Ossietzky University Oldenburg Germany The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Life Omics Beijing China Program for Bioinformatics Boston University Boston United States Department of Computer Science Applied Bioinformatics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Cambridge United Kingdom

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://***/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. Ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpy's capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4921 samples and 5766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse. Significance: Proteomics studies often rely on intensity-based absolute quantification (iBAQ) to assess protein abundance across various biological conditions. Despite its widespread use, computing iBAQ values at scale remains challenging due to the increasing complexity and volume of proteomics experiments. Existing tools frequently lack metadata integration, limiting their ability to handle experimental design intricacies such as replicates, fractions, and batch effects. Our work introduces ibaqpy, a scalable Python package that lev

关键词： Big data bioinformatics Data integration Proteomics Quantification

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A scalable approach for alignments in fuzzy biological networks

A scalable approach for alignments in fuzzy biological netwo...

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2010 International Conference on bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010

作者： Liu, Yunkai Verone, Alissa Bioinformatics Program Computer and Information Science Department Gannon University United States

ISBN: (纸本)9781617820694

Interactions between biological entities, whether cells, proteins or genes are under meticulous study. As high throughput calculations have made biological network a popular target for study, methods for comparison have not been mastered. Multiple classes of bionetwork models exist, such as metabolic, protein-gene, or protein-protein interactions;the latter being of most importance to our research. Several different computational approaches have been taken by introducing bionetworks as tools to evaluate genomic or biological data. The primary focus was to develop a new method for comparison of bionetworks with great amount of noises. In this paper, an algorithm was designed for the pairwise comparison of bionetworks, considering the most conserved and most variable areas. Copyright© (2010) by the International Society for Research in science and Technology.

关键词： Genes

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How to build a system that manages bioinformatics content

How to build a system that manages bioinformatics content

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2007 International Conference on Enterprise Information Systems and Web Technologies, EISWT 2007

作者： Phan, Vinhthuy Thomas, Allen Department of Computer Science Bioinformatics Program University of Memphis TN 38152 United States

ISBN: (纸本)9781615677245

We discuss how to build systems with sophisticated features based on a core set of bioinformatics tools. Beside from being the front-end server to the core analyses, such a system can manage users, bioinformatics content and shared data. Using tools available in the open-source community, we show how to build a system with such sophisticated features with a relatively minimal effort, especially in comparison to building such a system from scratch. We discuss the notion of Model-Content Management-View, under whose framework similar applications with such desirable features beyond the field bioinformatics can be built. Our system can be viewed at http://***/rnamotif.

关键词： bioinformatics

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Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data (vol 21, bby097, 2018)

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BRIEFINGS IN bioinformatics 2020年第3期21卷 1118-1118页

作者： Lim, Sangsoo Lee, Sangseon Jung, Inuk Rhee, Sungmin Kim, Sun Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Corresponding author: Sun Kim Interdisciplinary Program in Bioinformatics Department of Computer Science and Engineering and Bioinformatics Institute Seoul National University Seoul 08826 Korea. Tel.: +82-2-880-7280 Fax: +82-2-888-9623 Email: sunkim.bioinfo@snu.ac.kr

Motivation Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or *** In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.

关键词： pathway analysis individualized pathway activity pathway activity inference pan-cancer analysis

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Pursuit-evasion Voronoi diagrams in 1

Pursuit-evasion Voronoi diagrams in 1

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4th International Symposium on Voronoi Diagrams in science and Engineering 2007, ISVD 2007

作者： Cheung, Warren Evans, William Department of Computer Science University of British Columbia University of British Columbia Bioinformatics Program

ISBN: (纸本)0769528694

We are given m pursuers and one evader. Each pursuer and the evader has an associated starting point in the plane, a maximum speed, and a start time. We also have a set of line segment obstacles with a total of n endpoints. Our task is to find those points in the plane, called the evader's region, that the evader can reach via evasive paths. A path is evasive if the evader can traverse the path from its starting point without encountering a pursuer along the way. The evader and the pursuers must obey their start time and speed constraints, and cannot go through obstacles. The partition of the plane into the evader's region and the remaining pursuers' region is called the pursuit-evasion Voronoi diagram. We study pursuit-evasion Voronoi diagrams for the 1 metric. We show that the complexity of the diagram is O((n + m)2(mn + m)) and that it can be calculated in polynomial time. © 2007 IEEE.

关键词： Graphic methods

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： computer vision

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Mapping dihydropteroate synthase evolvability through identification of a novel evolutionarily critical substructure

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International Journal of Biological Macromolecules 2025年 311卷 143325页

作者： Sanyal, Dwipanjan Shivram, A. Pandey, Deeptanshu Banerjee, Suharto Uversky, Vladimir N. Muzata, Danny Chivukula, Aneesh Sreevallabh Jasuja, Ravi Chattopadhyay, Krishnananda Chowdhury, Sourav Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology Kolkata India Department of Computer Science and Information Systems Birla Institute of Technology and Science-Pilani Hyderabad India Department of Biological Sciences Birla Institute of Technology and Science-Pilani Hyderabad India Max Delbrück Center for Molecular Medicine Berlin Germany Research Program in Men's Health: Aging and Metabolism Brigham and Women's Hospital Harvard Medical School BostonMA United States USF Health Byrd Alzheimer's Research Institute Morsani College of Medicine University of South Florida TampaFL33612 United States

Protein evolution shapes pathogen adaptation-landscape, particularly in developing drug resistance. The rapid evolution of target proteins under antibiotic pressure leads to escape mutations, resulting in antibiotic resistance. A deep understanding of the evolutionary dynamics of antibiotic target proteins presents a plausible intervention strategy for disrupting the resistance trajectory. Mutations in Dihydropteroate synthase (DHPS), an essential folate pathway protein and sulfonamide antibiotic target, reduce antibiotic binding leading to anti-folate resistance. Deploying statistical analyses on the DHPS sequence-space and integrating deep mutational analysis with structure-based network-topology models, we identified critical DHPS subsequences. Our frustration landscape analysis suggests how conformational and mutational changes redistribute energy within DHPS substructures. We present an epistasis-based fitness prediction model that simulates DHPS adaptive walks, identifying residue positions that shape evolutionary constraints. Our optimality analysis revealed a substructure central to DHPS evolvability, and we assessed its druggability. Combining evolution and structure, this integrated framework identifies a DHPS substructure with significant evolutionary and structural impact. Targeting this region may constrain DHPS evolvability and slow resistance emergence, offering new directions for antibiotic development. © 2025

关键词： Topology

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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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bioinformatics in the undergraduate curriculum: Opportunities for computer science educators

Bioinformatics in the undergraduate curriculum: Opportunitie...

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Proceedings of the Thirty-Fifth SIGCSE Technical Symposium on computer science Education

作者： Burhans, Debra T. DeJongh, Matt Doom, Travis E. LeBlanc, Mark Computer Science Department Bioinformatics Program Canisius College Buffalo NY Computer Science Department Hope College Holland MI Computer Science Department BioMedical Science Program Wright State University Dayton OH Department of Math Wheaton College Norton MA

ISBN: (纸本)1581137982

Biology has become an increasingly data-driven science. Modern experimental techniques, including automated DNA sequencing, gene expression micro arrays, and X-ray crystallography are producing molecular data at a rate that has made traditional data analysis methods impractical. Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. bioinformatics is the term coined for the new field that merges biology and computer science to manage and analyze this data, with the ultimate goal of understanding and modeling living systems. The emergence of bioinformatics provides new challenges and opportunities for computer science educators. This panel assembles four individuals who collectively have experience teaching bioinformatics at both liberal arts colleges and universities, and who also have industry experience in bioinformatics, to discuss various approaches to incorporating bioinformatics into the undergraduate curriculum.

关键词： computer science

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