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rcsb-api: Python Toolkit for Streamlining Access to RCSB Protein data Bank APIs: rcsb-api: Python Toolkit to Access *** APIs

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Journal of Molecular Biology 2025年 168970页

作者： Piehl, Dennis W. Vallat, Brinda Truong, Ivana Morsy, Habiba Bhatt, Rusham Blaumann, Santiago Biswas, Pratyoy Rose, Yana Bittrich, Sebastian Duarte, Jose M. Segura, Joan Bi, Chunxiao Myers-Turnbull, Douglas Hudson, Brian P. Zardecki, Christine Burley, Stephen K. Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States

The Protein data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein data Bank *** research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support *** users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (***/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences. © 2025 The Author(s)

关键词： bioinformatics FAIR principles RCSB Protein data Bank *** structural biology

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

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Journal of Molecular Biology 2025年 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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Protecting centrosomes from fracturing enables efficient cell navigation

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science Advances 2025年第17期11卷

作者： Schmitt, Madeleine T. Kroll, Janina Ruiz-Fernandez, Mauricio J.A. Hauschild, Robert Ghosh, Shaunak Kameritsch, Petra Merrin, Jack Schmid, Johanna Stefanowski, Kasia Thomae, Andreas W. Cheng, Jingyuan Öztan, Gamze Naz Konopka, Peter Ortega, Germán Camargo Penz, Thomas Bach, Luisa Baumjohann, Dirk Bock, Christoph Straub, Tobias Meissner, Felix Kiermaier, Eva Renkawitz, Jörg Biomedical Center Walter Brendel Center of Experimental Medicine Institute of Cardiovascular Physiology and Pathophysiology Klinikum der Universität Ludwig Maximilians Universität München Munich Germany Institute of Science and Technology Austria Klosterneuburg Austria Institute Immune and Tumor Biology University of Bonn Bonn Germany Bioimaging Facility Biomedical Center Faculty of Medicine Ludwig Maximilians Universität München Munich Germany Institute of Innate Immunity Department of Systems Immunology and Proteomics Medical Faculty University of Bonn Bonn Germany Institute of Stem Cell Research Helmholtz Center Munich German Research Center for Environmental Health Munich Germany Physiological Genomics Biomedical Center Ludwig-Maximilians University Munich Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Medical Clinic III for Oncology Hematology Immuno-Oncology and Rheumatology University Hospital Bonn University of Bonn Bonn Germany Medical University of Vienna Institute of Artificial Intelligence Center for Medical Data Science Vienna Austria Bioinformatics Unit Biomedical Center Faculty of Medicine Ludwig Maximilians Universität München Munich Germany

The centrosome is a microtubule orchestrator, nucleating and anchoring microtubules that grow radially and exert forces on cargos. At the same time, mechanical stresses from the microenvironment and cellular shape changes compress and bend microtubules. Yet, centrosomes are membraneless organelles, raising the question of how centrosomes withstand mechanical forces. Here, we discover that centrosomes can deform and even fracture. We reveal that centrosomes experience deformations during navigational pathfinding within motile cells. Coherence of the centrosome is maintained by Dyrk3 and cNAP1, preventing fracturing by forces. While cells can compensate for the depletion of centriolar-based centrosomes, the fracturing of centrosomes impedes cellular function by generating coexisting microtubule organizing centers that compete during path navigation and thereby cause cellular entanglement in the microenvironment. Our findings show that cells actively maintain the integrity of the centrosome to withstand mechanical forces. These results suggest that centrosome stability preservation is fundamental, given that almost all cells in multicellular organisms experience forces. © 2025 The Authors, some rights reserved;exclusive licensee American Association for the Advancement of science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

关键词： Cell culture

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Parallel genome-wide analysis with central and graphic processing units

Parallel genome-wide analysis with central and graphic proce...

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IEEE International Conference on Computer and Communications, ICCC 2015

作者： Kacamarga, Muhamad Fitra Baurley, James W. Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781467381253

The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the MapReduce and Compute Unified Device Architecture (CUDA) frameworks and evaluate performance (speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data (1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620 (6 cores) and NVIDIA Tesla K20 over sequential processing. © 2015 IEEE.

关键词： MapReduce

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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

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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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