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Correction: Antioxidant Effect of Naringin Demonstrated Through a Bayes' Theorem Driven Multidisciplinary Approach Reveals its Prophylactic Potential as a Dietary Supplement for Ischemic Stroke

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Molecular neurobiology 2025年第5期62卷 6631-6632页

作者： Manju Babu Rajas M Rao Anju Babu Jenat Pazheparambil Jerom Anaekshi Gogoi Nikhil Singh Meenakshi Seshadri Animikh Ray Bhaskara P Shelley Arnab Datta Laboratory of Translational Neuroscience Division of Neuroscience Yenepoya Research Center Yenepoya (Deemed to Be University) University Road Deralakatte Mangalore 575018 Karnataka India. Division of Data Analytics Bioinformatics and Structural Biology Yenepoya Research Center Yenepoya (Deemed to Be University) University Road Deralakatte Mangalore 575018 Karnataka India. Department of Biological Sciences Indian Institute of Science Education and Research Bhopal 462066 MP India. School of Biosciences Mahatma Gandhi University Kottayam 686560 Kerala India. Department of Pharmacology Yenepoya Pharmacy College and Research Center Naringana Deralakatte Mangalore 575018 Karnataka India. Father Muller Research Center Father Muller Medical College Mangalore 575002 Karnataka India. Department of Neurology Yenepoya Medical College Yenepoya (Deemed to Be University) University Road Deralakatte Mangalore 575018 Karnataka India. Laboratory of Translational Neuroscience Division of Neuroscience Yenepoya Research Center Yenepoya (Deemed to Be University) University Road Deralakatte Mangalore 575018 Karnataka India. arnabdattaju@***. Department of Pharmacology Yenepoya Pharmacy College and Research Center Naringana Deralakatte Mangalore 575018 Karnataka India. arnabdattaju@***.

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Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

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Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

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Benchmarking SILAC Proteomics Workflows and data Analysis Platforms

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Molecular & cellular proteomics : MCP 2025年第6期24卷 100980页

作者： Ashley M Frankenfield Kevin Yang Wan Nur Atiqah Binti Mazli Jamison Shih Fengchao Yu Edwin Lo Alexey I Nesvizhskii Ling Hao Department of Chemistry the George Washington University Washington DC 20052 USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Chemistry & Biochemistry the University of Maryland College Park MD 20742 USA. Department of Pathology University of Michigan Ann Arbor MI USA. Data Science Institute the University of Chicago Chicago IL 60637 USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Department of Chemistry the George Washington University Washington DC 20052 USA Department of Chemistry & Biochemistry the University of Maryland College Park MD 20742 USA. Electronic address: linghao1@umd.edu.

Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful metabolic labeling technique with broad applications and various study designs. SILAC proteomics relies on the accurate identification and quantification of all isotopic versions of proteins and peptides during both data acquisition and analysis. However, a comprehensive comparison and evaluation of SILAC data analysis platforms is currently lacking. To address this critical gap and offer practical guidelines for SILAC proteomics data analysis, we designed a comprehensive benchmarking pipeline to evaluate various in vitro SILAC workflows and commonly used data analysis software. Ten different SILAC data analysis workflows using five software packages (MaxQuant, Proteome Discoverer, FragPipe, DIA-NN, and Spectronaut) were evaluated for static and dynamic SILAC labeling with both DDA and DIA methods. For benchmarking, we used both in-house generated and repository SILAC proteomics datasets from HeLa and neuron culture samples. We assessed twelve performance metrics for SILAC proteomics including identification, quantification, accuracy, precision, reproducibility, filtering criteria, missing values, false discovery rate, protein half-life measurement, data completeness, unique software features, and speed of data analysis. Each method/software has its strengths and weaknesses when evaluated for these performance metrics. Most software reaches a dynamic range limit of 100 folds for accurate quantification of light/heavy ratios. We do not recommend using Proteome Discoverer for SILAC DDA analysis despite its wide use in label-free proteomics. To achieve greater confidence in SILAC quantification, researchers could use more than one software packages to analyze the same dataset for cross-validation. In summary, this study offers the first systematic evaluation of various SILAC data analysis platforms, providing practical guidelines to support decision-making in SILAC proteomics study design and

关键词： DDA DIA protein turnover proteomics data analysis SILAC AGC Automatic gain control DDA data-dependent acquisition DIA data-independent acquisition D-DIA direct-DIA dSILAC dynamic stable isotope labeling by amino acids in cell culture FDP false discovery proportion FDR false discovery rate H-DIA hybrid-DIA iPSC inducible pluripotent stem cells LB-DIA library based-DIA MaxIT maximum injection time MBC match between channels MBR match between runs MS mass spectrometry PD Proteome Discoverer RIA relative isotope abundance SILAC stable isotope labeling by amino acids in cell culture

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A Design of Deep Learning Experimentation for Fruit Freshness Detection

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Febrian Valentino Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow easily in *** have many good nutrients such as vitamins, proteins and others. But the fruit also has a period where the fruit is said to be fresh *** this time there are still many fruit supplier companies that send fruit unfit for consumption due to lack of accuracy in the process of sorting the fruit when the fruit is taken from the plantation and the entry of other fruit into an improper packaging. Thus, it makes detecting food spoilage from the production stage to consumption is very important. We propose a design of computer vision-based technique usingdeep learning with the Convolutional Neural Network (CNN) model to detect fruit freshness. The specially designed CNN model is then evaluated with public datasets of fruits fresh and rotten for classification derived from Kaggle.

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Analysis of machine learning strategies for prediction of passing undergraduate admission test

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International Journal of Information Management data Insights 2022年第2期2卷

作者： Walid, Md. Abul Ala Ahmed, S.M. Masum Zeyad, Mohammad Galib, S. M. Saklain Nesa, Meherun Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU) Gopalganj 8100 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering and Technology (KUET) Khulna 9203 Bangladesh Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram 4226 Bangladesh Faculty of Engineering University of Mons (UMONS) Bd Dolez 31 Mons 7000 Belgium School of Engineering and Physical Sciences (EPS) Heriot-Watt University (HWU) EH14 4AS Scotland Edinburgh United Kingdom Department of Energy Engineering University of the Basque Country (UPV/EHU) Ingeniero Torres Quevedo Plaza 1 Biscay Bilbao 48013 Spain School of Science & Technology International Hellenic University (IHU) 14th km Thessaloniki – N. Moudania Thessaloniki Thermi 57001 Greece Department of Biomedical Engineering Khulna University of Engineering and Technology (KUET) Khulna 9203 Bangladesh

This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model. © 2022 The Author(s)

关键词： Adaboost Balanced dataset Machine Learning Precision Support Vector Machines (SVM)

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Enabling a Massive data Collection for Hotel Receptionist Chatbot Using a Crowdsourcing Information System

Enabling a Massive Data Collection for Hotel Receptionist Ch...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reval Levannoza Rizky Fauzi Latif Syafira Indah Nurkafianti Kristianus Oktriono Devina Chandra Kurniawan Wiharja Tjeng Wawan Cenggoro School of Computer Science Bina Nusantara University Jakarta Indonesia Language Center Tourism Destination Faculty of Humanities Bina Nusantara University Jakarta Indonesia Language Center Industrial Engineering Faculty of Humanities Bina Nusantara University Jakarta Indonesia Faculty of Humanities Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

With the heavy utilization of deep learning, modern chatbot systems require massive training dataset to achieve their peak performance. Without exception, the development of chatbot for the hotel receptionist tasks also demands a massive training dataset. Unfortunately, laborious effort and hefty time are needed to manually collect a dataset with that size. Therefore, an information system is needed to assist the dataset collection. In regards to solve the problem, this research contributes in developing the information system that enables a massive data collection for training a hotel receptionist chatbot in a reasonable time. In detail, the contribution of this research are twofold: (1) developing a realiable crowdsourcing information system design to collect and store the dataset for training hotel receptionist chatbot; and (2) developing a user experience that can stimulate the crowdworkers to provide more data.

关键词： Crowdsourcing Computer science Training Task analysis Natural language processing Information management

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Nuclear Medicine Artificial Intelligence in Action: The Bethesda Report (AI Summit 2024)

arXiv

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arXiv 2024年

作者： Rahmim, Arman Bradshaw, Tyler J. Davidzon, Guido Dutta, Joyita El Fakhri, Georges Ghesani, Munir Karakatsanis, Nicolas A. Li, Quanzheng Liu, Chi Roncali, Emilie Saboury, Babak Yusufaly, Tahir Jha, Abhinav K. Departments of Radiology and Physics University of British Columbia Canada Department of Radiology University of Wisconsin United States Department of Radiology Division of Nuclear Medicine & Molecular Imaging Stanford University United States Department of Biomedical Engineering University of Massachusetts Amherst United States PET Center Departments of Radiology & Biomedical Engineering and Bioinformatics & Data Science Yale University United States United Theranostics Department of Radiology Weill Cornell Medical College Cornell University New York United States Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital Harvard Medical School United States Department of Radiology and Biomedical Imaging Yale University United States Departments of Biomedical Engineering and Radiology University of California Davis United States Department of Radiology and Imaging Sciences Clinical Center National Institutes of Health Russell H. Morgan Department of Radiology and Radiological Sciences Johns Hopkins School of Medicine United States Department of Biomedical Engineering Mallinckrodt Institute of Radiology Washington University St. Louis United States

The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was "AI in Action". Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript. © 2024, CC BY.

关键词： Nuclear medicine

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Earthquake Damage Report Interactive Dashboard Using Bayesian Structural Time Series and Value-Suppressing Uncertainty Palettes

Earthquake Damage Report Interactive Dashboard Using Bayesia...

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IEEE Symposium on Visual Analytics science and Technology (VAST)

作者： Natthawut Adulyanukosol Division of Bioinformatics and Data Management for Research Development and Promotion of Science and Technology Talents Project Institute for the Promotion of Teaching Science and Technology Faculty of Medicine Siriraj Hospital Mahidol University

Earthquake Damage Report Interactive Dashboard addresses Mini Challenge 1 of VAST Challenge 2019. The project uses interactive visual analytics to report and summarize crowd-sourced damage reports temporally and spatially. We modelled the reports with Bayesian structural time series (BSTS) and encoded uncertainties in the reports visually with value-suppressing uncertainty palettes (VSUPs). The interactive visualization was developed with Vega and VueJS. We performed further analyses in Tableau. The dashboard and the challenge submission package can be found at https://***.

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Auto score-survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

arXiv

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arXiv 2021年

作者： Xie, Feng Ning, Yilin Yuan, Han Goldstein, Benjamin Alan Ong, Marcus Eng Hock Liu, Nan Chakraborty, Bibhas Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Statistics and Applied Probability National University of Singapore Singapore Singapore

Background: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method. Methods: AutoScore was previously developed as an interpretable machine learning score generator, integrated both machine learning and point-based scores in the strong discriminability and accessibility. We have further extended it to time-to-event data and developed AutoScore-Survival, for automatically generating time-to-event scores with right-censored survival data. Random survival forest provides an efficient solution for selecting variables, and Cox regression was used for score weighting. We implemented our proposed method as an R package. We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i.e., Cox) and the random survival forest. Results: The AutoScore-Survival-derived scoring model was more parsimonious than survival models built using traditional variable selection methods (e.g., penalized likelihood approach and stepwise variable selection), and its performance was comparable to survival models using the same set of variables. Although AutoScore-Survival achieved a comparable integrated area under the curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores generated are favorable in clinical applications because they are easier to compute and interpret. Conclusions: Our proposed AutoScore-Survival provides an automated, robust and easy-to-use machine learning-based clinical score generator to studies of time-to-event outcomes. It provides a systematic guideline to facilitate the future development of time-to-event scores for clinical applications. © 2021, CC BY.

关键词： Intensive care units

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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

arXiv

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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