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Federated Learning in Healthcare:A Benchmark Comparison of Engineering and Statistical Approaches for Structured data Analysis

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Health data science 2024年第1期4卷 45-57页

作者： Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu Centre for Quantitative Medicine Duke-NUS Medical SchoolSingaporeSingapore Department of Biostatistics and Bioinformatics Duke UniversityDurhamNCUSA Department of Biostatistics Columbia University Mailman School of Public HealthNew YorkNYUSA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingaporeSingapore Programme in Health Services and Systems Research Duke-NUS Medical SchoolSingaporeSingapore Health Services Research Centre Singapore Health ServicesSingaporeSingapore Department of Emergency Medicine Singapore General HospitalSingaporeSingapore Department of Computing Imperial College LondonLondonEnglandUK Institute of Data Science National University of SingaporeSingaporeSingapore

Background:Federated learning(FL)holds promise for safeguarding data privacy in healthcare *** the term“FL”was originally coined by the engineering community,the statistical field has also developed privacy-preserving algorithms,though these are less *** goal was to bridge this gap with the ffrst comprehensive comparison of FL frameworks from both ***:We assessed 7 FL frameworks,encompassing both engineering-based and statistical FL algorithms,and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator(Lasso).Our evaluation utilized both simulated data and real-world emergency department data,focusing on comparing both estimated model coefffcients and the performance of model ***:The ffndings reveal that statistical FL algorithms produce much less biased estimates of model ***,engineering-based methods can yield models with slightly better prediction performance,occasionally outperforming both centralized and statistical FL ***:This study underscores the relative strengths and weaknesses of both types of methods,providing recommendations for their selection based on distinct study ***,we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.

关键词： holds utilized slightly

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Improved Prediction Of Ligand-Protein Binding Affinities By Meta-Modeling

arXiv

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arXiv 2023年

作者： Lee, Ho-Joon Emani, Prashant S. Gerstein, Mark B. Dept. of Genetics and Yale University New HavenCT06510 United States Yale Center for Genome Analysis Yale University New HavenCT06510 United States Dept. of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Dept. of Computer Science Yale University New HavenCT06520 United States Dept. of Statistics & Data Science Yale University New HavenCT06520 United States Dept. of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. © 2023, CC BY-NC-SA.

关键词： Prediction models

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GISAID’s Role in Pandemic Response

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China CDC weekly 2021年第49期3卷 1049-1051页

作者： Shruti Khare Céline Gurry Lucas Freitas Mark B Schultz Gunter Bach Amadou Diallo Nancy Akite Joses Ho Raphael TC Lee Winston Yeo GISAID Core Curation Team Sebastian Maurer-Stroh GISAID Global Data Science Initiative(GISAID) MunichGermany Bioinformatics Institute Agency for Science Technology and ResearchSingapore Oswaldo Cruz Foundation(FIOCRUZ) Rio de JaneiroBrazil Institut Pasteur de Dakar DakarSenegal National Institutes of Biotechnology Malaysia SelangorMalaysia Smorodintsev Research Institute of Influenza St.PetersburgRussia Genome Institute of Singapore Agency for Science Technology and ResearchSingapore China National GeneBank ShenzhenChina A*STAR Infectious Disease Labs(ID Labs) Singapore National Public Health Laboratory National Centre for Infectious DiseasesMinistry of HealthSingapore Department of Biological Sciences National University of SingaporeSingapore.

GISAID is a global data science initiative and the primary source of genomic and associated metadata of all influenza viruses,Respiratory Syncytial Virus(RSV)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pandemic coronavirus causing coronavirus disease 2019(COVID-19).GISAID’s publicly accessible data sharing platform enables collaboration of over 42,000 participating researchers from 198 nations and data generators from over 3,500 institutions across the *** the first wholegenome sequences were made available by China CDC through GISAID on January 10.

关键词： acute respiratory AID

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Exploring causal effects of hormone- and radio-treatments in an observational study of breast cancer using copula-based semi-competing risks models

arXiv

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arXiv 2024年

作者： Yu, Tonghui Peng, Mengjiao Cui, Yifan Chen, Elynn Chen, Chixiang School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Statistics East China Normal University China Center for Data Science Zhejiang University China Stern School of Business New York University New YorkNY United States Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland School of Medicine BaltimoreMD United States Department of Neurosurgery University of Maryland School of Medicine BaltimoreMD United States

Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast cancer and necessitates advanced statistical tools for unbiased analysis. Despite progress in estimation and inference within semi-competing risks regression, its application to causal inference is still in its early stages. This article aims to propose a frequentist and semi-parametric framework based on copula models that can facilitate valid causal inference, net quantity estimation and interpretation, and sensitivity analysis for unmeasured factors under right-censored semi-competing risks data. We also propose novel procedures to enhance parameter estimation and its applicability in real practice. After that, we apply the proposed framework to a breast cancer study and detect the time-varying causal effects of hormone- and radio-treatments on patients' relapse-free survival and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference. © 2024, CC BY-NC-ND.

关键词： Sensitivity analysis

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Scheme and Construction of a Smart Vacuum Cleaner Robot

Scheme and Construction of a Smart Vacuum Cleaner Robot

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Md. Ibrahim Hasan Mohammad Zeyad S.M. Masum Ahmed Md. Sadik Tasrif Anubhove Eftakhar Hossain Sayeed Hasan Dewan Mahnaaz Mahmud Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities School of Engineering and Physical Sciences (EPS) Heriot-Watt University Edinburgh United Kingdom Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering University of Mons (UMONS) Mons Belgium

A smart vacuum robot is a remarkable technological achievement that makes cleaning more efficient, faster, and easier. According to this study, the smart vacuum cleaning robot can be operated manually, automatically, mopped/swept, vacuumed/vacuumed, and observed from 360°. A design of a smart vacuum-cleaning robot is proposed with different characteristics. The Arduino IDE and Proteus Design Suite 8 Professional software were employed for analytical analysis, with a microcontroller serving as the primary control component. The hardware implementation comprises a microcontroller, ultrasonic sensor, and light-dependent resistor (LDR) where the ultrasonic sensor identifies obstacles (trash). Therefore, the LDR emits light and sends the microcontroller's signal to the microprocessor. All the sensors in the proposed system operated flawlessly during the experiment. Moreover, all the sensors were performing perfectly. With additional features of this robot, it can continue its operation during darkness. The proposed robot is equipped with a number of creative and intelligent features that allow it to provide the maximum possible support to employees, housewives, and ordinary people. This work presents a new notion of an advanced cleaning robot employing cutting-edge technology that can work independently in automated and regular situations and dramatically improve the cleaning system for humanity.

关键词： Resistors Humanities Microcontrollers Vacuum technology Robot sensing systems Cleaning Acoustics

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DEPS: AN IMPROVED DEEP LEARNING MODEL FOR DE NOVO PEPTIDE SEQUENCING

arXiv

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arXiv 2022年

作者： Ge, Cheng Lu, Yi Qu, Jia Xie, Liangxu Wang, Feng Zhang, Hong Kong, Ren Chang, Shan Institute of Bioinformatics and Medical Engineering School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou University Changzhou213164 China School of Mathematics China University of Mining and Technology Xuzhou221116 China

De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross-species dataset. © 2022, CC BY.

关键词： Amino acids

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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Tree edit distance with variables. Measuring the similarity between mathematical formulas

arXiv

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arXiv 2021年

作者： Akutsu, Tatsuya Mori, Tomoya Nakamura, Naotoshi Kozawa, Satoshi Ueno, Yuhei Sato, Thomas N. Bioinformatics Center Institute for Chemical Research Kyoto University Karydo TherapeutiX Inc. Center for Mathematical Modeling and Data Science Osaka University V-iCliniX Laboratory Nara Medical University

In this article, we propose tree edit distance with variables, which is an extension of the tree edit distance to handle trees with variables and has a potential application to measuring the similarity between mathematical formulas, especially, those appearing in mathematical models of biological systems. We analyze the computational complexities of several variants of this new model. In particular, we show that the problem is NP-complete for ordered trees. We also show for unordered trees that the problem of deciding whether or not the distance is 0 is graph isomorphism complete but can be solved in polynomial time if the maximum outdegree of input trees is bounded by a constant. This distance model is then extended for measuring the difference/similarity between two systems of differential equations, for which results of preliminary computational experiments using biological models are provided. © 2021, CC BY.

关键词： Polynomial approximation

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Bridging data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical data Analysis

arXiv

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arXiv 2024年

作者： Li, Siqi Li, Xin Yu, Kunyu Miao, Di Zhu, Mingcheng Yan, Mengying Ke, Yuhe D'Agostino, Danny Ning, Yilin Wu, Qiming Wang, Ziwen Shang, Yuqing Liu, Molei Hong, Chuan Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Anesthesiology Singapore General Hospital Singapore Singapore Department of Biostatistics Columbia University Mailman School of Public Health New YorkNY United States Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts. Transfer learning (TL), a machine learning technique, emerges as a powerful solution by utilizing knowledge from pre-trained models to enhance the performance of new models, offering promise across various healthcare domains. Despite its conceptual origins in the 1990s, the application of TL in medical research has remained limited, especially beyond image analysis. In our review of TL applications in structured clinical and biomedical data, we screened 3,515 papers, with 55 meeting the inclusion criteria. Among these, only 2% (one out of 55) utilized external studies, and 7% (four out of 55) addressed scenarios involving multi-site collaborations with privacy constraints. To achieve actionable TL with structured medical data while addressing regional disparities, inequality, and privacy constraints in healthcare research, we advocate for the careful identification of appropriate source data and models, the selection of suitable TL frameworks, and the validation of TL models with proper baselines. © 2024, CC BY.

关键词： Learning systems

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