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arXiv 2022年

作者： Liu, Mingxuan Li, Siqi Yuan, Han Ong, Marcus Eng Hock Ning, Yilin Xie, Feng Saffari, Seyed Ehsan Volovici, Victor Chakraborty, Bibhas Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Emergency Medicine Singapore General Hospital Singapore Department of Neurosurgery Erasmus MC University Medical Center Rotterdam Netherlands Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States SingHealth AI Office Singapore Health Services Singapore Institute of Data Science National University of Singapore Singapore

Objective The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. The increasing diversity and complexity of data have led many researchers to develop deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on data types, aiming to assist healthcare researchers from various disciplines in dealing with missing values. Materials and Methods We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to August 2021 that applied DL-based models to imputation. We assessed selected publications from four perspectives: health data types, model backbone (i.e., main architecture), imputation strategies, and comparison with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. Results We included 64 articles, of which tabular static (26.6%, 17/64) and temporal data (37.5%, 24/64) were the most frequently investigated. We found that model backbone(s) differed among data types as well as the imputation strategy. The "integrated" strategy, that is, the imputation task being solved concurrently with downstream tasks, was popular for tabular temporal (50%, 12/24) and multi-modal data (71.4%, 5/7), but limited for other data types. Moreover, DL-based imputation methods yielded better imputation accuracy in most studies, compared with non-DL-based methods. Conclusion DL-based imputation models can be customized based on data type, addressing the corresponding missing patterns, and its associated "integrated" strategy can enhance the efficacy of imputation, especially in scenarios where data is complex. Future research may focus on the portability and fairness of DL-based models for healthcare data imputation. © 2022, CC BY.

关键词： Clinical research

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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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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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Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data

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Journal of Biomedical Informatics 2023年 143卷 104423页

作者： Chattopadhyay, Amrita Lee, Chien-Yueh Shen, Ying-Cheng Lu, Kuan-Chen Hsiao, Tzu-Hung Lin, Ching-Heng Lai, Liang-Chuan Tsai, Mong-Hsun Lu, Tzu-Pin Chuang, Eric Y. Bioinformatics and Biostatistics Core Centre of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Department of Biomedical Engineering College of Biomedical Engineering China Medical University Taichung Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Public Health Institute of Epidemiology and Preventive Medicine National Taiwan University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taiwan Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan Institute of Biotechnology National Taiwan University Taipei Taiwan Center of Biotechnology National Taiwan University Taipei Taiwan Institute of Health Data Analytics and Statistics National Taiwan University Taipei Taiwan Population Health Research Center National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan

Objective: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. Methods: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. Results: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://***/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. Conclusion: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore

关键词： Quality control

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HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles

arXiv

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arXiv 2025年

作者： Wang, Qing Chen, Wen-Jie Li, Bo Su, Jing Wang, Guangyu Song, Qianqian Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL32611 United States School of Biological and Behavioural Sciences Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer and Information Science University of Macau Macau SAR Taipa999078 China Department of Biostatistics and Health Data Science Indiana University School of Medicine IN46202 United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute TX77030 United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University NY United States

Histopathology, particularly hematoxylin and eosin (H&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular profiling. HECLIP is specifically designed to infer gene expression profiles directly from H&E-stained images, eliminating the need for expensive spatial transcriptomics assays. HECLIP leverages an advanced image-centric contrastive loss function to optimize image representation learning, ensuring that critical morphological patterns in histology images are effectively captured and translated into accurate gene expression profiles. This design enhances the predictive power of the image modality while minimizing reliance on gene expression data. Through extensive benchmarking on publicly available datasets, HECLIP demonstrates superior performance compared to existing approaches, delivering robust and biologically meaningful predictions. Detailed ablation studies further underscore its effectiveness in extracting molecular insights from histology images. Additionally, HECLIP’s scalable and cost-efficient approach positions it as a transformative tool for both research and clinical applications, driving advancements in precision medicine. The source code for HECLIP is openly available at https://***/QSong-github/HECLIP. © 2025, CC BY-NC-ND.

关键词： Molecular imaging

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia Computer Science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

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Procedia Computer Science 2021年 179卷 47-54页

作者： Kartika Purwandari Join W.C. Sigalingging Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.

关键词： weather classification twitter machine learning support vector machine multinomial naive bayes logistic regression

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Restraint Validation of Biomolecular Structures Determined By NMR in the Protein data Bank

SSRN

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SSRN 2023年

作者： Baskaran, Kumaran Ploskon, Eliza Tejero, Roberto Yokochi, Masashi Harrus, Deborah Liang, Yuhe Peisach, Ezra Persikova, Irina Ramelot, Theresa A. Sekharan, Monica Tolchard, James Westbrook, John D. Bardiaux, Benjamin Schwieters, Charles D. Patwardnhan, Ardan Venankar, Sameer Burley, Stephen K. Kurisu, Genji Hoch, Jeffrey C. Montelione, Gaetano T. Vuister, Geerten W. Young, Jasmine Y. Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health FarmingtonCT06030 United States Department of Molecular and Cell Biology Leicester Institute of Structural and Chemical Biology University of Leicester LeicesterLE1 7RH United Kingdom Department of Chemistry and Chemical Biology Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita565-0871 Japan Protein Data Bank Japan Protein Research Foundation Osaka Minoh562-8686 Japan Protein Data Bank in Europe EMBL-EBI Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ08854 United States Dept of Chemistry and Chemical Biology Center for Biotechnology and Interdisciplinary Sciences Rensselaer Polytechnic Institute TroyNY12180 United States Department of Structural Biology and Chemistry Institut Pasteur Université Paris Cité CNRS UMR3528 Paris75015 France Computational Biomolecular Magnetic Resonance Core National Institute of Diabetes and Digestive and Kidney Diseases BethesdaMD20892 United States The Electron Microscopy Data Bank EMBL-EBI Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla CA United States Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ08854 United States Cancer Institute of New Jersey Rutgers The State University of New Jersey New BrunswickNJ08901 United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey PiscatawayNJ08854 United States

Biomolecular structure analysis from experimental NMR generally relies on empirical (residual dipolar couplings, scalar couplings, paramagnetic relaxation enhancements, nuclear Overhauser effects) data and derived geometrical restraints (distance, dihedral angle). A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing establishment of uniform methods of model-vs-data structure validation and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data harvesting system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. © 2023, The Authors. All rights reserved.

关键词： Chemical shift

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Fine-scale genetic structure of Tujia and central Han Chinese revealing massive genetic admixture under language borrowing

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植物分类学报 2021年第1期59卷 1-20页

作者： Guang-Lin He Ying-Xiang Li Meng-Ge Wang Xing Zou Hui-Yuan Yeh Xiao-Min Yang Zheng Wang Ren-Kuan Tang Su-Min Zhu Jian-Xin Guo Ting Luo Jing Zhao Jin Sun Zi-Yang Xia Hao-Liang Fan Rong Hu Lan-Hai Wei Gang Chen Yi-Ping Hou Chuan-Chao Wang Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China School of Humanities Nanyang Technological UniversityNanyang 639798Singapore Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Department of Forensic Medicine College of Basic Medicine Chongqing Medical UniversityChongqing 400000China Department of Forensic Medicine College of Basic Medicine Chongqing Medical UniversityChongqing 400000China Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology In

Archaeological,genetic,and linguistic evidence has supported the idea that northern China is the original center of modern Sino-Tibetan-speaking ***,the demographic history of subsequent southward migration and genetic admixture of Han Chinese with surrounding indigenous populations remain uncharacterized,and the language shifts and assimilations accompanied by movement of people,or just an adaptation of cultural ideas among populations in central China is still unclear,especially for Tibeto-Burman-speaking Tujia and central Han Chinese *** resolve this,we genotyped over 60K genome-wide markers in 505 unrelated individuals from 63 indigenous *** results showed both studied Han and Tujia were at the intermediate position in the modern East Asian North-South genetic cline and there was a correlation between the genetic composition and the *** observed the strong genetic assimilation between Tujia people and central Han Chinese,which suggested massive population movements and genetic admixture under language *** and central Han Chinese could be modeled as a two-way admixture deriving primary ancestry from a northern ancestral population closely related to the ancient DevilsCave and present-day Tibetans and a southern ancestral population closely related to the present-day Tai-Kadai and Austronesian-speaking *** ancestral northern population we suspect to be related to the Neolithic millet farming groups in the Yellow River Basin or central *** showed that the newly genotyped populations in Hubei Province had a higher proportion of DevilsCave or modern Tungusic/Mongolic-related northern ancestries,while the Hunan populations harbored a higher proportion of Austronesian/Tai-Kadai-related southern ancestries.

关键词： demographic history gene flow genetic admixture language borrowing Tibeto‐Burman Tujia

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