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arXiv 2022年

作者： Arya, Shreya Curry, Justin Mukherjee, Sayan Department of Mathematics Duke University DurhamNC United States Department of Mathematics and Statistics University at Albany SUNY AlbanyNY United States Department of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University DurhamNC United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig The Max Planck Institute for Mathematics in the Natural Sciences Leipzig Germany

We present a sheaf-theoretic construction of shape space-the space of all shapes. We do this by describing a homotopy sheaf on the poset category of constructible sets, where each set is mapped to its Persistent Homology Transforms (PHT). Recent results that build on fundamental work of Schapira have shown that this transform is injective, thus making the PHT a good summary object for each shape. Our homotopy sheaf result allows us to "glue" PHTs of different shapes together to build up the PHT of a larger shape. In the case where our shape is a polyhedron we prove a generalized nerve lemma for the PHT. Finally, by re-examining the sampling result of Smale-Niyogi-Weinberger, we show that we can reliably approximate the PHT of a manifold by a polyhedron up to arbitrary precision. © 2022, CC BY.

关键词： Geometry

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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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data annotation system for intelligent energy conservator in smart building 3

Data annotation system for intelligent energy conservator in...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Chandra, Bloomest Jansen Rahutomo, Reza Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department College of Letters and Science University of Wisconsin MadisonWI53706 United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Historic preservation

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RiceENCODE:A comprehensive epigenomicdatabase as a rice Encyclopedia of DNA Elements

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Molecular Plant 2021年第10期14卷 1604-1606页

作者： Liang Xie Minghao Liu Lun Zhao Kai Cao Peng Wang Wenhao Xu Wing-Kin Sung Xingwang Li Guoliang Li National Key Laboratory of Crop Genetic Improvement Hubei HongshanLaboratoryHuazhong Agricultural University1 Shizishan StreetHongshanDistrictWuhan 430070HubeiChina Agricultural Bioinformatics Key Laboratory of Hubei Province and HubeiEngineering Technology Research Center of Agricultural Big Data 3DGenomics Research CenterHuazhong Agricultural University1 ShizishanStreetHongshan DistrictWuhan 430070HubeiChina Department of Computer Science National University of SingaporeSingapore 117417Singapore Department of Computational and Systems Biology Genome Institute ofSingaporeSingapore 138672Singapore

Dear Editor,Rice(Oryza sativa)is one of the most important crops in the worldand a common model plant for genomic *** genomesofXian/IndicaandGeng/Japonicahave been completelysequenced and annotated with accurate genome *** the past few years,epigenomic information,including DNAmethylation,histone modification,and chromatin accessibility,has been characterized in theXian/IndicaandGeng/Japonicage-nomes(Zhao et al.,2020).Quite a few rice three-dimensionalgenome studies have been published in the meantime(Zhaoet al.,2019).However,it is still a big challenge for many groupsthat lack dedicated bioinformatic personnel or sufficientcomputational resources to utilize such epigenetic data.

关键词： sativa Oryza crops

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2023年 9卷 e45312页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001

关键词： artificial intelligence chatbot ChatGPT conversational agent education technology generative pre-trained transformer GPT machine learning medical education MedQA natural language processing NLP

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Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

arXiv

引用

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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RHC: A dataset for In-Room and Out-Room Human Counting

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Procedia Computer science 2021年 179卷 33-39页

作者： Bens Pardamean Hery Harjono Muljo Faizal Abid Herman Albert Susanto Tjeng Wawan Cenggoro Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://***/datasets/vt5c8h6kmh/1.

关键词： dataset human counting crowd counting in-room out-room

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine 3

Maintaining the Quality and Aroma of Coffee with Fuzzy Logic...

引用

3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Samodro, Bayu Mahesworo, Bharuno Suparyanto, Teddy Surya Atmaja, Don Bosco Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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FedScore: A Privacy-Preserving Framework for Federated Scoring System Development

arXiv

引用

arXiv 2023年

作者： Li, Siqi Ning, Yilin Ong, Marcus Eng Hock Chakraborty, Bibhas Hong, Chuan Xie, Feng Yuan, Han Liu, Mingxuan Buckland, Daniel M. Chen, Yong Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Duke University School of Medicine DurhamNC United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Institute of Data Science National University of Singapore Singapore Singapore

Objective We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. Materials and Methods The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore’s performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a preexisting score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. Results We compared the acquired FedScore model’s performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. Conclusion This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability. © 2023, CC BY.

关键词： Hospitals

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