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arXiv 2024年

作者： Aveni, Andrea Mukherjee, Sayan Department of Statistical Science Duke University United States Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

We study a generalization of the Fréchet mean on metric spaces, which we call φ-means. Our generalization is indexed by a convex function φ. We find necessary and sufficient conditions for φ-means to be finite and provide a tight bound for the diameter of the intrinsic mean set. We also provide sufficient conditions under which all the φ-means coincide in a single point. Then, we prove the consistency of the sample φ-mean to its population analogue. We also find conditions under which classes of φ-means converge uniformly, providing a Glivenko-Cantelli result. Finally, we illustrate applications of our results and provide algorithms for the computation of φ-*** Codes 0F05, 60F15, 60D05, 62E20, 62F10, 58K05 © 2024, CC BY.

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Recent Advances in Quantum Computing for Drug Discovery and Development

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IEEE Nanotechnology Magazine 2023年第2期17卷 26-30页

作者： Wang, Pei-Hua Chen, Jen-Hao Yang, Yu-Yuan Lee, Chien Tseng, Yufeng Jane Chung Yuan Christian University Quantum Information Center Taoyuan City320314 Taiwan National Taiwan University College of Electrical Engineering and Computer Science Graduate Institute of Biomedical Electronics and Bioinformatics Taipei10617 Taiwan Chunghwa Telecom Co. Ltd. Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan National Taiwan University Center for Quantum Science and Engineering Taipei10617 Taiwan

Drug discovery and development is a time-consuming and cost-intensive process. computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

关键词： Proteins

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Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers

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IEEE Robotics and Automation Letters 2023年第6期8卷 3772-3779页

作者： Huang, Ching-I Huang, Yu-Yen Liu, Jie-Xin Ko, Yu-Ting Wang, Hsueh-Cheng Chiang, Kuang-Hsing Yu, Lap-Fai Hsinchu30010 Taiwan Taipei Heart Institute Taipei Medical University Taipei11031 Taiwan Taipei Medical University Hospital Division of Cardiology and Cardiovascular Research Center Taipei110 Taiwan Taipei Medical University Department of Internal Medicine School of Medicine College of Medicine Taipei11031 Taiwan National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics Taipei106216 Taiwan George Mason University Department of Computer Science FairfaxVA22030 United States

Human-robot handover is a key capability of service robots, such as those used to perform routine logistical tasks for healthcare workers. Recent algorithms have achieved tremendous advances in object-agnostic end-to-end planar grasping with up to six degrees of freedom (DoF);however, compiling the requisite datasets is simply not feasible in many situations and many users consider the use of camera feeds invasive. This letter presents an end-to-end control system for the visual grasping of unseen objects with 6-DoF without infringing on the privacy or personal space of human counterparts. In experiments, the proposed Fed-HANet system trained using the federated learning framework achieved accuracy close to that of centralized non-privacy-preserving systems, while outperforming baseline methods that rely on fine-tuning. We also explores the use of a depth-only method and compares its performance to a state-of-the-art method, but ultimately emphasizes the importance of using RGB inputs for better grasp success. The practical applicability of the proposed system in a robotic system was assessed in a user study involving 12 participants. The dataset for training and all pretrained models are available at https://***/projects/***. © 2016 IEEE.

关键词： Robots

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Telomere-to-telomere and gap-free reference genome assembly of the kiwifruit Actinidia chinensis

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Horticulture Research 2023年第2期10卷 134-146页

作者： Junyang Yue Qinyao Chen Yingzhen Wang Lei Zhang Chen Ye Xu Wang Shuo Cao Yunzhi Lin Wei Huang He Xian Hongyan Qin Yanli Wang Sijia Zhang Ying Wu Songhu Wang Yi Yue Yongsheng Liu School of Horticulture Anhui Agricultural UniversityHefeiAnhui 230036China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhenGuangdong 518124China Institute of Fruit and Tea Hubei Academy of Agricultural SciencesWuhanHubei 430064China School of Information and Computer Anhui Agricultural UniversityHefeiAnhui 230036China Ministry of Education Key Laboratory for Bio-resource and Eco-environment College of Life ScienceState Key Laboratory of Hydraulic and Mountain River EngineeringSichuan UniversityChengduSichuan 610064China Department of Bioinformatics Anhui Double Helix Gene Technology CorporationHefeiAnhui 230022China Comprehensive Testing Ground Xinjiang Academy of Agricultural SciencesUrumqiXinjiang 830012China Institute of Special Animal and Plant Sciences Chinese Academy of Agricultural SciencesChangchunJilin 130112China

Kiwifruit is an economically and nutritionally important fruit crop with extremely high contents of vitamin ***,the previously released versions of kiwifruit genomes all have a mass of unanchored or missing ***,we report a highly continuous and completely gap-free reference genome of Actinidia chinensis cv.‘Hongyang’,named Hongyang v4.0,which is the first to achieve two de novo haploid-resolved haplotypes,HY4P and ***4P and HY4A have a total length of 606.1 and 599.6 Mb,respectively,with almost the entire telomeres and centromeres assembled in each *** comparison with Hongyang v3.0,the integrity and contiguity of Hongyang v4.0 is markedly improved by filling all unclosed gaps and correcting some misoriented regions,resulting in∼38.6–39.5 Mb extra sequences,which might affect 4263 and 4244 protein-coding genes in HY4P and HY4A,***,our gap-free genome assembly provides the first clue for inspecting the structure and function of ***,centromeric regions are characterized by higher-order repeats that mainly consist of a 153-bp conserved centromere-specific monomer(Ach-CEN153)with different copy numbers among *** enrichment analysis of the genes located within centromeric regions demonstrates that chromosome centromeres may not only play physical roles for linking a pair of sister chromatids,but also have genetic features for participation in the regulation of cell *** availability of the telomere-to-telomere and gap-free Hongyang v4.0 reference genome lays a solid foundation not only for illustrating genome structure and functional genomics studies but also for facilitating kiwifruit breeding and improvement.

关键词： breeding linking assembly

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Initial achievements in relation extraction from RNA-focused scientific papers 32

Initial achievements in relation extraction from RNA-focused...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Cavalleri, Emanuele Soto-Gomez, Mauricio Pashaeibarough, Ali Malchiodi, Dario Caufield, Harry Reese, Justin Mungall, Christopher J. Robinson, Peter N. Casiraghi, Elena Valentini, Giorgio Mesiti, Marco Department of Computer Science Università di Milano Via Celoria 18 Milano20133 Italy Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States ELLIS European Laboratory for Learning and Intelligent Systems Milan Unit Italy Berlin Institute of Health Charité Universitätsmedizin Berlin13353 Germany

Relation extraction from the scientific literature to comply with a domain ontology is a well-known problem in natural language processing and is particularly critical in precision medicine. The advent of large language models (LLMs) has paved the way for the development of new effective approaches to this problem, but the extracted relations can be affected by issues such as hallucination, which must be minimized. In this paper, we present the initial design and preliminary experimental validation of SPIREX, an extension of the SPIRES-based system for the extraction of RDF triples from scientific literature involving RNA molecules. Our system exploits schema constraints in the formulations of LLM prompts along with our RNA-based KG, RNA-KG, for evaluating the plausibility of the extracted triples. RNA-KG contains more than 9M edges representing different kinds of relationships in which RNA molecules can be involved. Initial experimental results on a controlled data set are quite encouraging. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

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CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network

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Journal of Chemical Information and Modeling 2024年第14期64卷 5725-5736页

作者： Yao, Lantian Xie, Peilin Guan, Jiahui Chung, Chia-Ru Huang, Yixian Pang, Yuxuan Wu, Huacong Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of HongKong Shenzhen518172 China School of Science and Engineering The Chinese University of HongKong Shenzhen518172 China School of Medicine The Chinese University of HongKong Shenzhen518172 China Department of Computer Science and Information Engineering National Central University Taoyuan320317 Taiwan Division of Health Medical Intelligence Human Genome Center The University of Tokyo Tokyo108-8639 Japan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence "images". Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks. © 2024 The Authors. Published by American Chemical Society.

关键词： DNA sequences

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Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation

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Analytical Chemistry 2024年第4期96卷 1538-1546页

作者： Yao, Lantian Guan, Jiahui Li, Wenshuo Chung, Chia-Ru Deng, Junyang Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China School of Medicine The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China Department of Computer Science and Information Engineering National Central University Taoyuan320317 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://***/lantianyao/ATPfinder/ (data sets and code) and https://***/dbAMP/*** (software). © 2024 American Chemical Society

关键词： Peptides

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Integrating Facial Image Data for Autism Identification: A Comparative Evaluation of Deep Learning Classifiers

Integrating Facial Image Data for Autism Identification: A C...

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International Conference on Signal Processing and Communication (ICSC)

作者： Khushi Jindal Jitendra Narayan D.K. Tayal Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Delhi India Council of Scientific & Industrial Research Institute of Genomics and Integrative Biology New Delhi India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Autism spectrum disorder (ASD) is characterized by difficulties in social relations, communication, and behavior, and as was shown by the results of several studies, deep learning models may be used for automated diagnosis of subjects with this disorder. This study aims to explore how deep learning models can be used to classify images of autistic individuals' facial features. For this purpose's objectives, a comparative analysis through several deep learning classifiers namely VGG16, VGG19, VGG Faces, and Resnet50 was performed to assess their ability to classify images of faces of children. The dataset includes 2926 images and the balance between autistic and non-autistic people was maintained. To improve model performance and to counter overfitting, we performed some preprocessing operations such as image normalization and resizing and used some data augmentation techniques such as rotation, shearing, and flipping. The results showed that the ResNet50 model accounted for all other models' limitations and achieved the highest score of 97.14%. The results of the study are linked to the recruitment of deep learning tools for the detection of ASD providing a great hope for improved tools for earlier interventions.

关键词： Deep learning Autism Neural networks Signal processing Faces Shearing Facial features Residual neural networks Recruitment Overfitting

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MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology

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Analytical Chemistry 2023年第6期95卷 3317-3324页

作者： Lin, Bo-Jhang Kuo, Tien-Chueh Chung, Hsin-Hsiang Huang, Ying-Chen Wang, Ming-Yang Hsu, Cheng-Chih Yao, Po-Yang Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei10617 Taiwan Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Surgery National Taiwan University Hospital Taipei10002 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei10002 Taiwan

Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images;the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://*** and https://***/CMDM-Lab/MSIr. © 2023 The Authors. Published by American Chemical Society.

关键词： Histology

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Sleep, physical activity and panic attacks: A two-year prospective cohort study using smartwatches, deep learning and an explainable artificial intelligence model

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Sleep Medicine 2024年 114卷 55-63页

作者： Tsai, Chan-Hen Christian, Mesakh Kuo, Ying-Ying Lu, Chen Chun Lai, Feipei Huang, Wei-Lieh Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan Department of Psychiatry En Chu Kong Hospital New Taipei City Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan Department of Psychiatry National Taiwan University Hospital Yunlin Branch Yunlin Taiwan Department of Psychiatry College of Medicine National Taiwan University Taipei Taiwan Cerebellar Research Centre National Taiwan University Hospital Yunlin Branch Yunlin Taiwan

Background: Sleep and physical activity suggestions for panic disorder (PD) are critical but less surveyed. This two-year prospective cohort study aims to predict panic attacks (PA), state anxiety (SA), trait anxiety (TA) and panic disorder severity (PDS) in the upcoming week. Methods: We enrolled 114 PD patients from one general hospital. Data were collected using the DSM-5, the MINI, clinical app questionnaires (BDI, BAI, PDSS-SR, STAI) and wearable devices recording daily sleep, physical activity and heart rate from 16 June 2020 to 10 June 2022. Our teams applied RNN, LSTM, GRU deep learning and SHAP explainable methods to analyse the data. Results: The 7-day prediction accuracies for PA, SA, TA, and PDS were 92.8 %, 83.6 %, 87.2 %, and 75.6 % from the LSTM model. Using the SHAP explainable model, higher initial BDI or BAI score and comorbidities with depressive disorder, generalized anxiety disorder or agoraphobia predict a higher chance of PA. However, PA decreased under the following conditions: daily average heart rate, 72–87 bpm;maximum heart rate, 100–145 bpm;resting heart rate, 55–60 bpm;daily climbing of more than nine floors;total sleep duration between 6 h 23 min and 10 h 50 min;deep sleep, >50 min;and awake duration, <53 min. Limitations: Moderate sample size and self-report questionnaires were the limitations. Conclusions: Deep learning predicts recurrent PA and various anxiety domains with 75.6–92.8 % accuracy. Recurrent PA decreases under adequate daily sleep and physical activity. © 2023 Elsevier B.V.

关键词： Deep learning Explainable artificial intelligence Heart rate variability Panic attack Panic disorder Smartwatch

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