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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Development of Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine) acceptor material for organic solar cells applications

Development of Heterocyclic-C61-butyric Acid Methyl Ester/ P...

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IEEE Conference on Nanotechnology

作者： Muath Bani Salim Reza Nekovei Tamer Elwaie Jeyakumar Ramanujam Sustainable Energy Systems Engineering Program Texas A&M University–Kingsville Kingsville TX Electrical Engineering and Computer Science Texas A&M University–Kingsville Kingsville TX Pharmaceutical Chemistry Department Cairo University Cairo Egypt Physics of Energy Harvesting Division CSIR-National Physical Laboratory Delhi India

This paper presents a novel modification on the Phenyl-C61-butyric acid methyl ester (PCBM) for better performance as an acceptor material by introducing a Heterocyclic ring (Pyrazine) in the place of the Phenyl group of the PCBM to form a new acceptor material, Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine). The electronic properties for the new material are obtained theoretically through the use of the Density Functional Theory (DFT) method with the help of the Gaussian 16 program. The results show that the HCBM-Pyrazine can create more voltage if used with any donor material. Besides that, it has a considerably higher electron affinity, making it a more efficient electron acceptor than the original PCBM. Furthermore, the results show that both molecules have closer absorption spectrums and reorganization energies.

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

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Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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Work in Progress – Establishing a Master program in Cyber Physical systems: Basic Findings and Future Perspectives

Work in Progress – Establishing a Master Program in Cyber P...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Isam Ishaq Rashid Jayousi Salaheddin Odeh Ezzaldeen Edwan Asmaa Shaheen Mohamed Elnaggar Ahmed Elagha Samy Salamah Christian Weber Hasan Abu Rasheed Raimund Kirner Martina Doolan Hamidreza Ahmadian Roman Obermaisser Dhiah el Diehn I. Abou-Tair Ala Khalifeh Sahel Alouneh Zaid Alhalhouli Khalid Alemerien Faiez Gargouri Bassem Bouaziz Nadia Aloui Mohammad Saleh Computer Engineering and Science Departments Al-Quds University Jerusalem Palestine Engineering Program and Computer Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Institute of Knowledge Based Systems & Knowledge Management University of Siegen Siegen Germany University of Siegen Siegen Germany School of Engineering and Computer Science University of Hertfordshire Hatfield UK Chair of Embedded Systems University of Siegen Siegen Germany German Jordanian University Amman Jordan School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan Computer and Information Technology Department Tafila Technical University Tafila Jordan Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia Higher Institute of Applied Sciences and Technology of Mateur University of Carthage Mateure Tunisia KTH Royal Institute of Technology Stockholm Sweden

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.

关键词： computer science Internet of Things Industries Knowledge engineering Embedded systems Europe Collaboration

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IoT-Based Architecture for Data Analytics of Arboviruses in Smart Cities

IoT-Based Architecture for Data Analytics of Arboviruses in ...

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IEEE Symposium on computers and Communications

作者： Priscylla da S. Tavares Emanuel B. Rodrigues Group of Computer Networks Software Engineering and Systems (GREat) Graduate Program in Computer Science (MDCC) Federal University of Ceará (UFC) Fortaleza Brazil

With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.

关键词： Internet of Things Data analytics Health care Smart city

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Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

arXiv

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arXiv 2021年

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Department of Radiology School of Medicine University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States College of Computer National University of Defense Technology Changsha410073 China Department of Bioengineering Swanson School of Engineering University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Department of Radiology University of Pittsburgh University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh 4401 Penn Ave PittsburghPA15224 United States Magee-Womens Hospital University of Pittsburgh Medical Center 300 Halket St PittsburghPA15213 United States Department of Biomedical Informatics University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Intelligent Systems Program University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States

Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model’s performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional "middle" classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. © 2021, CC BY.

关键词： Image classification

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Proposal of an integrated decision support system for Tuberculosis based on Semantic Web

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Procedia computer Science 2019年 164卷 552-558页

作者： Vinícius Lima Felipe Pellison Filipe Bernardi Isabelle Carvalho Rui Rijo Domingos Alves Bioengineering Postgraduate Program University of Sao Paulo Sao Carlos Brazil Institute of Mathematical and Computer Sciences University of Sao Paulo Sao Carlos Brazil School of Technology and Management Polytechnic Institute of Leiria Leiria Portugal Centre for Research in Health Technologies and Information Systems (CINTESIS) University of Porto Porto Portugal Institute for Systems and Computers Engineering at Coimbra (INESCC) Coimbra Portugal Ribeirão Preto Medical School University of Sao Paulo Ribeirao Preto Brazil

Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models.

关键词： Tuberculosis Decision Support System Health Information System Health Information Management Semantic Web

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

PurposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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