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The status of the human gene catalogue

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arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia De La Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer L. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence D. Pertea, Mihaela Pruitt, Kim D. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven L. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

Artificial intelligence for the noninvasive diagnosis of clinically significant portal hypertension

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EngMedicine

EngMedicine 2025年第2期2卷

作者： Zheyu Du Ling Yang Hongliang He Xiaofeng Wu Xiaolong Qi Yudong Zhang Imaging Science and Technology Laboratory School of Computer Science and Technology Southeast University Nanjing China Liver Disease Center of Integrated Traditional Chinese and Western Medicine Department of Radiology Zhongda Hospital Medical School Southeast University Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University) Nanjing China Basic Medicine Research and Innovation Center of Ministry of Education Zhongda Hospital Southeast University State Key Laboratory of Digital Medical Engineering Nanjing China State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory for Biomaterials and Devices School of Biological Sciences & Medical Engineering Southeast University Nanjing China State Key Laboratory of Digital Medical Engineering School of Biological Science and Medical Engineering Southeast University Nanjing China

Cirrhosis is frequently associated with portal hypertension (PH), which can result in severe complications, including varices, ascites, and hepatic encephalopathy. The gold standard for diagnosing PH is the hepatic venous pressure gradient; however, its invasive nature necessitates the exploration of noninvasive diagnostic alternatives. Imaging techniques such as ultrasound, computed tomography, and magnetic resonance imaging are frequently employed but encounter challenges in the early detection of clinically significant portal hypertension (CSPH). Recent advances in artificial intelligence (ai), particularly machine and deep learning, have provided promising solutions. ai can analyze complex medical images and enhance diagnostic accuracy by identifying early indicators of PH. Techniques such as radiomics and vascularomics have demonstrated high efficacy in predicting CSPH, thereby improving noninvasive assessment. ai integration with multimodal data may yield more comprehensive, precise, and noninvasive diagnostic tools, facilitating early detection and enhancing the treatment of cirrhosis-related complications.

关键词： Clinically significant portal hypertension Cirrhosis Artificial intelligence Machine learning Deep learning

Balancing the Influence of Evolutionary Operators for Global optimization

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Balancing the Influence of Evolutionary Operators for Global...

Congress on Evolutionary Computation

作者： Diego Oliva Erick Rodriguez-Esparza Marcella S. R. Martins Mohamed Abd Elaziz Salvador Hinojosa Ahmed A. Ewees Songfeng Lu Universidad de Guadalajara Guadalajara Mexico Federal University of Technology Ponta Grossa Brazil Huazhong University of Science and Technology Wuhan China Department of Computer Damietta University Egypt Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.

关键词： Sociology Statistics Genetic algorithms Evolutionary computation Optimization Space exploration Measurement

Efficient Hardware-Assisted Crash Consistency in Encrypted Persistent Memory

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Efficient Hardware-Assisted Crash Consistency in Encrypted P...

Design, Automation and Test in Europe Conference and Exhibition

作者： Zhan Zhang Jianhui Yue Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China Computer Science Department Michigan Technological University Houghton Michigan

ISBN: (数字)9783981926347

ISBN: (纸本)9781728144689

The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on programmers but also degrades performance. To address this issue, we propose a hardware-assisted encrypted persistent memory system. Specifically, logging and data encryption are assisted by hardware. Furthermore, we apply the counter-based encryption and the cipher feedback (CFB) mode encryption to data and log respectively, reducing the encryption overhead. Our primary experimental results show that the transaction throughput of the proposed design outperforms the baseline design by up to 34.4%.

关键词： Encryption computer crashes Hardware Writing Programming

Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

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arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe R., Terrien R., Holcomb E., Ford B., Zawadzki B.P., Bowler C., Bender W., Cochran S., Diddams M., Endl C., Fredrick S., Halverson F., Hearty G.J., Hill A., Lin A., Metcalf A., Monson L., Ramsey A., Roy C., Schwab J., Wright G., Zeimann Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

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arXiv 2024年

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024, CC BY-NC-ND.

关键词： Neurons

Robust machine learning models for calculating the carbon dioxide desublimation point within natural gas mixtures at low temperature conditions

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Journal of CO2 Utilization 2025年 99卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Dharmesh Sur Menon Soumya V Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Fereydoon Ranjbar Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Chemical Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Department of Chemistry Islamic Azad University Najafabad Branch Iran

Desublimation at low temperatures offers an efficient method for removing CO 2 from gas streams. Accurate prediction of the carbon dioxide desublimation temperature (CDDT) is essential for applying this method in natural gas processing. This investigation aimed to develop predictive tools utilizing machine learning approaches to estimate CDDT within natural gas mixtures. To reach this target, a large data set comprising 430 measurements obtained from published sources, was prepared. These data points cover the CDDT in binary and ternary natural gas mixtures under different pressures and gas fractions. In addition to black-box tools such as Decision Tree (DT), Gaussian Process Method (GPM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods, a mathematical equation was developed via Genetic Programming (GP) technique for CDDT calculation. The performances of the designed models were rigorously evaluated through various visual inspections and statistical indices. While all models demonstrated excellent predictive accuracy, the GPM model provided superior results among black-box tools, exhibiting a mean absolute percentage error (MAPE) of 0.99 %. Furthermore, the GP equation achieved an overall MAPE of 0.65 % for the CDDT data. The intelligent models also performed well in predicting the data pertinent to both binary and ternary mixtures. A series of simulations based on the models’ outcomes were carried out to depict the CDDT variations in response to operational parameters, and the findings showed full consistency with previous experimental results. Ultimately, a sensitivity analysis was conducted to pinpoint the dominant factors affecting the CO 2 desublimation. Overall, the outcomes of this research enhance the understanding of CDDT behavior and provide valuable information for optimizing low-temperature CO 2 capture processes used in natural gas purification.

关键词： Carbon dioxide desublimation temperature (CDDT) Carbon capture Natural gas Modeling Intelligent methods

$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing computer architecture Business intelligence Neural networks Semantics