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SSRN 2025年

作者： Itkyal, Vaibhavi Sanjeet Abrol, Anees LaGrow, Theodore J. Fedorov, Alex Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University GA United States Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University GA United States Department of Neuroscience Emory University GA United States Emory University United States

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis to support clinical decision-making and future intervention strategies. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI (sMRI) are promising tools for identifying AD-related brain alterations. In this study, we propose a novel deep learning framework that fuses spatial maps of rs-fMRI functional networks—via a new voxelwise intensity projection (iVIP) method—and gray matter segmentations to classify subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our multi-channel 3D convolutional neural network (CNN), inspired by the AlexNet architecture, integrates anatomical and functional modalities to perform both two-way (AD vs. CN) and three-way (AD vs. MCI vs. CN) classification tasks. While the two-way classification achieves high performance (94.12% accuracy, 97.79 AUC), the three-way classification offers clinically relevant utility, achieving 62.68% accuracy and 68.31 AUC, and enables finer differentiation among diagnostic subtypes. Comparative analysis shows that our iVIP representation outperforms traditional rs-fMRI measures such as ALFF and fALFF, and the fusion of iVIP with sMRI consistently outperforms unimodal models. Saliency map visualizations further reveal discriminative regions—including the hippocampus, amygdala, caudate nucleus, and thalamus—aligned with AD pathology. Our findings highlight the potential of deep multimodal fusion to enhance early diagnosis and subtype differentiation in Alzheimer’s disease. © 2025, The Authors. All rights reserved.

关键词： Brain mapping

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Comprehensive performance assessment of Bi- and Pb-based alloys for enhanced Gamma-Ray and neutron shielding applications

引用

Radiation Physics and Chemistry 2025年 237卷

作者： ALMisned, Ghada Susoy, Gulfem Baykal, Duygu Sen Kilic, G. Güler, Ömer Tekin, H.O. Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Istanbul University Faculty of Science Department of Physics Istanbul34134 Turkey Istanbul Nisantasi University Faculty of Engineering and Architecture Mechatronics Engineering Istanbul34398 Turkey Eskisehir Osmangazi University Faculty of Science Department of Physics Eskisehir26040 Turkey Munzur University Rare Earth Elements Application and Research Center Tunceli62000 Turkey Department of Medical Diagnostic Imaging College of Health Sciences University of Sharjah Sharjah27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul34396 Turkey

This comprehensive study investigates the radiation attenuation properties of various bismuth (Bi) and lead (Pb) based alloys, with a focus on identifying superior shielding materials for nuclear safety and industrial radiation protection. The research encompasses an in-depth analysis of seven alloys, namely GTSB5, Sn%40Bi, Bi50Sn50, A1, PbAl-4, Safe Alloy, and P1, assessing their efficacy in gamma-ray and neutron attenuation. Utilizing some computational tools, various parameters such as mass and linear attenuation coefficients, half and tenth value layer values, effective atomic and electron numbers, buildup factors, and transmission values were evaluated. Our findings highlight Alloy P1's outstanding performance, attributed to its high density (11.05 g/cm3) and optimized elemental composition, including Silver (Ag). In addition, P1 demonstrates exceptional gamma-ray attenuation across multiple energy spectra and superior neutron shielding capabilities, evidenced by its significant fast neutron removal cross-section (∑R, 0.12003 1/cm). The study revealed that the addition of Ag enhances the photon and beta particle absorption capacity, positioning P1 as a promising material in radiation shielding applications. The correlations between alloy density and gamma-ray shielding properties were particularly notable, with P1's density contributing to its enhanced attenuation capabilities. Furthermore, P1's lower half value layers (1.14469 cm) and tenth value layers (3.80256 cm) at 15 MeV indicate its efficiency in reducing radiation intensity with minimal material thickness. © 2025 Elsevier Ltd

关键词： Electromagnetic shielding

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Measurement of the Pr141(n,γ) cross section up to stellar s-process temperatures at the China Spallation Neutron Source Back-n facility

引用

Physical Review C 2023年第3期108卷 035802-035802页

作者： Xiankai Li Zhendong An Wei Jiang Jingyi Zhang Ruoran Bai Shaokun Liu Xinxiang Li Zhichao Zhu Gaole Yang Weiwei Qiu Zhengfa Liao Ziyue Zhuang Xiaoping Zhang Shengli Chen Chenchen Guo Erxi Xiao Xiao Fang Hongwei Wang Xinrong Hu Bing Jiang Wenqing Shen Jincheng Wang Jie Ren Xichao Ruan Wen Luo Haoyang Lan Zongwei Cao Xu Ma Yingdu Liu Pusen Wang Yi Yang Ping Su Xiangai Deng Wanbing He Yugang Ma Chunwang Ma Yuting Wang Zhitao Dai Ertao Li Pengqin He RenGuang Tang Tao Zhou Jing Wang Han Yi Yue Zhang Yonghao Chen Ruirui Fan Keqing Gao Qiang Li Kang Sun Zhixin Tan Minhao Gu Hantao Jing Jingyu Tang School of Nuclear Science and Technology University of South China Hengyang 421001 China School of Physics and Astronomy Sun Yat-sen University Zhuhai 519082 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology Macau 999078 China CNSA Macau Center for Space Exploration and Science Macau 999078 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China University of Chinese Academy of Sciences Beijing 100049 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai 519082 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China State Key Laboratory of Nuclear Physics and Technology and CAPT Peking University Beijing 100871 China Beijing Laser Acceleration Innovation Center Huairou Beijing 101400 China Shool of Materials Science and Engineering Xiangtan University Xiangtan 411100 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Institute of Particle and Nuclear Physics Henan Normal University Xinxiang 453007 China School of Physics Henan Normal University Xinxiang 453007 China Shenzhen Center Cancer hospital Chinese Academy of Medical Sciences Shenzhen 518100 China Institute for Advanced Study in Nuclear Energy and Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China

In nuclear astrophysics, Pr141 is mainly produced by an s process, which has a neutron magic number (N=82) leading to its low capture cross section and strongly affecting the s process. Its neutron capture cross section is an important input parameter for nuclear astrophysical network calculations. According to the EXFOR database, the measurement precision for the Pr141(n,γ) cross section should be improved in the resolved and unresolved resonance region. The (n,γ) cross section of Pr141 was measured between 1 eV and 500 keV at the Back-n facility of the Chinese Spallation Neutron Source using the time-of-flight (TOF) method. The prompt γ rays were detected by four C6D6 liquid scintillator detectors, and the data was analyzed using the pulse-height weighting technique (PHWT). The majority of results are in agreement with the evaluated data libraries ENDF/B-VIII.0, JENDL-5.0, and TENDL-2021, with some significant exceptions for small resonances. The multilevel R-matrix Bayesian code sammy was used to extract the resonance parameters of Pr141 in the resolved resonance region. And the Maxwell average cross section (MACS) from kT=5 to 100 keV is calculated in the temperature range of the s-process nucleosynthesis model. In particular, at kT=30 keV the MACS value of Pr141 is 145.0±13.7 mb, which shows an obvious discrepancy with the Karlsruhe Astrophysical database of Nucleosynthesis in Stars (KADoNiS) recommended value 111.4±1.4 mb.

关键词： Neutron physics Nuclear astrophysics Nuclear reactions Nucleosynthesis in explosive environments Radiative capture Resonance reactions 90 ≤ A ≤ 149 Nuclear data analysis & compilation Radiation detectors

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Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

arXiv

引用

arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (ai) has shown great potential to automatize this process. However, most existing ai algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized ai algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of ai algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern ai models. © 2023, CC BY-NC-ND.

关键词： Diseases

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Estimations of first 2+energy states of even-even: Nuclei by using artificial neural networks

arXiv

引用

arXiv 2020年

作者： Akkoyun, Serkan Kaya, Hüseyin Torun, Yunis Department of Physics Sivas Cumhuriyet University Sivas58140 Turkey Artificial Intelligence Systems and Data Science Application and Research Center Sivas Cumhuriyet University Sivas58140 Turkey Department of Electric and Electronic Engineering Sivas Cumhuriyet University Sivas58140 Turkey

The first excited 2+energy states of nuclei give much substantial information related to the nuclear structure. Including these levels, all excited states of nuclei are shown regularities in spin, parity and energy. In the even-even nuclei, the first excited state is generally 2+and the energy values of them increase as the closed shells are approached. The excited levels in nuclei can be investigated by using theoretical nuclear models such as nuclear shell model. In the present study for the first time, we have used artificial neural networks for the determination of the energies of the first 2+states in the even-even nuclei in the nuclidic chart as a function of Z and N numbers. According to the results, the method is convenient for this goal and one can confidently use the method for the prediction of the first 2+state energy values whose experimental values do not exist in the literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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Sudden polarization angle jumps of the repeating fast radio burst FRB 20201124A

arXiv

引用

arXiv 2024年

作者： Niu, Jiarui R. Wang, W.Y. Jiang, J.C. Qu, Y. Zhou, D.J. Zhu, W.W. Lee, K.J. Han, J.L. Zhang, B. Li, D. Cao, S. Fang, Z.Y. Feng, Y. Fu, Q.Y. Jiang, P. Jing, W.C. Li, J. Li, Y. Luo, R. Meng, L.Q. Miao, C.C. Miao, X.L. Niu, C.H. Pan, Y.C. Wang, B.J. Wang, F.Y. Wang, H.Z. Wang, P. Wu, Q. Wu, Z.W. Xu, H. Xu, J.W. Xu, L. Xue, M.Y. Yang, Y.P. Yuan, M. Yue, Y.L. Zhao, D. Zhang, C.F. Zhang, D.D. Zhang, J.S. Zhang, S.B. Zhang, Y.K. Zhu, Y.H. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China The Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Astronomy Peking University Beijing100871 China Yunnan Observatories Chinese Academy of Sciences P.O.Box110 Yunnan Kunming650011 China Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District Zhejiang Province Hangzhou China School of Computer Science and Engineering Beihang University Beijing100191 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Department of Astronomy School of Physics and Materials Science Guangzhou University Guangzhou510006 China Institute of Astrophysics Central China Normal University Wuhan430079 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education China Lanzhou University Lanzhou730000 China Faculty of Electrical engineering and Computer Science Ningbo University Ningbo315211 China Pengcheng Laboratory Shenzhen518055 China South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550025 China

We report the first detection of polarization angle (PA) orthogonal jumps, a phenomenon previously only observed from radio pulsars, from a fast radio burst (FRB) source FRB 20201124A. We find three cases of orthogonal jumps in over two thousand bursts, all resembling those observed in pulsar single pulses. We propose that the jumps are due to the superposition of two orthogonal emission modes that could only be produced in a highly magnetized plasma, and they are caused by the line of sight sweeping across a rotating magnetosphere. The shortest jump timescale is of the order of one-millisecond, which hints that the emission modes come from regions smaller than the light cylinder of most pulsars or magnetars. This discovery provides convincing evidence that FRB emission originates from the complex magnetosphere of a magnetar, suggesting an FRB emission mechanism that is analogous to radio pulsars despite a huge luminosity difference between two types of objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Polarization

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

引用

2nd Eai International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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Inferring scattering-type Scanning Near-Field Optical Microscopy data from Atomic Force Microscopy Images

arXiv

引用

arXiv 2025年

作者： Stanciu, Stefan G. Anton, Stefan R. Tranca, Denis E. Stanciu, George A. Ionescu, Bogdan Zalevsky, Zeev Kusnetz, Binyamin Belhassen, Jeremy Karsenty, Avi Cincotti, Gabriella Center for Microscopy-Microanalysis and Information Processing National University of Science and Technology POLITEHNICA Bucharest 313 Splaiul Independentei 060042 Romania CAMPUS Research Institute National University of Science and Technology POLITEHNICA Bucharest 313 Splaiul Independentei 060042 Romania AI Multimedia Lab National University of Science and Technology POLITEHNICA Bucharest 313 Splaiul Independentei 060042 Romania Faculty of Engineering Institute of Nanotechnology and Advanced Materials Bar-Ilan University Ramat Gan5290002 Israel Dept. of Applied Physic/Electro-Optics Engineering Faculty of Engineering Jerusalem College of Technology Jerusalem9116001 Israel Nanotechnology Center for Research and Education Jerusalem College of Technology Jerusalem9116001 Israel Department of Civil Computer Science and Aeronautical Technologies Engineering University Roma Tre Rome Italy

Optical nanoscopy is crucial in life and materials sciences, revealing subtle cellular processes and nanomaterial properties. Scattering-type Scanning Near-field Optical Microscopy (s-SNOM) provides nanoscale resolution, relying on the interactions taking place between a laser beam, a sharp tip and the sample. The Atomic Force Microscope (AFM) is a fundamental part of an s-SNOM system, providing the necessary probe-sample feedback mechanisms for data acquisition. In this Letter, we demonstrate that s-SNOM data can be partially inferred from AFM images. We first show that a generative artificial intelligence (ai) model (pix2pix) can generate synthetic s-SNOM data from experimental AFM images. Second, we demonstrate that virtual s-SNOM data can be extrapolated from knowledge of the tip position and, consequently, from AFM signals. To this end, we introduce an analytical model that explains the mechanisms underlying AFM-to-s-SNOM image translation. These insights have the potential to be integrated into future physics-informed explainable ai models. The two proposed approaches generate pseudo s-SNOM data without direct optical measurements, significantly expanding access to optical nanoscopy through widely available AFM systems. This advancement holds great promise for reducing both time and costs associated with nanoscale imaging. © 2025, CC BY.

关键词： Near field scanning optical microscopy

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

引用

research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

引用

arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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