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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Integration of droplet microfluidic tools for single-cell functional metagenomics: an engineering head start

TechRxiv

引用

TechRxiv 2021年

作者： Conchouso, David Al-Ma’abadi, Amani Behzad, Hayedeh Alarawi, Mohammed Hosokawa, Masahito Nishikawa, Yohei Takeyama, Haruko Mineta, Katsuhiko Gojobori, Takashi Departamento de Ingeniería Industrial y Mecánica Escuela de Ingeniería Universidad de las Américas Puebla Puebla72810 Mexico Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Research Organization for Nano & Life Innovation Waseda University Tokyo162–0041 Japan Department of Life Science and Medical Bioscience Waseda University Tokyo162–8480 Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Waseda University Tokyo169-8555 Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda University Tokyo169–0072 Japan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data. © 2021, CC BY-NC-SA.

关键词： Cell proliferation

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Iterative joint image demosaicking and denoising using a residual denoising network

arXiv

引用

arXiv 2018年

作者： Kokkinos, Filippos Lefkimmiatis, Stamatios Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology

Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them relies on generic network architectures which do not explicitly take into account the physical image model. In this work we propose a novel algorithm which is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared to other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noisefree data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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Antisymmetry rules of response properties in certain chemical spaces

arXiv

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arXiv 2025年

作者： Shiraogawa, Takafumi Krug, Simon León Ehara, Masahiro von Lilienfeld, O. Anatole Institute for Molecular Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Research Center for Computational Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan The Graduate University for Advanced Studies 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Machine Learning Group Technische Universität Berlin Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoM5S3H6 Ontario Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoM5S 3E4 Ontario Canada Vector Institute for Artificial Intelligence TorontoM5S 1M1 Ontario Canada Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Physics University of Toronto St. George Campus TorontoM5S 1A7 Ontario Canada Acceleration Consortium University of Toronto TorontoM5R 0A3 Ontario Canada

Understanding chemical compound space (CCS), a set of molecules and materials, is crucial for the rational discovery of molecules and materials. Concepts of symmetry have recently been introduced into CCS to account for near degeneracies and differences in electronic energies between iso-electronic materials. In this work, we present approximate relationships of response properties based on a first-principles view of CCS. They have been derived from perturbation theory and antisymmetry considerations involving nuclear charges. These rules allow approximate predictions of relative response properties of pairs of distinct compounds with opposite nuclear charge variations from a highly symmetric reference material, without the need for experiments or quantum chemical calculations of each compound. We numerically and statistically verified these rules for electric and magnetic response properties (electric dipole moment, polarizabilities, hyperpolarizabilities, and magnetizabilities) among charge-neutral and iso-electronic BN-doped polycyclic aromatic hydrocarbon derivatives of naphthalene, anthracene, and pyrene. Our analysis indicates that, despite their simplicity, antisymmetry rule-based predictions are remarkably accurate, enabling dimensionality reduction of CCS. Response properties in alchemical perturbation density functional theory were investigated to clarify the origin of this predictive power. © 2025, CC BY.

关键词： Naphthalene

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Scaling up the lattice dynamics of amorphous materials by orders of magnitude

引用

Physical Review B 2020年第2期102卷 024108-024108页

作者： Ivan Kriuchevskyi Vladimir V. Palyulin Rico Milkus Robert M. Elder Timothy W. Sirk Alessio Zaccone Department of Physics “A. Pontremoli” University of Milan via Celoria 16 20133 Milan Italy Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow 121205 Russia Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge CB3 0AS United Kingdom Polymers Branch U.S. Army Research Laboratory Aberdeen Proving Ground Maryland 20783 USA Bennett Aerospace Inc. Cary North Carolina 27518 USA Center for Devices and Radiological Health U.S. Food and Drug Administration Silver Spring Maryland 20903 USA

We generalize the nonaffine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic resolution, we propose an extension of the kernel polynomial method (KPM) for the computation of the vibrational density of states and the eigenmodes, including the Γ correlator of the affine force field, which is a key ingredient of lattice-dynamic calculations of viscoelasticity. We show that the results converge well to the solution obtained by direct diagonalization (DD) of the Hessian (dynamical) matrix. As is well known, the DD approach has prohibitively high computational requirements for systems with N=104 atoms or larger. Instead, the KPM approach developed here allows one to scale up lattice dynamic calculations of real materials up to 106 atoms, with a hugely more favorable (linear) scaling of computation time and memory consumption with N.

关键词： Dissipative dynamics Lattice dynamics Mechanical deformation Amorphous materials Polydisperse materials Polymer glasses Random & disordered media Viscoelasticity Molecular dynamics Numerical techniques

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Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

arXiv

引用

arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, Aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

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HPV-CCDC106 integration alters local chromosome architecture and hijacks an enhancer by three-dimensional genome structure remodeling in cervical cancer

引用

Journal of Genetics and Genomics 2020年第8期47卷 435-448页

作者： Canhui Cao Ping Hong Xingyu Huang Da Lin Gang Cao Liming Wang Bei Feng Ping Wu Hui Shen Qian Xu Ci Ren Yifan Meng Wenhua Zhi Ruidi Yu Juncheng Wei Wencheng Ding Xun Tian Qinghua Zhang Wei Li Qinglei Gao Gang Chen Kezhen Li Wing-Kin Sung Zheng Hu Hui Wang Guoliang Li Peng Wu Department of Gynecologic Oncology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Cancer Biology Research Center(Key Laboratory of the Ministry of Education) Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big DataCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan430070China Bio-Medical Center Huazhong Agricultural UniversityWuhan430070China College of Veterinary Medicine Huazhong Agricultural UniversityWuhan430070China Department of Obstetrics and Gynecology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China Department of Obstetrics and Gynecology Key Laboratory for Molecular Diagnosis of Hubei ProvinceThe Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430010China Department of Computational and Systems Biology Genome Institute of SingaporeSingapore Department of Gynecological Oncology The First Affiliated Hospital of Sun Yat-sen UniversityGuangzhou510080China

Integration of human papillomavirus(HPV)DNA into the human genome is a reputed key driver of cervical ***,the effects of HPV integration on chromatin structural organization and gene expression are largely *** studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome *** then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome,RNA,chromatin immunoprecipitation and high-throughput chromosome conformation capture(Hi-C)sequencing to identify the mechanisms of HPV integration in cervical *** analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities,with correlation found between three-dimensional(3D)structural change and gene ***,HPV integration divided one topologically associated domain(TAD)into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106,with a decrease in PEG3 expression and an increase in CCDC106 *** expression dysregulation was further confirmed using 10 samples from our cohort,which exhibited the same HPV-CCDC106 *** summary,we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure ***,this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

关键词： Cervical cancer HPV integration Fusion gene Hi-C Enhancer TAD

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Prediction of Oral Food Challenge Outcomes via Ensemble Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Pediatrics University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computer Science and Engineering University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Mary H. Weiser Food Allergy Center University of Michigan Ann ArborMI United States Department of Computer Science Queens College City University of New York New YorkNY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥ 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Optimizing Laser Pulses for Narrow-Band Inverse Compton Sources in the High-Intensity Regime

引用

Physical Review Letters 2019年第20期122卷 204802-204802页

作者： Daniel Seipt Vasily Yu. Kharin Sergey G. Rykovanov Helmholtz-Institut Jena Fröbelstieg 3 07743 Jena Germany Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan 48109 USA Research Institute Moscow R&D Lab Moscow Bersenevskaya nab. 6 119072 Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobel Str. 3 Skolkovo Russia

Scattering of ultraintense short laser pulses off relativistic electrons allows one to generate a large number of X- or gamma-ray photons with the expense of the spectral width—temporal pulsing of the laser inevitable leads to considerable spectral broadening. In this Letter, we describe a simple method to generate optimized laser pulses that compensate the nonlinear spectrum broadening and can be thought of as a superposition of two oppositely linearly chirped pulses delayed with respect to each other. We develop a simple analytical model that allows us to predict the optimal parameters of such a two-pulse—the delay, amount of chirp, and relative phase—for generation of a narrow-band γ-ray spectrum. Our predictions are confirmed by numerical optimization and simulations including three-dimensional effects.

关键词： Gamma-ray generation in plasmas High intensity laser-plasma interactions Strong electromagnetic field effects Radiation from moving charges

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Optimizing Laser Pulses for Narrowband Inverse Compton Sources in the High-Intensity Regime

arXiv

引用

arXiv 2019年

作者： Seipt, Daniel Yu. Kharin, Vasily Rykovanov, Sergey G. Helmholtz-Institut Jena Fröbelstieg 3 Jena07743 Germany Center for Ultrafast Optical Science University of Michigan Ann Arbor MI48109 United States Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobel Str. 3 Skolkovo Russia

Scattering of ultraintense short laser pulses off relativistic electrons allows one to generate a large number of X- or gamma-ray photons with the expense of the spectral width—temporal pulsing of the laser inevitable leads to considerable spectral broadening. In this Letter, we describe a simple method to generate optimized laser pulses that compensate the nonlinear spectrum broadening, and can be thought of as a superposition of two oppositely linearly chirped pulses delayed with respect to each other. We develop a simple analytical model that allow us to predict the optimal parameters of such a two-pulse—the delay, amount of chirp and relative phase—for generation of a narrowband γ-ray spectrum. Our predictions are confirmed by numerical optimization and simulations including 3D effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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