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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological data Analysis

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Journal of Chemical Information and Modeling 2025年第11期65卷 5567-5575页

作者： Koseki, Jun Motono, Chie Yanagisawa, Keisuke Kudo, Genki Yoshino, Ryunosuke Hirokawa, Takatsugu Imai, Kenichiro Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Integrated Research Center for Self-Care Technology (irc-sct) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) Waseda University Shinjuku-ku Tokyo 169-8555 Japan Department of Computer Science School of Computing Institute of Science Tokyo Tokyo 152-8550 Japan Middle Molecule IT-based Drug Discovery Laboratory (MIDL) Institute of Science Tokyo Tokyo 152-8550 Japan Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba Ibaraki 305-8571 Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba Ibaraki 305-8575 Japan Transborder Medical Research Center University of Tsukuba Ibaraki 305-8577 Japan Global Research and Development Center for Business By Quantum-AI Technology (G-QuAT) National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8560 Japan

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein’s conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy. © 2025 The Authors. Published by American Chemical Society.

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NIMG-27. RADIOMETHYLOMICS FOR NON-INVASIVE PREDICTION OF TUMOR PHENOTYPES AND SURVIVAL IN GLIOBLASTOMA

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii200–viii201页

作者： Matsumoto, Yuji Singh, Omkar Garcia, Jose Kwak, Sunwoo Abdullaev, Zied Freeburg, Nelson Silverbush, Dana Aldape, Kenneth Davatzikos, Christos Nasrallah, MacLean Center for AI and Data Science for Integrated Diagnostics Philadelphia USA Laboratory of Pathology National Cancer Institute National Institutes of Health Bethesda USA Department of Cancer Biology University of Pennsylvania Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA

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EPCO-15. INTRA-TUMORAL HETEROGENEITY OF DNA METHYLATION PROFILING AND CELLULAR COMPOSITION IN GLIOBLASTOMA

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii4-viii4页

作者： Matsumoto, Yuji Singh, Omkar Garcia, Jose Kwak, Sunwoo Abdullaev, Zied Freeburg, Nelson Silverbush, Dana Aldape, Kenneth Davatzikos, Christos Nasrallah, MacLean Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia USA Laboratory of Pathology National Cancer Institute National Institutes of Health Bethesda USA Department of Cancer Biology University of Pennsylvania Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA

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Building an Ethical and Trustworthy Biomedical ai Ecosystem for the Translational and Clinical Integration of Foundational Models

arXiv

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arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical ai ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical ai ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical ai pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical ai community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical ai ecosystem. © 2024, CC BY.

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Scalable Multiscale-Spectral Gfem with an Application to Composite Aero-Structures

SSRN

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SSRN 2023年

作者： Bénézech, Jean Seelinger, Linus Bastian, Peter Butler, Richard Dodwell, Timothy Ma, Chupeng Scheichl, Robert Centre for Integrated Materials Processes and Structures University of Bath BathBA2 7AY United Kingdom Institute for Applied Mathematics Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany Institute of Data Science and AI University of Exeter UK & digiLab Exeter Exeter United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom Institute of Scientific Research Great Bay University Dongguan China

In this paper, the first large-scale application of multiscale-spectral generalized finite element methods (MS-GFEM) to composite aero-structures is presented. The crucial novelty lies in the introduction of A-harmonicity in the local approximation spaces, which in contrast to [Babuska, Lipton,Multiscale Model. Simul.9,[Babuska, Lipton, Multiscale Model. Simul. 9, 2011] is enforced more efficiently via a constraint in the local eigenproblems. This significant modification leads to excellent approximation properties, which turn out to be essential to capture accurately material strains and stresses with a low dimensional approximation space, hence maximising model order reduction. The implementation of the framework in the DUNE software package, as well as a detailed description of all components of the method are presented and exemplified on a composite laminated beam under compressive loading. The excellent parallel scalability of the method, as well as its superior performance compared to the related, previously introduced GenEO method are demonstrated on two realistic application cases, including a C-shaped wing spar with complex geometry. Further, by allowing low-cost approximate solves for closely related models or geometries this efficient, novel technology provides the basis for future applications in optimisation or uncertainty quantification on challenging problems in composite aero-structures. © 2023, The Authors. All rights reserved.

关键词： Finite element method

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High-Frequency Stereo Matching Network

High-Frequency Stereo Matching Network

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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IMG-09. A DEEP LEARNING-BASED APPROACH FOR BRaiN TISSUE EXTRACTION USING MULTI- AND SINGLE-PARAMETRIC MRI IN PEDIATRICS

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Neuro-Oncology 2024年第Supplement_4期26卷 0–0页

作者： Deep B Gandhi Anurag Gottipati Wenxin Tu Ariana Familiar Shuvanjan Haldar Neda Khalili Paarth Jain Karthik Viswanathan Phillip B Storm Adam C Resnick Jeffrey B Ware Arastoo Vossough Ali Nabavizadeh Anahita Fathi Kazerooni Center for Data-Driven Discovery in Biomedicine (D3b) The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Neurosurgery Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurosurgery The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Division of Radiology Children’s Hospital of Philadelphia Philadelphia PA USA AI2D Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia PA USA

BACKGROUND Skull-stripping, the process of extracting brain tissue from MR images, is an important step for tumor segmentation and downstream imaging-based analytics such as ai-powered radiomic feature extraction. Existing skull-stripping models, designed for pediatric or adult patients, show limitations in accurately segmenting tumors in sellar/suprasellar regions. This limitation hinders their reliable application across different histologies of pediatric brain tumors. We propose a deep learning approach for fully automated skull-stripping, compatible with both single- or multi-parametric MRI sequences.

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Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

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Nature communications 2025年第1期16卷 4871页

作者： Filippos Anagnostakis Sarah Ko Mehrshad Saadatinia Jingyue Wang Christos Davatzikos Junhao Wen Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY USA. Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for AI and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY USA. junhao.wen89@***. Department of Radiology Columbia University New York NY USA. junhao.wen89@***. New York Genome Center (NYGC) New York NY USA. junhao.wen89@***. Department of Biomedical Engineering (BME) Columbia University New York NY USA. junhao.wen89@***. Data Science Institute (DSI) Columbia University New York NY USA. junhao.wen89@***. Zuckerman Institute Columbia University New York NY USA. junhao.wen89@***. Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID) Department of Radiology Columbia University New York NY USA. junhao.wen89@***.

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10/5). Using SBayesS, we estimate the SNP-based heritability (0.09< < 0.18), negative selection signatures (-0.93 < S < -0.76), and polygenicity (0.001ailable at https://***/medicine/ .

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NIMG-65. SYNTHETIC MRI GENERATION TO FACILITATE AUTOSEGMENTATION OF PEDIATRIC BRaiN TUMORS IN LIMITED data SCENARIOS

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii210–viii210页

作者： Chrysochoou, Dimosthenis Gandhi, Deep Familiar, Ariana Vossough, Arastoo Storm, Phillip Resnick, Adam Davatzikos, Christos Nabavizadeh, Ali Kazerooni, Anahita Fathi Department of Bioengineering University of Pennsylvania Philadelphia USA Center for Data-Driven Discovery in Biomedicine (D3b) The Children’s Hospital of Philadelphia Philadelphia USA Division of Radiology Children’s Hospital of Philadelphia Philadelphia USA Department of Neurosurgery Perelman School of Medicine University of Pennsylvania Philadelphia USA Department of Neurosurgery The Children’s Hospital of Philadelphia Philadelphia USA AI2D Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA

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