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Assessing in silico Tools for Accurate Pathogenicity Prediction in CHD Nucleosome Remodelers

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SSRN

SSRN 2025年

作者： Rabouhi, Nazim Guindon, Simon Coleman, Emilia Aisha van Heesbeen, H.J. Greenwood, Celia M.T. Lu, Tianyuan Campeau, Philippe M. CHU Sainte-Justine Research Center University of Montreal MontrealQC Canada Gina Cody School of Engineering and Computer Science Concordia University MontrealQC Canada Department of Informatics UQAM University MontrealQC Canada Lady Davis Institute for Medical Research Jewish General Hospital Montreal Canada Gerald Bronfman Department of Oncology Department of Epidemiology Biostatistics & Occupational Health McGill University MontrealQC Canada Department of Population Health Sciences School of Medicine and Public Health University of Wisconsin–Madison MadisonWI United States Department of Biostatistics and Medical informatics School of Medicine and Public Health University of Wisconsin-Madison MadisonWI United States

Chromodomain Helicase DNA-binding (CHD) proteins compose a family of chromatin remodelers that play crucial roles in DNA repair and gene expression regulation, neural stem cell differentiation and chromatin integrity. Genetic variants in CHD chromatin remodelers are associated with neurodevelopmental disorders with features like autism spectrum disorder and intellectual disability. Consequently, the determination of variant pathogenicity in clinical genetic tests for individuals bearing CHD variants is crucial. In this study, we compared the efficiency of multiple pathogenicity prediction tools, which are valuable resources for the identification and annotation of potentially disease-causing variants, to assess the most accurate in silico tool capable of distinguishing pathogenic CHD variants from benign ones. We have focused specifically on genes that share high structural and functional similarity and are strongly linked to pathogenic mutations. Here, we evaluated a range of pathogenicity prediction tools and compared their output with pathogenicity conclusions reported in the literature and genomic databases. Our findings showed that the top performing tools were BayesDel, ClinPred, AlphaMissense, ESM-1b and SIFT. BayesDel, specifically with its addAF component, was overall the most robust tool for CHD variant pathogenicity prediction. We also suggest incorporating SnpEff’s high-impact variant identification capabilities for the development of a hybrid tool that would enhance the classification of CHD variants. Our study emphasizes the need for continuous evaluation and integration of updated prediction tools, including emerging AI approaches. This research also emphasizes the importance of gathering better clinical and mechanistic data on the deleteriousness of pathogenic variants to improve clinical diagnostics’ accuracy. © 2025, The Authors. All rights reserved.

关键词： Gene expression regulation

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

A quadratic lower bound for algebraic branching programs 20

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A quadratic lower bound for algebraic branching programs

35th Computational Complexity Conference, CCC 2020

作者： Chatterjee, Prerona Kumar, Mrinal She, Adrian Volk, Ben Lee Tata Institute of Fundamental Research Mumbai India Department of Computer Science and Engineering IIT Bombay India Department of Computer Science University of Toronto Canada Center for the Mathematics of Information California Institute of Technology PasadenaCA United States

ISBN: (纸本)9783959771566

We show that any Algebraic Branching Program (ABP) computing the polynomial Pni=1 xni has at least Ω(n2) vertices. This improves upon the lower bound of Ω(n log n), which follows from the classical result of Baur and Strassen [24, 1], and extends the results of Kumar [13], which showed a quadratic lower bound for homogeneous ABPs computing the same polynomial. Our proof relies on a notion of depth reduction which is reminiscent of similar statements in the context of matrix rigidity, and shows that any small enough ABP computing the polynomial Pni=1 xni can be depth reduced to essentially a homogeneous ABP of the same size which computes the polynomial Pni=1 xni + Ε(x), for a structured "error polynomial" Ε(x). To complete the proof, we then observe that the lower bound in [13] is robust enough and continues to hold for all polynomials Pni=1 xni + Ε(x), where Ε(x) has the appropriate structure. © Prerona Chatterjee, Mrinal Kumar, Adrian She, and Ben Lee Volk;licensed under Creative Commons License CC-BY 35th Computational Complexity Conference (CCC 2020).

关键词： Polynomials

Introduction of fog computations to measuring system collection and accounting of data of distributed internet of things

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Introduction of fog computations to measuring system collect...

2021 International Conference on Automatics and Energy, ICAE 2021

作者： Munister, V.D. Zolkin, A.L. Aygumov, T.G. Ozhiganov, V.E. Mariupol State University Department of Enterprise Economics State Educational Institution of Higher Professional Education Donetsk National Technical University Ukraine Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Russia Department of Computer Engineering Software and Automated Systems Dagestan State Technical University Russia Federal State Unitary Enterprise Russian Federal Nuclear Center All-Russian Research Institute of Experimental Physics FSUE RFNC VNIIEF Russia

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device's behaviour at a given time *** practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Game theory

A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

Dynamical process of a bit-width reduced Ising model with simulated annealing

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arXiv 2023年

作者： Kikuchi, Shuta Togawa, Nozomu Tanaka, Shu Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q Keio University Tokyo108-8345 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Research Frontiers Initiative Tokyo Institute of Technology Tokyo108-0023 Japan

Ising machines have attracted attention as efficient solvers for combinatorial optimization problems, which are formulated as ground-state (lowest-energy) search problems of the Ising model. Due to the limited bit-width of coefficients on Ising machines, the Ising model must be transformed into a bit-width reduced (BWR) Ising model. According to previous research, the bit-width reduction method, which adds auxiliary spins, ensures that the ground state of the BWR Ising model is theoretically the same as the Ising model before bit-width reduction (original Ising model). However, while the dynamical process is closely related to solution accuracy, how the BWR Ising model progresses towards the ground state remains to be elucidated. Therefore, we compared the dynamical processes of these models using simulated annealing (SA). Our findings reveal significant differences in the dynamical process across models. Analysis from the viewpoint of statistical mechanics found that the BWR Ising model has two characteristic properties: an effective temperature and a slow relaxation. These properties alter the temperature schedule and spin flip probability in the BWR Ising model, leading to differences in the dynamical process. Therefore, to obtain the same dynamical process as the original Ising model, we proposed SA parameters for the BWR Ising model. We demonstrated the proposed SA parameters using a square lattice Ising model, in which all coefficients were set uniformly to the same positive values or randomly. Our experimental evaluations demonstrated that the dynamical process of the BWR and original Ising model became closer. © 2023, CC BY.

关键词： Simulated annealing

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

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arXiv 2024年

作者： Zhang, Xiaoge Wang, Tao Yan, Chao Najdawi, Fedaa Zhou, Kai Ma, Yuan Cheung, Yiu-Ming Malin, Bradley A. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN United States Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Department of Mechanical Engineering Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computer Science Hong Kong Baptist University Kowloon Tong Kowloon Hong Kong Department of Computer Science Vanderbilt University NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings. © 2024, CC BY.

关键词： Lung cancer

Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

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Matching Using Sufficient Dimension Reduction for Heterogene...

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 IEEE.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

Roadmap of spin-orbit torques

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arXiv 2021年

作者： Shao, Qiming Li, Peng Liu, Luqiao Yang, Hyunsoo Fukami, Shunsuke Razavi, Armin Wu, Hao Wang, Kang Freimuth, Frank Mokrousov, Yuriy Stiles, Mark D. Emori, Satoru Hoffmann, Axel Åkerman, Johan Roy, Kaushik Wang, Jian-Ping Yang, See-Hun Garello, Kevin Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Auburn University Electrical Engineering and Computer Science Massachusetts Institute of Technology Department of Electrical and Computer Engineering National University of Singapore Research Institute of Electrical Communication Tohoku University Japan Department of Electrical and Computer Engineering University of California Los Angeles United States Forschungszentrum Juelich GmbH University of Mainz Alternative Computing Group National Institute of Standards and Technology Department of Physics Virginia Tech Department of Materials Science and Engineering University of Illinois Urbana-Champaign Physics Department University of Gotherburg Department of Electrical and Computer Engineering Purdue University Electrical and Computer Engineering Department University of Minnesota IBM Research - Almaden United States IMEC Leuven Belgium CEA-Spintec Grenoble France Physics Department Oakland University

Spin-orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin-orbit coupling, conduction electron spins and magnetization. More recently interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this Roadmap paper, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, two-dimensional materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers, such as magnetic insulators, antiferromagnets, and ferrimagnets. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three-terminal and two-terminal SOT-magnetoresistive random-access memories (MRAMs);we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain wall and skyrmion racetrack memories. This paper aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors. © 2021, CC BY.

关键词： Spin Hall effect