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Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

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Efficient Optimal Control of Open Quantum Systems

arXiv

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arXiv 2024年

作者： He, Wenhao Li, Tongyang Li, Xiantao Li, Zecheng Wang, Chunhao Wang, Ke Center for Computational Science and Engineering MIT United States School of Physics Peking University China Center on Frontiers of Computing Studies School of Computer Science Peking University China Department of Mathematics Pennsylvania State University United States Department of Computer Science and Engineering Pennsylvania State University United States

The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this paper, we present algorithms for solving this optimal control problem efficiently, i.e., having a poly-logarithmic dependency on the system dimension, which is exponentially faster than best-known classical algorithms. Our algorithms are hybrid, consisting of both quantum and classical components. The quantum procedure simulates time-dependent Lindblad evolution that drives the initial state to the final state, and it also provides access to the gradients of the objective function via quantum gradient estimation. The classical procedure uses the gradient information to update the control variables. At the technical level, we provide the first (to the best of our knowledge) simulation algorithm for time-dependent Lindbladians with an 1-norm dependence. As an alternative, we also present a simulation algorithm in the interaction picture to improve the algorithm for the cases where the time-independent component of a Lindbladian dominates the time-dependent part. On the classical side, we heavily adapt the state-of-the-art classical optimization analysis to interface with the quantum part of our algorithms. Both the quantum simulation techniques and the classical optimization analyses might be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum chemistry

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Multi-source unsupervised domain-adaptation for automatic sleep staging

Multi-source unsupervised domain-adaptation for automatic sl...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yangxin Zhu Shikui Tu Lin Zhang Lei Xu Department of Computer Science and Engineering Center for Cognitive Machines and Computational Health (CMaCH) Shanghai Jiao Tong University Shanghai China

Sleep staging using electroencephalogram (EEG) is of great significance for diagnosing sleep disorders. Recently, due to the high cost of manually annotating EEG signals and the problem of domain shift across different datasets, many unsupervised domain adaptation methods have been applied to sleep staging. However, all these methods are single-source unsupervised domain adaptation (SUDA) methods. When applied to the multi-source unsupervised domain adaptation (MUDA) problem, most SUDA methods treat all source domains equally, without considering the domain shift between them. As a result, achiving feature alignment across all domains becomes a challenge for these SUDA methods. In our proposed model, we address the challenge of domain shift in multi-source unsupervised domain adaptation (MUDA) for automatic sleep staging. Specially, we create a domain-specific branch for each pair of source and target domains, which focuses on aligning the features extracted from the respective domains using an adversarial-based domain adaptation approach. Additionally, we incorporate a domain-invariant branch into our model for learning the domain-invariant representation. We also introduce an adaptive-based mixing strategy to assigns weights to each branches, which can dynamically adjust the importance of each branch depending on their relevance to the specific prediction task. The experiments conducted on three public datasets show the superior performance of our model. Compared to other state-of-the-art MUDA models, our model’s average classification accuracy improves by 2% to 9.9%. The source code is available at https://***/CMACH508/MUDAEEG.

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Towards the Question of Packet Recovery at the Transport Layer

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Moscow University computational Mathematics and Cybernetics 2025年第1期49卷 16-29页

作者： Borisov, A. V. Gurov, S. I. Semenikhin, K. V. Smelyanskiy, R. L. Stepanov, E. P. Federal Research Center ‘‘Computer Science and Control’’ of the Russian Academy of Sciences Moscow Russia Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Moscow Aviation Institute (National Research University) Moscow Russia

A problem of using Forward Error Correction (FEC) codes at the transport layer for packet recovery is considered. This problem aims at avoiding multiple transmission of the same packet, reducing data transmission delay, and preventing a waste of network resources. The main idea of FEC coding methods is the introduction of redundancy into the transmitted data, which makes possible to recover lost data at the receiver end. The paper studies various FEC methods applied at the transport layer to select the most promising, in terms of computational complexity in encoding transmitted data and decoding it at the receiver end, as well as the effect of redundancy on data transmission delay and loss level in the transport connection. The redundancy level in the selected FEC schemes is evaluated taking into account the constraints on losses in the transport connection and quality-of-service characteristics.

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Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response 27th

Percolate: An Exponential Family JIVE Model to Design DNA-B...

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27th International Conference on Research in computational Molecular Biology, RECOMB 2023

作者： Mourragui, Soufiane M. C. Loog, Marco van Nee, Mirrelijn de Wiel, Mark A van Reinders, Marcel J. T. Wessels, Lodewyk F. A. Computational Cancer Biology Division of Molecular Carcinogenesis Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Faculty of EEMCS Delft University of Technology Delft Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Epidemiology and Biostatistics Amsterdam Public Health research institute Amsterdam University medical centers Amsterdam Netherlands MRC Biostatistics Unit University of Cambridge Cambridge United Kingdom Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands

ISBN: (纸本)9783031291180

Motivation: Anti-cancer drugs may elicit resistance or sensitivity through mechanisms which involve several genomic layers. Nevertheless, we have demonstrated that gene expression contains most of the predictive capacity compared to the remaining omic data types. Unfortunately, this comes at a price: gene expression biomarkers are often hard to interpret and show poor robustness. Results: To capture the best of both worlds, i.e. the accuracy of gene expression and the robustness of other genomic levels, such as mutations, copy-number or methylation, we developed Percolate, a computational approach which extracts the joint signal between gene expression and the other omic data types. We developed an out-of-sample extension of Percolate which allows predictions on unseen samples without the necessity to recompute the joint signal on all data. We employed Percolate to extract the joint signal between gene expression and either mutations, copy-number or methylation, and used the out-of sample extension to perform response prediction on unseen samples. We showed that the joint signal recapitulates, and sometimes exceeds, the predictive performance achieved with each data type individually. Importantly, molecular signatures created by Percolate do not require gene expression to be evaluated, rendering them suitable to clinical applications where only one data type is available. Availability: Percolate is available as a Python 3.7 package and the scripts to reproduce the results are available here. © 2023, The Author(s).

关键词： Gene expression

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Predicting Compound-Protein Interaction by Deepening the Systemic Background via Molecular Network Feature Embedding

Predicting Compound-Protein Interaction by Deepening the Sys...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Han Zhu, Hangxu Li, Wenhao Liu, Ming Zhang, Li Xu, Dong Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China Changchun University of Technology School of Mathematics and Statistics School of Computer Science and Engineering Changchun China University of Missouri Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9781665468190

Identifying compound-protein interactions (CPI) is crucial for drug screening, drug repurposing, and combination therapy studies. The performance of CPI prediction depends heavily on the features extracted from compounds and target proteins. The existing prediction methods use different feature combinations, but both molecular-based and network-based models have the problem of incomplete feature representations. Therefore, completely integrating the relevant features of CPI would be an effective way to solve the existing problem. This study proposed a novel model named MCPI, which integrated the PPI (protein-protein interaction) network, CCI (compound-compound interaction) network, and structure features of CPI to improve prediction performance. We compared our model with other existing methods for predicting CPI on public datasets. The experimental results showed that MCPI outperformed the peer methods. In addition, in response to the SARS-CoV-2 pandemic, we applied the model to search for potential inhibitors among FDA-approved drugs and validated the prediction results through the literature. This work may also provide potential guidance for drug development. © 2022 IEEE.

关键词： Coronavirus

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A Machine Learning Approaches in Sustainable Healthcare for Predicting Lumpy Skin Disease 2nd

A Machine Learning Approaches in Sustainable Healthcare for ...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Alankar, Bhavya Chauhan, Ritu Mishra, Aarushi Jena, Mehak Kaur, Harleen Center for Computational Biology and Bioinformatics Amity University UP Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

The integration of machine learning in healthcare has transformed disease prognosis and diagnosis. Healthy livestock contributes to a better environment, sustainable resource use, and the prosperity and expansion of rural communities. Various machine-learning techniques have been used in tasks such as disease prediction, medical image analysis, and drug discovery. These techniques have the potential to improve diagnostics, treatment strategies, and patient outcomes. Machine learning algorithms, using diverse data sources, enable early detection and tailored interventions. Ongoing research aims to use machine learning algorithms in healthcare databases to identify effective patterns for future predictive modeling. This study investigates machine learning algorithms and descriptive analysis methodologies to determine an individual's diabetic status. Four machine learning classifiers, including K Nearest Neighbour, Support Vector Machine, Decision Tree, and Random Forest, are examined, and compared. These classifiers show potential in enhancing diabetes prediction and improving sustainable healthcare decision-making with respective accuracies of 81.16%, 84.84%, 77.16%, and 89.83%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training computer vision Accuracy Perturbation methods Neural networks Closed box computer architecture Robustness Ensemble learning Standards

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An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression

arXiv

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arXiv 2024年

作者： Luo, Shiyi Liu, Mingshuo Sun, Pu Yu, Yifeng Ren, Shangping Bai, Yu Computational Science Research Center San Diego State University San Diego United States Computational Science Research Center University of California Irvine Irvine United States Department of Electrical and Computer Engineering University of California Davis Davis United States Department of Mathematics University of California Irvine Irvine United States Department of Computer Science San Diego State University United States Department of Electrical and Computer Engineering California State University Fullerton Fullerton United States

In the field of model compression, choosing an appropriate rank for tensor decomposition is pivotal for balancing model compression rate and efficiency. However, this selection, whether done manually or through optimization-based automatic methods, often increases computational complexity. Manual rank selection lacks efficiency and scalability, often requiring extensive trial-and-error, while optimization-based automatic methods significantly increase the computational burden. To address this, we introduce a novel, automatic, and budget-aware rank selection method for efficient model compression, which employs Layer-Wise Imprinting Quantitation (LWIQ). LWIQ quantifies each layer's significance within a neural network by integrating a proxy classifier. This classifier assesses the layer's impact on overall model performance, allowing for a more informed adjustment of tensor rank. Furthermore, our approach includes a scaling factor to cater to varying computational budget constraints. This budget awareness eliminates the need for repetitive rank recalculations for different budget scenarios. Experimental results on the CIFAR-10 dataset show that our LWIQ improved by 63.2% in rank search efficiency, and the accuracy only dropped by 0.86% with 3.2x less model size on the ResNet-56 model as compared to the state-of-the-art proxy-based automatic tensor rank selection *** Codes 68T10, 65K10 Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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