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MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery

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Fundamental Research 2025年

作者： Lan, Wei Tang, Zhentao Liao, Haibo Chen, Qingfeng Chen, Yi-Ping Phoebe Zhang, Zhaolei Wang, Jianxin Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Guangxi 530004 China Department of Computer Science and Information Technology La Trobe University 3086 VIC Australia Donnelly Centre Department of Computer Science and Department of Molecular Genetics University of Toronto M5S 1A1 ON Canada School of Computer Science and Engineering Central South University Hunan 410083 China

Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence. © 2025

关键词： Biomarker discovery Cancer recurrence Deep learning Interpretability Multi-omics data

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Self-adjustable hyper-graphs for video pose estimation based on spatial-temporal subspace construction

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science China(Information sciences) 2022年第3期65卷 260-262页

作者： Jizhou MA Shuai LI Hong QIN Aimin HAO Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science Stony Brook University

Dear editor,In recent years many supervised video pose estimation methods have achieved growing successes based on well-labeled training datasets. Nonetheless, when facing roughly-labeled training data, it still remains challenging to intrinsically encode the video contents' spatial-temporal coherency for robust video pose *** researches aimed to directly improve and refine the existing confidence maps by combining the spatial-temporal structure models [1, 2]. Li et al.

关键词： Video Pose Estimation Self-adjustable Hyper-graph Spatial-temporal Subspace Exploration Maximum Matching Subspace Operator Action Pattern

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Sed-Dedup: An efficient secure deduplication system with data modifications

Sed-Dedup: An efficient secure deduplication system with dat...

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作者： Tian, Wenlong Li, Ruixuan Xu, Cheng-Zhong Xu, Zhiyong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Electrical and Computer Engineering Wayne State University DetroitMI United States State Key Laboratory of IoTSC and Department of Computer Science University of Macau China Math and Computer Science Department Suffolk University BostonMA United States Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China

The amount of outsourced data grows rapidly. In recent years, cloud service providers integrate data deduplication systems with convergent encryption (CE) methods, in which a file encryption key is determined by its own content instead of the secret of a specific user, to save the storage cost and ensure the security of outsourced data. However, present secure deduplication systems failed to deal with data modifications efficiently. We observe that when a client makes small changes on an existing file, the current chunking algorithms cannot effectively detect the similarities and always create chunks with largely overlapped contents. It reduces data deduplication ratios and results in unnecessary overhead. In this paper, we propose Sed-Dedup, an efficient secure delta encoding deduplication system to address this problem. In Sed-Dedup, we introduce a novel delta encoding approach to store modified contents in delta files and leave the original files intact. Two schemes with different encoding policies are designed. Both of them can solve the issue and improve the secure deduplication performance. To evaluate the performance, we implement a prototype and conduct extensive experiments based on synthetic and real-world datasets. Our experimental results show that Sed-Dedup is superior to the state-of-the-art secure deduplication systems. © 2019 John Wiley & Sons, Ltd.

关键词： Cryptography

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Objective evaluation of relighting models on translucent materials from multispectral RTI images

Objective evaluation of relighting models on translucent mat...

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2021 Material Appearance Conference, MAAP 2021

作者： Kitanovski, Vlado Hardeberg, Jon Yngve The Norwegian Color and Visual Computing Laboratory Department of Computer Science NTNU - Norwegian University of Science and Technology Gjøvik Norway

In this paper, we evaluate the quality of reconstruction i.e. relighting from images obtained by a newly developed multispectral reflectance transformation imaging (MS-RTI) system. The captured MS-RTI images are of objects with different translucency and color. We use the most common methods for relighting the objects: polynomial texture mapping (PTM) and hemispherical harmonics (HSH), as well as the recent discrete model decomposition (DMD). The results show that all three models can reconstruct the images of translucent materials, with the reconstruction error varying with translucency but still in the range of what has been reported for other non-translucent materials. DMD relighted images are marginally better for the most transparent objects, while HSH- and PTM- relighted images appear to be better for the opaquer objects. The estimation of the surface normals of highly translucent objects using photometric stereo is not very accurate. Utilizing the peak of the fitted angular reflectance field, the relighting models, especially PTM, can provide more accurate estimation of the surface normals. © 2021 Society for Imaging science and Technology. All rights reserved.

关键词： Reflection

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Multi-Target Rydberg Gates via Spatial Blockade Engineering

arXiv

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arXiv 2025年

作者： Stein, Samuel Liu, Chenxu Kan, Shuwen Crane, Eleanor Ding, Yufei Mao, Ying Schuckert, Alexander Li, Ang Future Computing Research Group Pacific Northwest National Laboratory United States Department of Computer and Information Science Fordham University United States Department of Physics King’s College London Strand LondonWC2R 2LS United Kingdom Department of Computer Science & Engineering University of California San Diego United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science University of Maryland and NIST United States

Multi-target gates offer the potential to reduce gate depth in syndrome extraction for quantum error correction. Although neutral-atom quantum computers have demonstrated native multi-qubit gates, existing approaches that avoid additional control or multiple atomic species have been limited to single-target gates. We propose single-control-multi-target C(Z⊗N) gates on a single-species neutral-atom platform that require no extra control and have gate durations comparable to standard CZ gates. Our approach leverages tailored interatomic distances to create an asymmetric blockade between the control and target atoms. Using a GPU-accelerated pulse synthesis protocol, we design smooth control pulses for CZZ and CZZZ gates, achieving fidelities of up to 99.55% and 99.24%, respectively, even in the presence of simulated atom placement errors and Rydberg-state decay. This work presents a practical path to implementing multi-target gates in neutral-atom systems, significantly reducing the resource overhead for syndrome extraction. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Enhanced Traffic Sign Recognition Using Deep Learning Techniques

Enhanced Traffic Sign Recognition Using Deep Learning Techni...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Dilshad Islam Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Dept. of CSE Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Traffic sign recognition plays a pivotal role in modern intelligent transportation systems, contributing significantly to traffic management and road safety. This thesis presents a comprehensive investigation into the utilization of deep learning techniques for enhanced traffic sign recognition. The research delves into edge detection methodologies, deep learning architectures, and classification techniques specifically tailored for the identification of traffic and road signs. A diverse range of deep learning models, including Convolutional Neural Networks (CNN), VGG19, ResNet50, ResNet101, and ResNet152, are scrutinized for their effectiveness in traffic sign classification. The evaluation is conducted on a comprehensive dataset encompassing various road signs captured under diverse environmental conditions. The classification pipeline integrates edge detection algorithms such as Canny, Sobel, and Prewitt in conjunction with the selected neural network models. Experimental results exhibit notable performance disparities among the evaluated architectures. CNN achieves the highest accuracy of 96% when combined with the Prewitt edge detection method, while VGG19 attains 95% accuracy under the same conditions. ResNet50 achieves a peak accuracy of 96% with the Prewitt edge detection technique, while ResNet101 demonstrates the capability to achieve 97% accuracy when utilizing the Canny edge detection method. Remarkably, ResNet152 emerges as the top-performing model, achieving an impressive accuracy rate of 98% when employing the Sobel edge detection method, along with an exceptional F1-Score.

关键词： Deep learning Accuracy Image edge detection Pipelines Neural networks computer architecture Road safety Convolutional neural networks Intelligent transportation systems Residual neural networks

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PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

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Analytical Chemistry 2024年第12期96卷 4835-4844页

作者： Lin, Yi-Ching Chien, Wei-Chen Wang, Yu-Xuan Wang, Ying-Hau Yang, Feng-Shuo Tseng, Li-Ping Hung, Jui-Hung Department of Laboratory Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung807 Taiwan Department of Laboratory Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Doctoral Degree Program of Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung807 Taiwan Department of Computer Science National Yang Ming Chiao Tung University HsinChu300 Taiwan Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung807 Taiwan Program in Biomedical Artificial Intelligence National Tsing Hua University HsinChu300 Taiwan

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS2MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS2MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS2MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS2MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts. © 2024 The Authors. Published by American Chemical Society.

关键词： Mass spectrometry

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Analysis of evolutionary algorithms on fitness function with time-linkage property (hot-off-the-press track at GECCO 2021) 21

Analysis of evolutionary algorithms on fitness function with...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Zheng, Weijie Chen, Huanhuan Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781450383516

In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions. Although the rigorous theoretical analysis on evolutionary algorithms has rapidly developed in the last two decades, it remains an open problem to theoretically understand the behaviors of evolutionary algorithms on time-linkage problems. This paper takes the first step towards the rigorous analyses of evolutionary algorithms for time-linkage functions. Based on the basic OneMax function, we propose a time-linkage function where the first bit value of the last time step is integrated but has a different preference from the current first bit. We prove that with probability 1 - o(1), randomized local search and (1 + 1) EA cannot find the optimum, and with probability 1 - o(1), (+ 1) EA is able to reach the optimum. This paper for the Hot-off-the-Press track at GECCO 2021 summarizes the work "Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property"by W. Zheng, H. Chen, and X. Yao, which has been accepted for publication in the IEEE Transactions on Evolutionary Computation 2021 [19]. © 2021 Owner/Author.

关键词： Evolutionary algorithms

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Segmented Merge: A New Primitive for Parallel Sparse Matrix Computations 17th

Segmented Merge: A New Primitive for Parallel Sparse Matrix...

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Ji, Haonan Lu, Shibo Hou, Kaixi Wang, Hao Liu, Weifeng Vinter, Brian Super Scientific Software Laboratory Department of Computer Science and Technology China University of Petroleum-Beijing Beijing China Department of Computer Science Virginia Tech Blacksburg United States Department of Computer Science and Engineering The Ohio State University Columbus United States Faculty of Technical Sciences Aarhus University Aarhus Denmark

ISBN: (纸本)9783030794774

Segmented operations, such as segmented sum, segmented scan and segmented sort, are important building blocks for parallel irregular algorithms. We in this work propose a new parallel primitive called segmented merge. Its function is in parallel merging q sub-segments to p segments, both of nonuniform lengths. We implement the segmented merge primitive on GPUs and demonstrate its efficiency on parallel sparse matrix transposition (SpTRANS) and sparse matrix-matrix multiplication (SpGEMM) operations. © 2021, IFIP International Federation for Information Processing.

关键词： graphics processing unit

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A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes

arXiv

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arXiv 2023年

作者： Raj, Akhilesh Perarnau, Swann Gokhale, Aniruddha Department of Electrical Engineering Vanderbilt University NashvilleTN United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Department of Computer Science Vanderbilt University NashvilleTN United States

As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data centers will continue to grow. A common approach to reducing this energy demand is to limit the power consumption of hardware components when workloads are experiencing bottlenecks elsewhere in the system. However, designing a resource controller capable of detecting and limiting power consumption on-the-fly is a complex issue and can also adversely impact application performance. In this paper, we explore the use of Reinforcement Learning (RL) to design a power capping policy on cloud compute nodes using observations on current power consumption and instantaneous application performance (heartbeats). By leveraging the Argo Node Resource Management (NRM) software stack in conjunction with the Intel Running Average Power Limit (RAPL) hardware control mechanism, we design an agent to control the maximum supplied power to processors without compromising on application performance. Employing a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a mathematical model of the compute nodes, we demonstrate and evaluate using the STREAM benchmark how a trained agent running on actual hardware can take actions by balancing power consumption and application performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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