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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： An, Congcong Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data. © 2023 IEEE.

关键词： clustering generative adversarial networks single cell multi-omics data unsupervised learning

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Wen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything"to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA's LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS's unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes. © 2023 IEEE.

关键词： Multi-omics Data Pathological Images Segment Anything Survival Analysis

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

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Journal of systems Science and systems engineering 2022年第2期31卷 133-149页

作者： Haixing Wu Shunfu Jin Wuyi Yue School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China Department of Intelligence and Informatics Konan UniversityKobe 658-8501Japan

In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate ***,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system *** to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is *** constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the ***,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are ***,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user ***,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.

关键词： Cognitive radio networks dynamic spectrum scheme batch arrival impatient packets Markov chain social optimization

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Yijia Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. © 2023 IEEE.

关键词： deep learning multimodal pathogenicity protein structure variant

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Prediction of diabetes and hypertension using multi-layer perceptron neural networks

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 120-137页

作者： Hani Bani-Salameh Shadi MAlkhatib Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod Ahed J Alkhatib Department of Software Engineering The Hashemite University ZarqaJordan Department of Computer Information System The Hashemite University ZarqaJordan Department of Legal Medicine School of Medicine JUSTJordan

Background:Diabetes and hypertension are two of the commonest diseases in the *** they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in ***:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these ***:This work used two online datasets which consist of data collected from 768 *** applied neural network algorithms to predict if the individuals have those two diseases based on some *** prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and ***:A model based on the Multi-Layer Perceptron Neural Network(MLP)was *** inputs of the network were the factors for each disease,while the output was the prediction of the disease’s *** model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of ***,a tool was implemented to help diagnose the diseases and to understand the ***:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for *** results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and *** shows MLPs effectiveness in early disease prediction.

关键词： Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools

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Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease 1

Implementation Learning Vector Quantization Using Neural Net...

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1st Lekantara Annual Conference on engineering and Information Technology, LiTE 2021

作者： Samsir, Samsir Daulay, Nelly Khairani Harahap, Syaiful Zuhri Zalmi, Wahyuni Fithratul Sari, Afni Nia Nasution, Fitri Aini Watrianthos, Ronal Department of Informatics Engineering Universitas Al Washliyah Labuhanbatu Indonesia Department of Computer System Universitas Bina Insan Indonesia Department of Information System Universitas Labuhanbatu Indonesia Department of Information System Universitas Putra Indonesia Padang Indonesia Department of Software Engineer Universitas Dharmawangsa Medan Indonesia

This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors were fever, tiredness, nausea, breathing pain, sore throat, hearing loss, allergies, chills and sweating, and thick and transparent mucus. The factors studied were ear canal infections, pharyngitis of the neck, throat, nose, and sinusitis. The findings revealed that ten neurons with an objective value of 0.01 in the learning rate range of 0.01 - 0.05 resulted in categorizing snoring, nose, and ear disorders, including the input layer. The MATLAB program is utilized in this approach, with an average accuracy of 67 per cent and a mean square error of 0.2. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms 13

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time. © 2024 IEEE.

关键词： Optimization algorithms

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WIP: An Engaging Undergraduate Intro to Model Checking in software engineering Using TLA+

WIP: An Engaging Undergraduate Intro to Model Checking in So...

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Frontiers in Education (FIE) Conference

作者： Konstantin Läufer Gunda Mertin George K. Thiruvathukal Department of Computer Science Software and Systems Laboratory Loyola University Chicago Institute for Software Engineering and Programming Languages University of Lübeck

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+})$ , into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive “failuresy” through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding $\text { TLA }^{+}$ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in $\text { TLA }^{+}$ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudi

关键词： computer science Open Access Model checking system recovery software Safety Logic Reliability Proposals software engineering

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Vulnerability severity prediction model for software based on Markov chain

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International Journal of Information and computer Security 2021年第2-3期15卷 109-140页

作者： Jabeen, Gul Yang, Xi Luo, Ping The Key Laboratory for Information System Security School of Software Tsinghua University Beijing China Department of Computer Science Karakoram International University Gilgit-Baltistan Pakistan

software vulnerabilities primarily constitute security risks. Commonalities between faults and vulnerabilities prompt developers to utilise traditional fault prediction models and metrics for vulnerability prediction. Although traditional models can predict the number of vulnerabilities and their occurrence time, they fail to accurately determine the seriousness of vulnerabilities, impacts, and severity level. To address these deficits, we propose a method for predicting software vulnerabilities based on a Markov chain model, which offers a more comprehensive descriptive model with the potential to accurately predict vulnerability type, i.e., the seriousness of the vulnerabilities. The experiments are performed using real vulnerability data of three types of popular software: Windows 10, Adobe Flash Player and Firefox. Our model is shown to produce accurate predictive results. © 2021 Inderscience Enterprises Ltd.

关键词： Forecasting

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