检索结果-内蒙古大学图书馆

5th World conference on Smart Trends in Systems Security and Sustainability, WorldS4 2021

作者： Kalinin, Maxim O. Krundyshev, Vasiliy M. Peter the Great St.Petersburg Polytechnic University St.Petersburg Russia

ISBN: (纸本)9781665400961

Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope. © 2021 ieee.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Ensemble of Protein Stability upon Point Mutation Predictors 10th

Ensemble of Protein Stability upon Point Mutation Predictors

引用

10th Brazilian conference on Intelligent Systems, BRACIS 2021

作者： de Freitas, Eduardo Kenji Hasegawa Camargo, Alex Dias Balboni, Maurício Werhli, Adriano V. dos Santos Machado, Karina Computational Biology Laboratory - COMBI-LAB Centro de Ciências Computacionais Universidade Federal do Rio Grande - FURG Av. Itália km 8 RS Rio Grande Brazil

ISBN: (纸本)9783030916985

computational determination of protein stability upon point mutations is very useful in a wide field of applications. The reliability of such computational predictions is essential. Unfortunately, existing computational tools frequently disagree in their results. In the present study, the usage of Ensemble Learning Algorithms to aggregate the results from different stability prediction tools is investigated. Techniques of Stacking, Bagging, and Boosting as well as different Machine Learning algorithms as combiner function are explored. All the investigation is carried out in real dataset ProTherm for which experimental results are known. The proposed methodology was validated considering two different experiments according to the training set. Results show that our proposed ensemble approach is appropriate to predict the effect of point mutations on protein stability showing more reliable results than the individual tools improving overall accuracy, precision, and/or recall. © 2021, Springer Nature Switzerland AG.

关键词： Stability

来源：评论

学校读者我要写书评

暂无评论

Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Predicting COVID-19 Severity Using a Cut-and-Solve Feature S...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Kenneth Smith Michael Chan John Brandenburg Katarina A Jones Shawn R. Campagna Michael Garvin Alan R. Templeton Daniel Jacobson Carlos Cruchaga Sharlee Climer Department of Computer Science University of Missouri - St. Louis Saint Louis USA Optimization GotSport Jacksonville Beach USA Stord Department of Chemistry University of Tennessee Knoxville Knoxville USA Department of Chemistry The University of Tennessee Knoxville USA Computational Predictive Biology Oak Ridge National Laboratory Oak Ridge USA Department of Biology Washington University Saint Louis USA Biosciences Division Oak Ridge National Laboratory Oak Ridge USA Department of Psychiatry Washington University School of Medicine Saint Louis USA

Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Utilizing domain-based features to improve classification accuracy of biomedical text having bacterial associations

Utilizing domain-based features to improve classification ac...

引用

2021 ieee International conference on bioinformatics and Biomedicine, BIBM 2021

作者： Pokhrel, Vatsala Kuntal, Bhusan K. Srivastava, DIvyanshu Mande, Sharmila S. Das Baksi, Krishanu TCS-Research Tata Consultancy Services Ltd Pune India University of Lausanne Department of Computational Biology Lausanne Switzerland

ISBN: (纸本)9781665401265

Biomedical text can serve as a crucial source for obtaining a deeper understanding of the complexities pertaining to inter-bacterial associations. Existing methods are either based on bacterial co-occurrence observed in texts or rely on generic text mining methods, both of which may lead to extraction of limited amount of information. In this article, the efficiency of a multitude of generic text mining approaches for classifying text having bacterial associations have been first evaluated on a manually curated corpus. Further, a set of domain (or field of microbiology) specific features accompanied by three dictionaries relevant to bacterial associations were introduced. Incorporation of these domain-specific features in addition to generic text mining features from abstracts as well as sentences were shown to significantly improve the classification accuracy. © 2021 ieee.

关键词： Bacteria

来源：评论

学校读者我要写书评

暂无评论

Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

关键词：

来源：评论

学校读者我要写书评

暂无评论

APT: An Automated Probe Tracker From Gene Expression Data

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1864-1874页

作者： Kumar, Gautam Kumar, Rajnish Pal, Manoj Kumar Pramanik, Nilotpal Lahiri, Tapobrata Gupta, Ankita Pandey, Saket Indian Inst Informat Technol Allahabad Dept Appl Sci Comp Ctr 2 Room 4302 Allahabad 211015 Uttar Pradesh India

Out of currently available semi-automatic tools for detecting diagnostic probes relevant to a pathophysiological condition, ArrayMining and GEO2R of NCBI are most popular. The shortcomings of ArrayMining and GEO2R are that both tools list the probes ordering them on the basis of their individual statistical level of significances with only difference of statistical methods used by them. While the latest tool GEO2R outputs either top 250 or all genes following its own ranking mechanism, ArrayMining requires number of probes to be inputted by the user. This study provided a way for automatic selection of probe-set that can be obtained from the voting of outputs resulted from statistical methods, t-Test, Mann-Whitney Test and Empirical Bayes Moderated t-test. It was also intriguing to find that the parameters of these statistical methods can be represented as a mathematical function of group fisher's discriminant ratio of a disease-control expression data-pair. Result of this fully automatic method, APT shows 88.97 percent success in comparison to 80.40 and 87.60 percent successes of ArrayMining and GEO2R respectively to include reported probes. Furthermore, out of 10 fold cross validation and 5 new test cases, APT shows a better performance than both ArrayMining and GEO2R in regards to sensitivity and specificity.

关键词： Probes bioinformatics Genomics Gene expression Tools Diseases Arrays Pattern recognition automatic probe selection differential gene expression t-test Mann-Whitney test empirical Bayes moderated t-test Fisher's discriminant ratio non-linear polynomial regression curve fitting

来源：评论

学校读者我要写书评

暂无评论

An Explainable Intrusion Detection System 23

An Explainable Intrusion Detection System

引用

23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on Data Science and Systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yun Wang, Pan Wang, ZiXuan Cao, Mengting School of Modern Posts Nanjing University of Posts & Telecommunications Nanjing China

ISBN: (纸本)9781665494571

Malicious traffic detection has become a challenge in modern communications. As the research of intrusion detection systems becomes more and more in-depth, from machine learning to deep learning, the accuracy of intrusion detection continues to improve, the complexity of the model is getting higher and higher, and the interpretability of the model is lower. The model is like a black box, it is difficult for people to know the reason behind the decision. In fact, the interpretability of the model has made some achievements in the fields of biology, computer vision, and natural language processing. Therefore, in this article, for an intrusion detection system based on deep neural networks, we propose an interpretable artificial intelligence framework to enhance the transparency of machine learning decision-making, that is, which important features the model uses to determine attack or non attack. We use the SHAP method to explain, and combine local and global explanations to realize IDS interpretation in all directions. The partial explanation gives us the reason why the model makes a decision on a particular instance, which gives us a certain reference value. The global interpretation gives the important characteristics of the model as a whole, which helps to improve the transparency of IDS. We use the CICIDS2017 dataset to verify the feasibility of the intrusion detection system's two-classification experiment. Interpretability work can help network security personnel better understand the decision-making of intrusion detection system. © 2021 ieee.

关键词： Deep learning Smart cities computational modeling Biological system modeling Decision making Neural networks Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

computational insights on the molecular mechanisms across breast cancer progression combining gene differential expression and co-expression

Computational insights on the molecular mechanisms across br...

引用

2021 ieee EMBS International conference on Biomedical and Health Informatics, BHI 2021

作者： Ikonomakis, Emmanouil K. Bourdakou, Marilena M. Kolios, George Vrahatis, Michael N. Spyrou, George M. Computational Intelligence Laboratory Department of Mathematics University of Patras Patras Greece Laboratory of Pharmacology Faculty of Medicine Democritus University of Thrace Alexandroupolis Greece The Cyprus Institute of Neurology and Genetics The Cyprus School of Molecular Medicine Ayios Dometios Nicosia Cyprus

ISBN: (纸本)9781665403580

As a complex disease with mechanisms that are not fully understood, breast cancer pathology and progression is expected to be determined not only by individual genes, but also by their coordinated effects though a more systemic framework. In this contribution, we combine gene differential analysis (single gene view) with gene co-expression analysis (systemic view) to provide insights on the implicated molecular mechanisms across breast cancer progression. Important gene-gene links in a gene co-expression network are identified and clustered with Density and Distance Content driven (DeDiCo) algorithm, which has been recently published by members of our groups. This algorithm presents very good performance regarding its accuracy on both synthetic and real-life datasets and ability to determine arbitrarily shaped clusters without any assumptions on the shape and the number of the clusters. This work provides a pipeline, exploiting a novel clustering algorithm, in order to analyse gene-gene links based on their co-expression relationships and gene differential analysis across breast cancer progression stages. Existing bibliography verifies the validity of the resulted clusters while exclusive pathways per breast cancer stage as well as a common pathway signature across all breast cancer stages are reported and proper discussion takes place. © 2021 ieee

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Transformer-based CNNs: Mining Temporal Context Information for Multi-sound COVID-19 Diagnosis 43

Transformer-based CNNs: Mining Temporal Context Information ...

引用

43rd Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society (ieee EMBC)

作者： Chang, Yi Ren, Zhao Schuller, Bjorn W. Imperial Coll London GLAM Grp Language Audio & Mus London SW7 2AZ England Univ Augsburg Chair Embedded Intelligence Hlth Care & Wellbeing D-86159 Augsburg Germany

ISBN: (纸本)9781728111797

Due to the COronaVIrus Disease 2019 (COVID-19) pandemic, early screening of COVID-19 is essential to prevent its transmission. Detecting COVID-19 with computer audition techniques has in recent studies shown the potential to achieve a fast, cheap, and ecologically friendly diagnosis. Respiratory sounds and speech may contain rich and complementary information about COVID-19 clinical conditions. Therefore, we propose training three deep neural networks on three types of sounds (breathing/counting/vowel) and assembling these models to improve the performance. More specifically, we employ Convolutional Neural Networks (CNNs) to extract spatial representations from log Mel spectrograms and a multi-head attention mechanism in the transformer to mine temporal context information from the CNNs' outputs. The experimental results demonstrate that the transformer-based CNNs can effectively detect COVID-19 on the DiCOVA Track-2 database (AUC: 70.0 %) and outperform simple CNNs and hybrid CNN-RNNs.

关键词： COVID-19 Training Deep learning Pandemics Databases computational modeling Transformers

来源：评论

学校读者我要写书评

暂无评论

Analysis of feedback schemes in proliferating stem cells

Analysis of feedback schemes in proliferating stem cells

引用

ieee conference on Decision and Control

作者： A. Borri P. Palumbo A. Singh Istituto di Analisi dei Sistemi e Informatica “A. Ruberti” Italian National Research Council (IASI-CNR) Via dei Taurini 19 Roma Italy Department of Biotechnologies and Biosciences University of Milano-Bicocca Milan Italy Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Stem cells have emerged as a pivotal player in biomedical research, offering unprecedented opportunities for regenerative medicine, disease modeling, and drug discovery. These undifferentiated cells possess the unique ability to self-renew and differentiate into various specialized cell types, making them invaluable in tissue repair and regeneration. This note analyzes a stochastic model aiming at unraveling the control machinery providing the proper ratio of stem-versus-differentiated cells, in agreement with a couple of feedback schemes regulating cell proliferation. What emerges from the closed-loop schemes analysis is that in order to have an asymptotically stable, nontrivial accumulation of differentiated cells a negative feedback on cell proliferation is required. Numerical simulations in the stochastic setting validate the approximate theoretical results achieved to relate how random fluctuations propagate with respect to variations of model parameters.

关键词： Analytical models Negative feedback Fluctuations Biological system modeling Stochastic processes Stem cells Maintenance engineering Numerical simulation Mathematical models Numerical models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：