检索结果-内蒙古大学图书馆

ieee International conference on Global Software Engineering Workshop (ICGSEW)

作者： Tarek Ali Mervat Gheith Eman S. Nasr Perihan Elbaz Department of Computer Science Cairo University Cairo Egypt Independent Researcher Cairo Egypt Service Delivery Manager Global Consulting Services and Software Development

ISBN: (纸本)9781509036264

The expression "hybrid-computing elements" denotes a category of computing elements where human-based and machine-based computing elements complement each other. The aim from such hybridity is to support human tasks. For example, TopCoder could be used to develop hybrid computing elements to be used in crowdsourcing software. Developing human-based computing elements is a more complex process than developing machine-based computing elements, as the task must be "structured" well as the Turing machines. This leads to the most difficult question of the "unknown unknown" requirements and having to deal with more macro-sociological factors on tasks than formal languages usually work with. In this paper, we model the hybridity using two types of computing elements: the humanbased, with whatever purposes the crowd envision, and the machine-based, which is used to develop it. We present a new framework to help provide such hybridity. It identifies the underlying building-blocks by using a biological metaphor. We call these building-blocks the "genes" of collective intelligence systems, the conditions under which each gene is useful, and the possibilities for combining and re-combining these genes to harness crowds effectively. Employing an analogy from biology where the operator relating the crowd and the parameter to be social phenomena is assumed to belong to semi-algebraic sets. We evaluated our framework by developing a business application through crowd work. The primary result was completed well for managing the crowdsourced software.

关键词： Software Crowdsourcing computational modeling Collaboration biology

来源：评论

学校读者我要写书评

暂无评论

Physiologically based analysis of cochlear implant representations

Physiologically based analysis of cochlear implant represent...

引用

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

作者： J.B. Laflen T.M. Talavaoe P.M. Thirukkonda M.A. Svirsky School of Electrical & Computer Engineering Purdue University West Lafayette IN Purdue University System West Lafayette IN US Dept. of Biomed. Eng. Purdue Univ. West Lafayette IN USA School of Electrical & Computer Engineering Purdue University West Lafayette IN USA Department of Otolaryngology-HNS Indiana University School of Medicine USA Department of Otolaryngology-HNS School of Medicine Indianapolis IN

A method is presented for analyzing cochlear implant stimulations and typical representations used in simulations. Filtered "white-noise" bands are modulated using sinusoids, representing differing stimulation channels. These representations, along with their corresponding envelopes, are used to generate neural activation patterns (NAPs), which represent "normal-hearing" responses In the auditory nerve to these stimuli. Additionally, NAPs are generated to represent the neural activity induced by cochlear implant stimulation strategies, assuming exponential rolloff from the electrodes. The mean squared error is measured between NAPs both directly, and after compensation for perceptual resolution. Results suggest that the noise-band approximation of the CIS implant signal actually has more in common with the original source than with the implant stimulation patterns.

关键词： Cochlear implants Electrodes computational intelligence Society Deafness Auditory system Acoustic noise Biomedical engineering Auditory implants Microphone arrays Sensor arrays

来源：评论

学校读者我要写书评

暂无评论

An Omnibus Permutation Test on Ensembles of Two-Locus Analyses for the Detection of Purely Epistatic Multi-locus Interactions

引用

16th International conference on Neural Information Processing (ICONIP 2009)

作者： Wongseree, Waranyu Assawamakin, Anunchai Piroonratana, Theera Sinsomros, Saravudh Limwongse, Chanin Chaiyaratana, Nachol King Mongkuts Univ Technol N Bangkok Fac Engn Dept Elect Engn 1518 Piboolsongkram Rd Bangkok 10800 Thailand Mahidol Univ Siriraj Hosp Fac Med Dept Res & DevDiv Mol Genet Bangkok 10700 Thailand

ISBN: (纸本)9783642106828

Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions. 2LOmb which performs an omnibus permutation test on ensembles of two-locus analyses is proposed. The algorithm consists of four main steps: two-locus analysis, a permutation test;global p-value determination and a progressive search for the best;ensemble. 2LOmb is benchmarked against a set association approach, a correlation-based feature selection technique and a tuned ReliefF technique. The simulation results from multi-locus interaction problems indicate that 2LOmb has a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs), which signifies a high detection power. 2LOmb is subsequently applied to type 1 and type 2 diabetes mellitus (T1D and T2D) data sets, which are obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium. After primarily screening for SNPs that locate within or near candidate genes and exhibit no marginal single-locus effects, the T1D and T2D data sets are reduced to 2,359 SNPs from 350 genes and 7,065 SNPs from 370 genes, respectively. The 2LOmb search reveals that 28 SNPs in 21 genes are associated with T1D while 11 SNPs in four genes are associated with T2D. The findings provide an alternative explanation for the aetiology of T1D and T2D in a UK population.

关键词： bioinformatics epistasis genetic association study genetic epidemiology machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

Enhancing Protein Sequence Classification with a Fuzzy Neura...

引用

International conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Nguyen Quoc Khanh Le Van-Nui Nguyen Thi-Tuyen Nguyen Thi-Xuan Tran Trang-Thi Ho Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan University of Information and Communication Technology Thai Nguyen University Thai Nguyen Vietnam University of Economics and Business Administration Thai Nguyen University Thai Nguyen Vietnam Department of Computer Science and Information Engineering TamKang University New Taipei Taiwan

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.

关键词： Fuzzy logic Uncertainty Accuracy Biological system modeling Peptides Machine learning Fuzzy neural networks

来源：评论

学校读者我要写书评

暂无评论

Building an automated classification of DNA-binding protein domains

引用

bioinformatics 2002年第sup10期18卷 S192-S201页

作者： Ponomarenko, JV Bourne, PE Shindyalov, IN Univ Calif San Diego San Diego Supercomp Ctr La Jolla CA 92093 USA Russian Acad Sci Inst Cytol & Genet Siberian Branch Novosibirsk 630090 Russia Univ Calif San Diego Dept Pharmacol La Jolla CA 92093 USA

Intensive growth in 3D structure data on DNA-protein complexes as reflected in the Protein Data Bank (PDB) demands new approaches to the annotation and characterization of these data and will lead to a new understanding of critical biological processes involving these data. These data and those from other protein structure classifications will become increasingly important for the modeling of complete proteomes. We propose a fully automated classification of DNA-binding protein domains based on existing 3D-structures from the PDB. The classification, by domain, relies on the Protein Domain Parser (PDP) and the Combinatorial Extension (CE) algorithm for structural alignment. The approach involves the analysis of 3D-interaction patterns in DNA-protein interfaces, assignment of structural domains interacting with DNA, clustering of domains based on structural similarity and DNA-interacting patterns. Comparison with existing resources on describing structural and functional classifications of DNA-binding proteins was used to validate and improve the approach proposed here. In the course of our study we defined a set of criteria and heuristics allowing us to automatically build a biologically meaningful classification and define classes of functionally related protein domains. It was shown that taking into consideration interactions between protein domains and DNA considerably improves the classification accuracy. Our approach provides a high-throughput and up-to-date annotation of DNA-binding protein families which can be found at http://***.

关键词： Artificial intelligence Binding Sites Computer Simulation Databases, Protein Databases, Protein DNA/analysis DNA/chemistry DNA/classification DNA-Binding Proteins/analysis DNA-Binding Proteins/chemistry DNA-Binding Proteins/classification Models, Chemical Models, Molecular Protein Binding Protein Structure, Tertiary Sequence Analysis/methods Sequence Analysis, Protein/methods

来源：评论

学校读者我要写书评

暂无评论

Modeling UGT2B7 and NR1I3 genes through multilayer network to highlight hidden link with taxane neurotoxicity

Modeling UGT2B7 and NR1I3 genes through multilayer network t...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Giuseppe Agapito Marianna Milano Francesca Scionti Nicoletta Staropoli Pierfrancesco Tassone Pierosandro Tagliaferri Mario Cannataro Mariamena Arbitrio Dept. of Law Economics and Social Sciences University Magna Græcia Catanzaro Italy Dept. of Experimental and Clinical Medicine University Magna Græcia Catanzaro Italy Dept. of Medical and Surgical Sciences University Magna Græcia Catanzaro Italy Institute for Biomedical Research and Innovation National Research Council Catanzaro Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Breast cancer (BC) remains a leading global malignancy, with taxane-based treatment (TBT), including paclitaxel and docetaxel, significantly enhancing outcomes across early-stage, locally advanced, and metastatic BC. Despite its efficacy, TBT can cause severe taxane-related peripheral neurotoxicity (TrPN), which limits dosing and affects patient quality of life. TrPN is cumulative, primarily sensory, and unpredictable, with genetic predisposition playing a key role in its development. Previous pharmacogenomic (PGx) study identified five single nucleotide polymorphisms (SNPs) in the NR1I3 and UGT2B7 genes, linked to protection against severe TrPN in BC patients. ROC analysis validated these findings in an independent BC dataset. Neuroprotective effects were in patients homozygous for allele variants 2 with ultrametabolizer phenotype, which accelerates taxane inactivation and reduces treatment efficacy, potentially worsening prognosis. NR1I3 and UGT2B7 genes could serve as predictive biomarkers for TrPN and taxane bioavailability. Further network and pathway enrichment analyses (PEA) provided insights into the molecular pathways involved in TrPN and BC progression. Network analysis, a powerful tool in computational biology, integrates genomic and proteomic data to uncover drug-response mechanisms. This approach advances personalized medicine by identifying key genes and biomarkers for predicting treatment response and adverse effects, offering new therapeutic opportunities.

关键词： Correlation Phenotypes Precision medicine Proteomics Biomarkers Optical fiber networks Metabolism bioinformatics Protection Prognostics and health management

来源：评论

学校读者我要写书评

暂无评论

PocketLG: Protein Binding Pocket Identification Using Protein Language Model and Graph Transformer

PocketLG: Protein Binding Pocket Identification Using Protei...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Li Zhang Heng Chang Zehua Sun Wenjie Li Li Zhu Han Wang College of Computer Science and Engineering Changchun University of Technology Changchun China School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China The Radiology Department of General Hospital Ningxia Medical University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Protein pocket is a special region on the surface of a protein that commonly interacts with other molecules, especially small molecule compounds. Accurately identifying and understanding the structure character of protein pockets, that can accelerate the development of new drugs, as well improve existing drugs to reveal disease mechanisms. In this study, we propose a deep learning method named PocketLG, to identify the potential binding pockets integrating a Protein Language model and Graph Transformer. The method constructs the pocket identification task as a regression problem, which can more accurately identify the real pocket boundaries. In addition, We propose a data sampling strategy and construct a pipeline to support the data sampling effort. This strategy has the benefit of augmenting the data, allowing the model to better learn the intrinsic characteristics of the pocket structure, even for pockets with unknown boundaries This method can sample the unknown pocket by dividing different sample sizes and then determine the boundary of the unknown pocket by the regression value, it can be a direct help for downstream tasks. Experimental results show that our deep learning model can identify binding pockets on proteins with 91 percent success rate. This work provides a new technical route for protein binding pocket prediction research, which can greatly contribute to the development of the pharmaceutical industry. PocketLG source code, pre-trained models and data preprocessing code are available at https://***/NENUBioCompute/PocketLG.

关键词： Proteins Drugs Deep learning Protein engineering Predictive models Feature extraction Transformers Data models Pharmaceutical industry Diseases

来源：评论

学校读者我要写书评

暂无评论

A Regressor-Guided Graph Diffusion Model for Predicting Enzyme Mutations to Enhance Turnover Number

A Regressor-Guided Graph Diffusion Model for Predicting Enzy...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Xiaozhu Yu Kai Yi Yu Guang Wang Yiqing Shen Toursun Synbio Shanghai China Pratt School of Engineering Duke University Durham USA MRC Laboratory of Molecular Biology University of Cambridge Cambridge UK Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Computer Science Johns Hopkins University Baltimore USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Enzymes are biological catalysts that can accelerate chemical reactions compared to uncatalyzed reactions in aqueous environments. Their catalytic efficiency is quantified by the turnover number (k cat ), a parameter in enzyme kinetics. Enhancing enzyme activity is important for optimizing slow chemical reactions, with far-reaching implications for both research and industrial applications. However, traditional wet-lab methods for measuring and optimizing enzyme activity are often resource-intensive and time-consuming. To address these limitations, we introduce k cat Diffuser, a novel regressor-guided diffusion model designed to predict and improve enzyme turnover numbers. Our approach innovatively reformulates enzyme mutation prediction as a protein inverse folding task, thereby establishing a direct link between structural prediction and functional optimization. k cat Diffuser is a graph diffusion model guided by a regressor, enabling the prediction of amino acid mutations at multiple random positions simultaneously. Evaluations on BERENDA dataset shows that k cat Diffuser can achieve a ∆logk cat of 0.209, outperforming state-of-the-art methods like ProteinMPNN, PiFold, GraDe-IF in improving enzyme turnover numbers. Additionally, k cat Diffuser maintains high structural fidelity with a recovery rate of 0.716, pLDDT score of 92.515, RMSD of 3.764, and TM-score of 0.934, demonstrating its ability to generate enzyme variants with enhanced activity while preserving essential structural properties. Overall, k cat Diffuser represents a more efficient and targeted approach to enhancing enzyme activity. The code is available at https://***/xz32yu/KcatDiffuser.

关键词： Measurement Enzymes Protein engineering computational modeling Biological system modeling Predictive models Diffusion models Chemical reactions Kinetic theory Optimization

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems

Assessing the Impact of Vulnerabilities on Confidentiality, ...

引用

Informatics (ICI), International conference on

作者： Shubham Minhass Ritu Chauhan Harleen Kaur Amity Institute of Information Technology Amity University UP India Center for Computational Biology and Bioinformatics IoT and Artificial Intelligence Lab Amity University UP India Department of Computer Science and Engineering Jamia Hamdard Delhi India

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Monitoring system of plant growth environment temperature based on LabVIEW and BP neural network

Monitoring system of plant growth environment temperature ba...

引用

ieee Information Technology, Networking, Electronic and Automation Control conference

作者： Xudong Cui Liguo Tian Meng Li Han Han Shidong Hou Chunhu Shang College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin China

In the current agricultural intelligence, the monitoring of plant growth environment parameters needs to use a variety of sensors which also leads to the existing intelligent control system being generally large. The electrical signal is another important signal in plants which contains abundant information besides hydraulic and chemical signals. Plant electrical signals can not only regulate plant growth but also reflect the environment to a certain extent. The collection of Aloe Vera electric signals at different temperatures (15, 19, 23, 27, 31, and 35 ℃) is accomplished by using LabVIEW software and hardware. The signal is denoised by using the module in the signal processing toolkit. The BP network is trained repeatedly by extracting the eigenvalues from the signal and the model with the smallest mean variance is selected as the optimal model. Deploy the model to the computer and use mixed programming with MATLAB to invoke the model. Use the powerful graphical programming and interface development capabilities of LabVIEW to ultimately build a monitoring system for plant growth environment temperature. Making the future agricultural intelligent control system lightweight and portable.

关键词： Temperature sensors computational modeling Plants (biology) Neural networks Programming Mathematical models Eigenvalues and eigenfunctions

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：