Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.最新文献_第5页

Computational model of the role of deficit-related drives in sequential movement learning in a T-maze environment t型迷宫环境中序列运动学习中缺陷相关驱动作用的计算模型

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188962

Wei Li, Jeffrey D. Johnson

{"title":"Computational model of the role of deficit-related drives in sequential movement learning in a T-maze environment","authors":"Wei Li, Jeffrey D. Johnson","doi":"10.1109/BIBE.2003.1188962","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188962","url":null,"abstract":"We present a computational model of approach teaming in a T-maze environment. We show that our model learns the correct sequence of six decisions that lead to the location of positive reinforcement and in a manner consistent with experimental observations. Our model exhibits many properties that are characteristic of animal learning in maze environments including delay conditioning, secondary conditioning, and backward chaining. Our model incorporates a comprehensive definition of drive that consists of a primary drive (food) and deficit-related signal (hunger), and an acquired drive (the learned expectation for future reward or punishment). In the T-maze environment, the deficit-related drive of hunger motivates the teaming system to search for food. After several trials in the T-maze, the acquired drive (learned expectation) will shape the teaming system's behavior and allow it to consistently find the food. We propose that changes in drive level, not merely the level of the drive, lead to teaming. Positive changes in drive level results in the enhanced behavior and negative changes result in the depressed behavior. Our comprehensive definition of drive allows us to explain teaming in a biologically plausible manner and is supported by results from hypertension, obesity, and Parkinson's disease research.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130963004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of spontaneous activity in cultured brain tissue using the discrete wavelet transform 用离散小波变换分析培养脑组织的自发活动

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188930

Jeffrey D. Johnson, D. Plenz, John M. Beggs, Wei Li, M. Mieier, N. Miltner, K. Owe

引用次数: 1

A repulsive clustering algorithm for gene expression data 基因表达数据的排斥聚类算法

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188980

Chyun-Shin Cheng, Shiuan-Sz Wang

引用次数: 2

Effective indexing and filtering for similarity search in large biosequence databases 大型生物序列数据库中相似性搜索的有效索引与过滤

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188974

Ozgur Ozturk, H. Ferhatosmanoğlu

引用次数: 29

An investigation of phylogenetic likelihood methods 系统发育似然方法的研究

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188932

T. Williams, Bernard M. E. Moret

引用次数: 48

Application of XML Schema and active rules system in management and integration of heterogeneous biological data XML模式和主动规则系统在异构生物数据管理与集成中的应用

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188975

William M. Shui, R. Wong

引用次数: 11

A novel laboratory version management system for tracking complex biological experiments 一种新颖的实验室版本管理系统，用于跟踪复杂的生物实验

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188939

William M. Shui, N. Lam, R. Wong

引用次数: 5

Regulating gene expression using optimal control theory 利用最优控制理论调控基因表达

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188968

Yunlong Liu, H. Sun, H. Yokota

引用次数: 7

Performance comparison of generalized PSSM in in signal peptide cleavage site and disulfide bond recognition 广义PSSM在信号肽裂解位点和二硫键识别中的性能比较

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188927

P. Clote

{"title":"Performance comparison of generalized PSSM in in signal peptide cleavage site and disulfide bond recognition","authors":"P. Clote","doi":"10.1109/BIBE.2003.1188927","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188927","url":null,"abstract":"We generalize the familiar position-specific score matrix (PSSM), aka weight matrix, by considering a log-odds score for (nonadjacent) k-tuple frequencies, each k-tuple score weighted by the product of its mutual information and its statistical significance, as measured by a point estimator for the p-value of the mutual information. Performance of this new approach, along with other variants of generalized PSSM and profile methods, is measured by receiver-operating characteristic (ROC) curves for the specific problem of signal peptide cleavage site recognition. We additionally compare Vert's recent support vector machine string kernel, Brown's joint probability approximation algorithm and the method WAM. Similar algorithm comparisons are made, though not as extensively, in the case of disulfide bond recognition. While in the case of signal peptide cleavage site recognition, the monoresidue PSSM is essentially competitive, within the limits of statistical significance, even against Vert's support vector machine kernel, diresidue and triresidue PSSM methods display improved performance over monoresidue PSSM for disulfide bond recognition.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"374 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122772236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Towards automated derivation of biological pathways using high-throughput biological data 利用高通量生物数据实现生物途径的自动推导

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188925

Yu Chen, T. Joshi, Ying Xu, Dong Xu

{"title":"Towards automated derivation of biological pathways using high-throughput biological data","authors":"Yu Chen, T. Joshi, Ying Xu, Dong Xu","doi":"10.1109/BIBE.2003.1188925","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188925","url":null,"abstract":"Characterizing biological pathways at the genome scale is one of the most important and challenging tasks in the post genomic era. To address this challenge, we have developed a computational method to systematically and automatically derive partial biological pathways in yeast using high-throughput biological data, including yeast two hybrid data, protein complexes identified from mass spectroscopy, genetics interactions, and microarray gene expression data in yeast Saccharomyces cerevisiae. The inputs of the method are the upstream starting protein (e.g., a sensor of a signal) and the downstream terminal protein (e.g., a transcriptional factor that induces genes to respond the signal); the output of the method is the protein interaction chain between the two proteins. The high-throughput data are coded into a graph of interaction network, where each node represents a protein. The weight of an edge between two nodes models the \"closeness\" of the two represented proteins in the interaction network and it is defined by a rule-based formula according to the high-throughput data and modified by the protein function classification and subcellular localization information. The protein interaction cascade pathway in vivo is predicted as the shortest path identified from the graph of the interaction network using Dijkstra's algorithm. We have also developed a web server of this method (http://compbio.ornl.gov/structure/pathway) for public use. To our knowledge, our method is the first automated method to generally construct partial biological pathways using a suite of high-throughput biological data. This work demonstrates the proof of principle using computational approaches for discoveries of biological pathways with high-throughput data and biological annotation data.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3