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检索条件"主题词=Support Vector Machine Model"
130 条 记 录,以下是121-130 订阅
排序:
Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information
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BMC BIOINFORMATICS 2006年 第1期7卷 124-124页
作者: Song, JN Burrage, K Yuan, Z Huber, T Univ Queensland Adv Computat Modelling Ctr Brisbane Qld 4072 Australia Univ Queensland Inst Mol Biosci Brisbane Qld 4072 Australia Univ Queensland ARC Ctr Bioinformat Brisbane Qld 4072 Australia
Background: The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/ trans... 详细信息
来源: 评论
machine learning techniques in disease forecasting: a case study on rice blast prediction
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BMC BIOINFORMATICS 2006年 第1期7卷 485-485页
作者: Kaundal, Rakesh Kapoor, Amar S. Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India Himachal Pradesh Agr Univ CSK Dept Plant Pathol Palampur 176062 Himachal Prades India
Background: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown ... 详细信息
来源: 评论
Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method
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BMC BIOINFORMATICS 2006年 第1期7卷 399-399页
作者: Takahashi, Hiro Nemoto, Takeshi Yoshida, Teruhiko Honda, Hiroyuki Hasegawa, Tadashi Nagoya Univ Sch Engn Dept Biotechnol Chikusa Ku Nagoya Aichi 4648603 Japan Natl Canc Ctr Res Inst Div Genet Chuo Ku Tokyo 1040045 Japan Natl Canc Ctr Res Inst Div Pathol Chuo Ku Tokyo 1040045 Japan Sapporo Med Univ Sch Med Dept Surg Pathol Chuo Ku Sapporo Hokkaido 0608543 Japan
Background: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of tr... 详细信息
来源: 评论
support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition
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BMC BIOINFORMATICS 2006年 第1期7卷 223-223页
作者: Zhou, Tong Weng, Jianhong Sun, Xiao Lu, Zuhong SE Univ State Key Lab Bioelect Nanjing 210096 Peoples R China
Background: Meiotic double-strand breaks occur at relatively high frequencies in some genomic regions ( hotspots) and relatively low frequencies in others ( coldspots). Hotspots and coldspots are receiving increasing ... 详细信息
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Demonstration of two novel methods for predicting functional siRNA efficiency
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BMC BIOINFORMATICS 2006年 第1期7卷 271-271页
作者: Jia, Peilin Shi, Tieliu Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Partner Inst Computat Biol CAS MPG Shanghai Peoples R China Shanghai Jiao Tong Univ Life Sci Sch Shanghai Peoples R China Univ Manchester Dept Biomol Sci Manchester M60 1QD Lancs England
Background: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siR... 详细信息
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Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein
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BMC BIOINFORMATICS 2005年 第1期6卷 1-14页
作者: Raghava, GPS Han, JH Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang 790784 South Korea Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India
Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulator... 详细信息
来源: 评论
An SVM-based system for predicting protein subnuclear localizations
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BMC BIOINFORMATICS 2005年 第1期6卷 291-291页
作者: Lei, ZD Dai, Y Univ Illinois Dept Bioengn MC063 Chicago IL 60607 USA
Background: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computational tool for the prediction of subnucl... 详细信息
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Building a protein name dictionary from full text: a machine learning term extraction approach
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BMC BIOINFORMATICS 2005年 第1期6卷 88-88页
作者: Shi, L Campagne, F Weill Cornell Med Coll Inst Computat Biomed New York NY 10021 USA Weill Cornell Med Coll Dept Physiol & Biophys New York NY 10021 USA
Background: The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most l... 详细信息
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Entropy-based gene ranking without selection bias for the predictive classification of microarray data
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BMC BIOINFORMATICS 2003年 第1期4卷 54-54页
作者: Furlanello, C Serafini, M Merler, S Jurman, G ITC Irst Trento Italy
Background: We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to ... 详细信息
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support vector machines for predicting protein structural class
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BMC BIOINFORMATICS 2001年 第3期2卷 3-3页
作者: Cai, Yu-Dong Liu, Xiao-Jun Xu, Xue-biao Zhou, Guo-Ping Chinese Acad Sci Shanghai Res Ctr Biotechnol Shanghai 200233 Peoples R China Univ Edinburgh Inst Cell Anim & Populat Biol Edinburgh EH9 3JT Midlothian Scotland Univ Wales Coll Cardiff Coll Cardiff Dept Comp Sci Cardiff CF2 3XF S Glam Wales Burnham Inst Dept Biol Struct La Jolla CA 92037 USA
Background: We apply a new machine learning method, the so-called support vector machine method, to predict the protein structural class. support vector machine method is performed based on the database derived from S... 详细信息
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