Sixth International Conference on Machine Learning and Applications (ICMLA 2007)最新文献_第2页

Cox's Proportional Hazards Model with Lp Penalty for Biomarker Identification and Survival Prediction 带有Lp惩罚的Cox比例风险模型用于生物标志物鉴定和生存预测

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.96

Zhenqiu Liu

引用次数: 8

Learning bayesian networks consistent with the optimal branching 学习符合最优分支的贝叶斯网络

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.74

Alexandra M. Carvalho, Arlindo L. Oliveira

引用次数: 15

Efficient label propagation for interactive image segmentation 有效的标签传播交互式图像分割

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.54

Fei Wang, Xin Wang, Ta-Hsin Li

引用次数: 23

Rule refinement with extended data expression 使用扩展数据表达式进行规则细化

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.75

Jung Min Kong, Dong-Hun Seo, W. Lee

引用次数: 10

Uncertainty optimization for robust dynamic optical flow estimation 鲁棒动态光流估计的不确定性优化

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.15

Volker Willert, Marc Toussaint, J. Eggert, E. Körner

引用次数: 16

Feature Extraction from Microarray Expression Data by Integration of Semantic Knowledge 基于语义知识集成的微阵列表达数据特征提取

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.10

Young-Rae Cho, Xian Xu, W. Hwang, A. Zhang

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引用次数: 3

A Clustering-Based Approach to Predict Outcome in Cancer Patients 基于聚类的方法预测癌症患者预后

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.20

Kai Xing, D. Henson, Dechang Chen, Li Sheng

引用次数: 11

Support Vector Machine Classification of Probability Models and Peptide Features for Improved Peptide Identification from Shotgun Proteomics 基于概率模型和肽特征的支持向量机分类，用于改进霰弹枪蛋白质组学的肽识别

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.17

C. H. Yamamoto, Maria Cristina Ferreira de Oliveira, M. L. Fujimoto, S. O. Rezende

{"title":"Support Vector Machine Classification of Probability Models and Peptide Features for Improved Peptide Identification from Shotgun Proteomics","authors":"C. H. Yamamoto, Maria Cristina Ferreira de Oliveira, M. L. Fujimoto, S. O. Rezende","doi":"10.1109/ICMLA.2007.17","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.17","url":null,"abstract":"Mass spectrometry (MS)-based proteomics is a powerful and popular high-throughput process for characterizing the global protein content of a sample. In shotgun proteomics, typically proteins are digested into fragments (peptides) prior to mass analysis, and the presence of a protein in inferred from the identification of its constituent peptides. Thus, accurate proteome characterization is dependent upon the accuracy of this peptide identification step. Database search routines generate predicted spectra for all peptides derived from the known genome information, and thus, identify a peptide by 'matching' an experimental to a predicted spectrum. However, due to many problems, such as incomplete fragmentation, this process results in a large number of false positives. We present a new scoring algorithm that integrates probabilistic database scoring metrics (from the MSPolygraph program) with physico-chemical properties in a support vector machine (SVM). We demonstrate that this peptide identification classifier SVM (PICS) score is not only more accurate than the single best database scoring metric, but is also significantly more accurate than models derived using a linear discriminant analysis, decision tree, or artificial neural network.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"81 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177713","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: 13

Using genetic programming for the induction of oblique decision trees 利用遗传规划进行斜决策树的归纳

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.66

A. Shali, M. Kangavari, B. Bina

引用次数: 13

Hybrid Neural Network Based Model for Predicting the Performance of a Two Stroke Spark Ignition Engine 基于混合神经网络的二冲程火花点火发动机性能预测模型

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.107

M. M. Wani, M. Wani

引用次数: 6