2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)最新文献_第5页

Protein secondary structure prediction with ICA feature extraction 基于ICA特征提取的蛋白质二级结构预测

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318000

J. Melo, George D. C. Cavalcanti, K. Guimaraes

{"title":"Protein secondary structure prediction with ICA feature extraction","authors":"J. Melo, George D. C. Cavalcanti, K. Guimaraes","doi":"10.1109/NNSP.2003.1318000","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318000","url":null,"abstract":"An original application of the independent component analysis (ICA) is presented in this work. This linear transformation method is used for feature extraction for a machine learning approach to the protein secondary structure prediction problem. PSI-blast profiles, built on NCBI's nonredundant protein database, have their dimensionality reduced through ICA method. The resulting components are used as input data to three artificial neural networks with 30, 35 or 40 nodes in the hidden layer. Those classifiers are trained with the RPROP algorithm and five rules are used for the combination of their outputs. The results achieved are compared with the best ones recently obtained in similar conditions, including experiments using principal component analysis (PCA) as feature extraction method, presenting the best result. The performance of each network individually achieved a Q/sub 3/ accuracy of 74.1% on average, using only 120 independent components. When the networks are combined with the product rule the performance achieved is 75.2%. This result is overcome only when the raw data are informed to the networks, when an accuracy of 75.9% is achieved.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165181","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

Support vector machine for the simultaneous approximation of a function and its derivative 支持向量机同时逼近一个函数和它的导数

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318018

M. Lázaro, I. Santamaría, F. Pérez-Cruz, Antonio Artés-Rodríguez

引用次数: 3

Computed simultaneous imaging of multiple biomarkers 多种生物标志物的计算机同步成像

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318026

Y. Wang, J. Xuan, R. Srikanchana, Junying Zhang, Z. Szabo, Z. Bhujwalla, P. Choyke, King C. Li

引用次数: 2

Thermal modelling with neural network applied to Planck space mission 神经网络热建模在普朗克航天任务中的应用

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318014

C. Leroy, J. Bernard, J. Trouilhet

引用次数: 0

On optimal segmentation of sequential data 序列数据的最优分割

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318044

J. Kohlmorgen

引用次数: 9

Using gene ontology on genome-scale studies to find significant associations of biologically relevant terms to groups of genes 在基因组规模的研究中使用基因本体来发现与基因组相关的生物学术语的显著关联

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318003

F. Al-Shahrour, Javier Herrero, Á. Mateos, J. Santoyo, R. Díaz-Uriarte, J. Dopazo

{"title":"Using gene ontology on genome-scale studies to find significant associations of biologically relevant terms to groups of genes","authors":"F. Al-Shahrour, Javier Herrero, Á. Mateos, J. Santoyo, R. Díaz-Uriarte, J. Dopazo","doi":"10.1109/NNSP.2003.1318003","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318003","url":null,"abstract":"The analysis of genome-scale data from different high throughput techniques usually involves the grouping of genes based on experimental criteria. These groups are a consequence of the biological roles the genes are playing within the cell. Establishing which of these groups are functionally important is essential. Gene ontology terms provide a specialised vocabulary to describe the relevant biological properties of genes. We used a simple procedure to extract terms that are significantly over or under-represented in sets of genes within the context of a genome-scale experiment. Said procedure, which takes the multiple-testing nature of the statistical contrast into account, has been implemented as a Web application, FatiGO, allowing for easy and interactive querying. Several examples demonstrate its application and the type of information that can be extracted. Although a number of genes still lack gene ontology annotations, the results were informative enough to characterise the biological processes in the systems analysed.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124581676","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

Loss functions to combine learning and decision in multiclass problems 多类问题中结合学习与决策的损失函数

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318031

A. Guerrero-Curieses, R. Alaíz-Rodríguez, Jesús Cid-Sueiro

引用次数: 4

Independent component analysis (ICA) for blind equalization of frequency selective channels 独立分量分析(ICA)用于频率选择信道的盲均衡

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318041

C. S. Wong, D. Obradovic, N. Madhu

引用次数: 16

Identifying underlying factors in breast cancer using independent component analysis 使用独立成分分析确定乳腺癌的潜在因素

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318006

J. A. Berger, S. Hautaniemi, H. Edgren, O. Monni, S. Mitra, O. Yli-Harja, J. Astola

引用次数: 12

A prediction matrix approach to convolutive ICA 卷积ICA的预测矩阵方法

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318024

L. K. Hansen, M. Dyrholm

引用次数: 13