2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第5页

Feature Selection Based on Physicochemical Properties of Redefined N-term Region and C-term Regions for Predicting Disorder 基于重定义n项区域和c项区域物理化学性质的特征选择预测紊乱

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594927

Kana Shimizu, Y. Muraoka, S. Hirose, T. Noguchi

{"title":"Feature Selection Based on Physicochemical Properties of Redefined N-term Region and C-term Regions for Predicting Disorder","authors":"Kana Shimizu, Y. Muraoka, S. Hirose, T. Noguchi","doi":"10.1109/CIBCB.2005.1594927","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594927","url":null,"abstract":"The prediction of intrinsic disorder from amino acid sequence has been gaining increasing attention because these have come to be known as important regions for protein functions. The most common way of predicting disorder is based on binary classification with machine learning. Since amino acid composition has different propensities in the N-term, C-term, and internal regions, the accuracy of prediction increases by dividing training data into these three regions and predicting them separately. However, previous work has lacked discussion about a concrete definition of the N-term and C-term regions, and has only used the heuristic length from the terminal. Other previous work has shown that general physicochemical properties rather than specific amino acids are important factors contributing to disorder, and a reduced amino acid alphabet can maintain excellent precision in predicting disorder. In this paper, we redefine a suitable length and position for the N-term and C-term regions for predicting disorder. Moreover, we show that each region has different physicochemical properties, which are important factors contributing to disorder. We also suggest a region-specific-reduced set of amino acid and modified PSSM based on that for predicting disorder. We implemented our method and (1) compare it with the conventional division method, (2) compare our feature selection with all physicochemical features, on casp6 benchmark, PDB dataset, and DisProt. The result supports that the method of new data separation is effective, and indicates each region has different physicochemical properties that are important factors for predicting protein disorders.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131372335","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

A Neural Network for Predicting Protein Disorder using Amino Acid Hydropathy Values 利用氨基酸亲水性值预测蛋白质紊乱的神经网络

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594958

Deborah Stoffer, L. Volkert

{"title":"A Neural Network for Predicting Protein Disorder using Amino Acid Hydropathy Values","authors":"Deborah Stoffer, L. Volkert","doi":"10.1109/CIBCB.2005.1594958","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594958","url":null,"abstract":"Proteins have been discovered to contain ordered regions and disordered regions, where ordered regions have a defined three-dimensional (3D) structure and disordered regions do not. While in the past it was believed that proteins only function in a defined 3D structure, proteins with disordered regions have been discovered to have at least 28 distinct functions. It is now important to be able to determine the ordered and disordered regions in proteins. Several experimental techniques such as X-ray crystallography, NMR spectroscopy, circular dichroism, protease digestion, and Stokes radius determination, along with several computational techniques such as artificial neural networks (ANNs), support vector machines (SVMs), logistic regression, and discriminant analysis have so far been used to detect disordered proteins. Past research has shown that ANNs and amino acid properties are an effective tool at predicting protein disorder. This research uses a feed-forward neural network implemented using JavaNNS and the hydropathy values of amino acids to predict protein disorder. The results show that hydropathy is an important amino acid property for disorder.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127224908","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

Interpretable Prediction of Protein Stability Changes upon Mutation by Using Decision Tree 基于决策树的蛋白质突变稳定性可解释性预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594963

Larry Huang, Wen-Lin Huang, Shinn-Ying Ho, Shiow-Fen Hwang

{"title":"Interpretable Prediction of Protein Stability Changes upon Mutation by Using Decision Tree","authors":"Larry Huang, Wen-Lin Huang, Shinn-Ying Ho, Shiow-Fen Hwang","doi":"10.1109/CIBCB.2005.1594963","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594963","url":null,"abstract":"For protein stability changes upon mutation, an accurate predictor with linguistic interpretability is beneficial to protein designs. Traditional analysis based on linear correlation between predicted and experimental data reveals their primitive relationships. Recently, some machine learning techniques such as artificial neural network (ANN)-based methods were applied to find an accurate predictor. However, the ANN-predictor without interpretability is insufficient in knowledge discovery. This paper proposes an interpretable predictor using a rule-based decision tree method (named iPTREE) for accurately predicting protein stability changes upon single point mutations. Besides being a sign predictor, iPTREE can be used both as a model for verifying attributes effect, and as a rules miner in the protein stability change study. iPTREE is depending on features including mutation type (deleted and introduced residues), the relative solvent accessibility value (RSA), the experimental conditions (pH and temperature) and the local spatial environment. To evaluate the performance of iPTREE, a thermodynamic dataset consisting of 1615 mutations generated from ProTherm is used. The computer simulation shows that iPTREE has an accurate prediction for the direction of stability changes as high as 87%, which is significantly better than the ANN-predictor for the same features.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126513820","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

Identification of Functional RNA Genes Using Evolved Neural Networks 利用进化神经网络鉴定功能性RNA基因

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594895

Mars Cheung, G. Fogel

引用次数: 4

Hybrid PCA and LDA Analysis of Microarray Gene Expression Data 微阵列基因表达数据的混合PCA和LDA分析

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594942

Yijuan Lu, Q. Tian, Maribel Sanchez, Yufeng Wang

引用次数: 6

The Homology Kernel: A Biologically Motivated Sequence Embedding into Euclidean Space 同源核:嵌入欧几里得空间的生物动机序列

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594915

E. Eskin, S. Snir

引用次数: 8

Optimized Kernel Machines for Cancer Classification Using Gene Expression Data 利用基因表达数据进行癌症分类的优化核机器

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594928

Huilin Xiong, Xue-wen Chen

引用次数: 17

An Architecture Combining Bayesian segmentation and Neural Network Ensembles for Protein Secondary Structure Prediction 一种结合贝叶斯分割和神经网络集成的蛋白质二级结构预测体系

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594960

Niranjan P. Bidargaddi, M. Chetty, J. Kamruzzaman

{"title":"An Architecture Combining Bayesian segmentation and Neural Network Ensembles for Protein Secondary Structure Prediction","authors":"Niranjan P. Bidargaddi, M. Chetty, J. Kamruzzaman","doi":"10.1109/CIBCB.2005.1594960","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594960","url":null,"abstract":"A combined architecture of Bayesian segmentation along with ensembles of two layered feedforward network has been built and tested on widely studied two non membrane, non homologous databases comprising of 480 and 608 protein sequences respectively. In the first stage, Bayesian segmentation is used to infer sequence/structure relationship in terms of structural segments which is well suited to model non-local interactions among segments. The probability scores for the three structural states (helix, sheet and coil) of each residue obtained from the Bayesian segmentation has been used as the inputs at the second stage to a feedforward neural network. The neural network is trained with the sliding window comprising of the scores of seven consecutive residues along with additional inputs for physicochemical properties of the residues where the prediction is made for the central residue. The key aspect of the model is inclusion of physicochemical properties of the amino acids at the second stage. An ensemble of neural networks have been trained in second stage based on the posterior probabilities approach to determine the number of neural networks. This model achieves a Q3 accuracy of above 71% which is one of the highest accuracy values for single sequence prediction methods.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132425871","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

Feature Selection for Classification with Proteomic Data of Mixed Quality 混合质量蛋白质组数据分类的特征选择

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594944

E. Marchiori, N. Heegaard, C. Jiménez, Mikkel West-Nielsen

{"title":"Feature Selection for Classification with Proteomic Data of Mixed Quality","authors":"E. Marchiori, N. Heegaard, C. Jiménez, Mikkel West-Nielsen","doi":"10.1109/CIBCB.2005.1594944","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594944","url":null,"abstract":"In this paper we assess experimentally the performance of two state-of-the-art feature selection methods, called RFE and RELIEF, when used for classifying pattern proteomic samples of mixed quality. The data are generated by spiking human sera to artificially create differentiable sample groups, and by handling samples at different storage temperature. We consider two type of classifiers: support vector machines (SVM) and k-nearest neighbour (kNN). Results of leave-one-out cross validation (LOOCV) experiments indicate that RELIEF selects more stable feature subsets than RFE over the runs, where the selected features are mainly spiked ones. However, RFE outperforms RELIEF in terms of (average LOOCV) accuracy, both when combined with SVM and kNN. Perfect LOOCV accuracy is obtained by RFE combined with 1NN. Almost all the samples that are wrongly classified by the algorithms have high storage temperature. The results of experiments on this data indicate that when samples of mixed quality are analyzed computationally, feature selection of only relevant (spiked) features does not necessarily correspond to highest accuracy of classification.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493826","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

Analysis of MALDI-TOF Serum Profiles for Biomarker Selection and Sample Classification MALDI-TOF血清谱分析用于生物标志物选择和样本分类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594943

H. Ressom, R. Varghese, E. Orvisky, S. K. Drake, G. Hortin, M. Abdel-Hamid, C. Loffredo, R. Goldman

{"title":"Analysis of MALDI-TOF Serum Profiles for Biomarker Selection and Sample Classification","authors":"H. Ressom, R. Varghese, E. Orvisky, S. K. Drake, G. Hortin, M. Abdel-Hamid, C. Loffredo, R. Goldman","doi":"10.1109/CIBCB.2005.1594943","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594943","url":null,"abstract":"Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality, and substantial noise. These characteristics generate challenges in discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. We present data processing methods and computational intelligence that combines support vector machines (SVM) with particle swarm optimization (PSO) for biomarker selection from MALDI-TOF spectra of enriched serum. SVM classifiers were built for various combinations of m/z windows guided by the PSO algorithm. The method identified mass points that achieved high classification accuracy in distinguishing cancer patients from non-cancer controls. Based on their frequency of occurrence in multiple runs, six m/z windows were selected as candidate biomarkers. These biomarkers yielded 100% sensitivity and 91% specificity in distinguishing liver cancer patients from healthy individuals in an independent dataset.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121821582","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: 16