Journal ArticleDOI
Using LogitBoost classifier to predict protein structural classes.
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TLDR
The LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data.About:
This article is published in Journal of Theoretical Biology.The article was published on 2006-01-07. It has received 187 citations till now. The article focuses on the topics: LogitBoost & Boosting (machine learning).read more
Citations
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Journal ArticleDOI
iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC.
TL;DR: A novel predictor called iDNA6mA-PseKNC is proposed that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PSEKNC), and it has been observed via rigorous cross-validations that the predictor's sensitivity, specificity, accuracy, and stability are excellent.
Journal ArticleDOI
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.
Zhen-Dong Su,Yan Huang,Zhao-Yue Zhang,Ya-Wei Zhao,Dong Wang,Dong Wang,Wei Chen,Wei Chen,Kuo-Chen Chou,Hao Lin +9 more
TL;DR: A sequence‐based bioinformatics tool called ‘iLoc‐lncRNA’ is developed to predict the subcellular locations of LncRNAs by incorporating the 8‐tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach.
Journal ArticleDOI
Brief Communication: A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
TL;DR: A new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein which incorporates both the sequence order and the length effect.
Journal ArticleDOI
Using pseudo-amino acid composition and support vector machine to predict protein structural class
TL;DR: A novel predictor is developed for predicting protein structural class by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.
Journal ArticleDOI
Prediction of protein structural class using novel evolutionary collocation-based sequence representation.
TL;DR: A novel sequence representation that incorporates evolutionary information encoded using PSI‐BLAST profile‐based collocation of AA pairs is proposed that is shown to substantially improve the accuracy of the structural class prediction.
References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
Proceedings ArticleDOI
Improved boosting algorithms using confidence-rated predictions
Robert E. Schapire,Yoram Singer +1 more
TL;DR: Several improvements to Freund and Schapire’s AdaBoost boosting algorithm are described, particularly in a setting in which hypotheses may assign confidences to each of their predictions.
Journal ArticleDOI
Knowledge-based analysis of microarray gene expression data by using support vector machines
Michael S. Brown,William Noble Grundy,David Lin,Nello Cristianini,Charles W. Sugnet,Terrence S. Furey,Manuel Ares,David Haussler +7 more
TL;DR: In this paper, a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments is introduced based on the theory of support vector machines (SVMs).