Journal ArticleDOI
iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition
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TLDR
It was observed that the overall success rate achieved by iHSP-PseRAAAC in identifying the functional types of HSPs among the aforementioned six types was more than 87%, which was derived by the jackknife test on a stringent benchmark dataset.About:
This article is published in Analytical Biochemistry.The article was published on 2013-11-01. It has received 270 citations till now. The article focuses on the topics: Pseudo amino acid composition & HSP60.read more
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Impacts of bioinformatics to medicinal chemistry.
TL;DR: This minireview is to summarize the progresses by focusing on the following six aspects: Use the pseudo amino acid composition or PseAAC to predict various attributes of protein/peptide sequences that are useful for drug development.
Journal ArticleDOI
iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.
TL;DR: It was discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ54 promoters.
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iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition
TL;DR: It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
Journal ArticleDOI
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection
Bin Liu,Deyuan Zhang,Ruifeng Xu,Jinghao Xu,Xiaolong Wang,Qingcai Chen,Qiwen Dong,Kuo-Chen Chou +7 more
TL;DR: This work proposes a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles, which can be calculated from the multiple sequence alignments generated by PSI-BLAST.
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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.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Weizhong Li,Adam Godzik +1 more
TL;DR: Cd-hit-2d compares two protein datasets and reports similar matches between them; cd- Hit-est clusters a DNA/RNA sequence database and cd- hit-est-2D compares two nucleotide datasets.
Journal ArticleDOI
Prediction of protein cellular attributes using pseudo‐amino acid composition
TL;DR: A remarkable improvement in prediction quality has been observed by using the pseudo‐amino acid composition and its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features.
Journal ArticleDOI
Some remarks on protein attribute prediction and pseudo amino acid composition.
TL;DR: This review is to discuss each of the five procedures of the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.