iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

Li, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo‐Chen

doi:10.18632/oncotarget.9148

Cited by 171 publications

(59 citation statements)

References 130 publications

(123 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature analysis implies that the λ + 1 components beyond the amino acid composition play very important roles in the performance of the experiment. As shown in a series of recent publications (see, e.g., [58,[67][68][69][70][71][72]) in demonstrating new methods or approaches, user-friendly and publicly accessible web-servers will significantly enhance their impacts [73]. We will make efforts in our future work to further improve our method and provide a web-server for the new method presented.…”

Section: Discussionmentioning

confidence: 94%

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Lin

2016

Applied Sciences

View full text Add to dashboard Cite

Abstract:The technique of comparison and analysis of biological sequences is playing an increasingly important role in the field of Computational Biology and Bioinformatics. One of the key steps in developing the technique is to identify an appropriate manner to represent a biological sequence. In this paper, on the basis of three physical-chemical properties of amino acids, a protein primary sequence is reduced into a six-letter sequence, and then a set of elements which reflect the global and local sequence-order information is extracted. Combining these elements with the frequencies of 20 native amino acids, a (21 + λ) dimensional vector is constructed to characterize the protein sequence. The utility of the proposed approach is illustrated by phylogenetic analysis and identification of DNA-binding proteins.

show abstract

Section: Discussionmentioning

confidence: 94%

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Lin

2016

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…All these 4 metrics were widely used in assessing the performance of protein function predictors [98][99][100][101][102][103].…”

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

View full text Add to dashboard Cite

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction.Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research. KEYWORDSfalse discovery rate; machine learning; protein function prediction; support vector machine; BLAST Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

show abstract

“…incorrectly predicted to be of the i-th location. The metrics of Equation (19) have been widely used to examine the quality of predictors in genome/proteome analysis (see, e.g., [46,47,[76][77][78][79][80][107][108][109]) and computational biomedicine (see, e.g., [82,[110][111][112]). Natural Science Given in Table 3 are the corresponding results obtained by pLoc-mGpos for each of the four subcellular locations.…”

Section: Comparison With the State-of-the-art Predictormentioning

confidence: 99%

“…Actually, user-friendly web-servers as shown in a series of recent publications [40,46,100,[107][108][109][110][111][112][114][115][116][117][118][119][120][121][122] will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [52]. In view of this, the web-server for the new predictor pLoc-mGpos has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/.…”

Section: Web Server and User Guidementioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

Self Cite

View full text Add to dashboard Cite

The basic unit in life is cell. It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method, we developed a new predictor called "pLoc-mGpos" by in-depth extracting the key information from GO (Gene Ontology) into the Chou's general PseAAC (Pseudo Amino Acid Composition) for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to "iLoc-Gpos", the state-of-the-art predictor for the same purpose. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at Natural Science http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

show abstract

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

Cited by 171 publications

References 130 publications

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Contact Info

Product

Resources

About