iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

Zhang, Chang-Jian; Tang, Hua; Li, Wenchao; Lin, Hao; Chen, Wei; Chou, Kuo‐Chen

doi:10.18632/oncotarget.11975

Cited by 162 publications

(54 citation statements)

References 125 publications

(99 reference statements)

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“…Since user-friendly and publicly-accessible web-servers represent the future direction [54] for developing practically more useful predictors and will significantly enhance their impacts [55,56], we have provided a web-server for the new method presented in this paper, as done in many recent works [39,43,48,49,57,58,59,60]. The server, named PredPPIS, provides a graphical user interface for users to submit their sequences and receive the predictive results.…”

Section: Resultsmentioning

confidence: 99%

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Kuo¹,

2016

IJMS

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Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at .

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Section: Resultsmentioning

confidence: 99%

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Kuo¹,

2016

IJMS

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“…For a brief introduction to Chou’s PseAAC and its recent development and applications, a comprehensive review is available35. Recently, the concept of the pseudo-component approach was further employed in the fields of computational genetics and genomics36373839404142434445.…”

Section: Methodsmentioning

confidence: 99%

“…If only using NC to extract features, the sequence-order information hidden in DNA samples would be lost, markedly reducing the quality of prediction36373839404142434445. Nucleotide triplets form codons within coding regions, each of which specifies a particular amino acid.…”

Section: Methodsmentioning

confidence: 99%

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EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features

Jia

2016

Sci Rep

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Enhancers are cis elements that play an important role in regulating gene expression by enhancing it. Recent study of modifications revealed that enhancers are a large group of functional elements with many different subgroups, which have different biological activities and regulatory effects on target genes. As powerful auxiliary tools, several computational methods have been proposed to distinguish enhancers from other regulatory elements, but only one method has been considered to clustering them into subgroups. In this study, we developed a predictor (called EnhancerPred) to distinguish between enhancers and nonenhancers and to determine enhancers’ strength. A two-step wrapper-based feature selection method was applied in high dimension feature vector from bi-profile Bayes and pseudo-nucleotide composition. Finally, the combination of 104 features from bi-profile Bayes, 1 feature from nucleotide composition and 9 features from pseudo-nucleotide composition yielded the best performance for identifying enhancers and nonenhancers, with overall Acc of 77.39%. The combination of 89 features from bi-profile Bayes and 10 features from pseudo-nucleotide composition yielded the best performance for identifying strong and weak enhancers, with overall Acc of 68.19%. The process and steps of feature optimization illustrated that it is necessary to construct a particular model for identifying strong enhancers and weak enhancers.

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“…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

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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.

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iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

Cited by 162 publications

References 125 publications

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features

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

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