Prediction of nuclear receptors with optimal pseudo amino acid composition

Gao, Qing-Bin; Jin, Zhichao; Ye, Xiaofei; Wu, Cheng; He, Jia

doi:10.1016/j.ab.2009.01.018

Cited by 37 publications

(29 citation statements)

References 59 publications

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“…A few methods have already been published for predicting NRP and/or their sub-families12151620. It is not practically possible to compare the performance with all existing methods due to difference in number of sub-families or training datasets.…”

Section: Resultsmentioning

confidence: 99%

“…An SVM trained to predict proteins of a particular sub-family was trained on all samples of that sub-family with positive label and proteins of remaining sub-families with negative label. The same approach has been used in a number of earlier studies like prediction of sub-cellular localization323334, G-protein coupled receptors3536, NRP protein sub-family prediction12151620.…”

Section: Methodsmentioning

confidence: 99%

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NRfamPred: A proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families

Kumar

Kumari

Srivastava

et al. 2014

Sci Rep

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Nuclear receptor proteins (NRP) are transcription factor that regulate many vital cellular processes in animal cells. NRPs form a super-family of phylogenetically related proteins and divided into different sub-families on the basis of ligand characteristics and their functions. In the post-genomic era, when new proteins are being added to the database in a high-throughput mode, it becomes imperative to identify new NRPs using information from amino acid sequence alone. In this study we report a SVM based two level prediction systems, NRfamPred, using dipeptide composition of proteins as input. At the 1st level, NRfamPred screens whether the query protein is NRP or non-NRP; if the query protein belongs to NRP class, prediction moves to 2nd level and predicts the sub-family. Using leave-one-out cross-validation, we were able to achieve an overall accuracy of 97.88% at the 1st level and an overall accuracy of 98.11% at the 2nd level with dipeptide composition. Benchmarking on independent datasets showed that NRfamPred had comparable accuracy to other existing methods, developed on the same dataset. Our method predicted the existence of 76 NRPs in the human proteome, out of which 14 are novel NRPs. NRfamPred also predicted the sub-families of these 14 NRPs.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

NRfamPred: A proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families

Kumar

Kumari

Srivastava

et al. 2014

Sci Rep

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“…[5] also uses split AAC for mitochondrial prediction. Gao et al [31] propose a nuclear receptor sub-families predicting method using PseAAC as the feature expression. PseAAC has also been used for effective prediction of antiviral peptides [32] using Adaboost algorithm with five different base classifiers, i.e., RBF, naive bayes, J48, REPTree and decision stump.…”

Section: A Protein Classificationmentioning

confidence: 99%

Accurate Classification of Biological Data Using Ensembles

Bhardwaj

Dash

Bhatnagar

2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

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Predicting the class to which a given protein sequence belongs is a challenging research area in bioinformatics. Machine learning techniques have been successfully applied to protein prediction problems like allergen prediction, mitochondrial prediction and toxin prediction. Physicochemical properties derived from sequences of amino acids have been commonly used for this purpose.In this paper, we propose an SVM based ensemble method for classification of protein datasets. The constituent classifiers of the ensemble are generated in a sequential manner, each one attempting to rectify mistakes made by previous one. The ensemble is aptly called Self-Chastisting Ensemble (SCE) because of the iterative refinement each classifier carries out over the previous one. We present two versions of the algorithm: SCE-Bal for balanced datasets and SCE-Imbal for imbalanced datasets. Empirical results further demonstrate that the algorithm delivers superior performance using simple and computationally efficient features (amino acid composition and dipeptide composition) compared to other machine learning methods using complex feature sets.

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“…It is effectively used by various authors of the papers for the prediction of nuclear receptors [14], DNA binding sites [43] subcellular localization [44][45][46] enzyme functions [47][48][49] and GProtein-Coupled Receptors [20,21]. In this paper the pseudo amino acid composition is calculated by using the three properties: hydrophobicity (H1), hydrophilicity (H2) and side chain mass (M) of each 20 amino acid to represent the sequence order correlation between all of residues which are separated by 1 to 30 residues.…”

Section: Pseudo Amino Acid Composition (Paac)mentioning

confidence: 99%

“…Ion channels play an important target for antiepileptic drug design, antihypertensive and antipsychotics disorder such as schizophrenia [3,4]. Currently, there are various computational intelligence techniques based approaches that have been proposed to predict membrane proteins [5][6][7][8][9][10][11] .Similarly various computational intelligence techniques based approaches have been proposed to predict nuclear receptor [12][13][14][15][16][17] and G-protein coupled receptor [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%