AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties

Kandaswamy, Krishna Kumar; Chou, Kuo‐Chen; Martinetz, Thomas; Möller, Steffen; Suganthan, Ponnuthurai Nagaratnam; Sridharan, Sridha; Pugalenthi, Ganesan

doi:10.1016/j.jtbi.2010.10.037

Cited by 221 publications

(175 citation statements)

References 85 publications

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“…The problems can be understood as how to construct an effective feature model, and whether the algorithm performance can be improved. This is because the existing classifier algorithm, such as neural network, (SVM) [8], random forest [9], K-nearest neighbor (KNN) [10], can highly effectively deal with biological problems.…”

Section: Methodsmentioning

confidence: 99%

iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests

Xiao¹,

Ju²,

Hui³

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. Inoxidizability of proteins is one of the most basic function attribute, and shares a sustainable effect for biological process in protein repair and regulate redox-sensitive signaling pathways. In the genome era, however, it is urgent to design an effective computation method to rapidly detect the antioxidant proteins based on sequence information due to the addition of the larger amount of sequence. We designed a novel automations computational algorithm named "iANOP-Enble". In this predictor, the protein sample was formulated by protein similarity scores matrix and amino acid prosperities information into Random Forest. The process of the new predictor algorithm to identify antioxidants protein is designed as a voting system, which consists of eleven sub-classifiers. In order to verify our algorithm availabilities, we adopted a fair comparison method that used the same bench data set. Finally, the result shows that our algorithm is more promising than existing method on the basis of the same standard of comparison

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Section: Methodsmentioning

confidence: 99%

iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests

Xiao¹,

Ju²,

Hui³

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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“…We obtained AFP dataset used for the development of AFP-Pred [5]. Briefly, antifreeze protein sequences were collected from seed proteins of the Pfam database [18], enriched by performing Position Specific Iteration -Basic Local Alignment Search Tool (PSI-BLAST) [19] with a string threshold (E-value) of 0.001, and followed by manual inspection for presence of AFPs.…”

Section: Datasetmentioning

confidence: 99%

“…Since these proteins hold a promising scope for wide range of biotechnological applications in industry, medicine, food technology, cell lines and organ preservation, cryosurgery and transgenics, gaining knowledge into their functional mechanisms has become increasingly essential [5]. 4 for prediction.…”

Section: Introductionmentioning

confidence: 99%

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Pseudo amino acid composition improves antifreeze protein prediction

Mondal

Pai

2014

Preprint

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Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area did not cover introduction of sequence order information, known to represent important properties of various proteins and protein systems for prediction of their attributes. In this study, the effect of Chou's pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youden's Index (Sensitivity + Specificity -1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.

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“…This is because almost all the existing machine-learning algorithms, such as "Neural Network" or NN algorithm [1][2][3] "Support Vector Machine" or SVM algorithm [4][5][6][7][8][9][10][11][12] "Nearest Neighbor" or NN algorithm [13,14] and "Random Forest" algorithm [15][16][17][18][19][20][21][22] can only handle vectors but not sequence samples as elucidated in a review paper [23]. Unfortunately, if using the sequential model, i.e., the model in which all the samples are represented by their original sequences, it is hardly able to train a machine learning model that can cover all the possible cases concerned, as elaborated in [24].…”

Section: Introductionmentioning

confidence: 99%

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

Liu¹,

Wu²,

Chou³

2017

Self Cite

129

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Pse-in-One 2.0 is a package of web-servers evolved from Pse-in-One (Liu, B., Liu, F., Wang, X., Chen, J. Fang, L. & Chou, K.C. Nucleic Acids Research, 2015, 43:W65-W71). In order to make it more flexible and comprehensive as suggested by many users, the updated package has incorporated 23 new pseudo component modes as well as a series of new feature analysis approaches. It is available at http://bioinformatics.hitsz.edu.cn/Pse-in-One2.0/. Moreover, to maximize the convenience of users, provided is also the stand-alone version called "Pse-inOne-Analysis", by which users can significantly speed up the analysis of massive sequences.

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AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties

Cited by 221 publications

References 85 publications

iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests

iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests

Pseudo amino acid composition improves antifreeze protein prediction

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

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