CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Kalita, Mridul; Nandal, Umesh K.; Pattnaik, Ansuman; Anandhan, Sivalingam; Ramasamy, Gowthaman; Kumar, Manish; Raghava, Gajendra P. S.; Gupta, Dinesh

doi:10.1371/journal.pone.0002605

Cited by 30 publications

(18 citation statements)

References 44 publications

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“…PSSM profiles have been employed for training SVMs for a legion of classification problems, like prediction of cyclins [21], nucleic acid binding residues [22], protein subcellular localization [23] etc.…”

Section: Resultsmentioning

confidence: 99%

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

Ramana

Gupta

2010

PLoS ONE

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Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.

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Section: Resultsmentioning

confidence: 99%

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

Ramana

Gupta

2010

PLoS ONE

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“…Machine learning techniques like SVMs have also been applied to the challenging task of functional annotations of proteins that have very low sequence similarity 41. Cyclins is an example of such proteins and it is a crucial part of cell cycle regulation.…”

Section: Kernel Methods For Biological Sequencesmentioning

confidence: 99%

“…Machine learning techniques like SVMs have also been applied to the challenging task of functional annotations of proteins that have very low sequence similarity. 41 Cyclins is an example of such proteins and it is a crucial part of cell cycle regulation. In the study SVMs are used to analyze the sequences of cyclins and to recognize features that are crucial to discriminate Cyclin and non-Cyclin proteins.…”

Section: Sequence Analysismentioning

confidence: 99%

Computational biology perspective: kernel methods and deep learning

Lodhi

2012

WIREs Computational Stats

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The field of machine learning provides useful means and tools for finding accurate solutions to complex and challenging biological problems. In recent years a class of learning algorithms namely kernel methods has been successfully applied to various tasks in computational biology. In this article we present an overview of kernel methods and support vector machines and focus on their applications to biological sequences. We also describe a new class of approaches that is termed as deep learning. These techniques have desirable characteristics and their use can be highly effective within the field of computational biology. WIREs Comput Stat 2012 doi: 10.1002/wics.1223 This article is categorized under: Applications of Computational Statistics > Computational and Molecular Biology Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks Statistical Learning and Exploratory Methods of the Data Sciences > Support Vector Machines

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“…Every single peptide/protein... Rhythms are classified based on DPC, which compares pairs of residues in a sequence (e.g., AA, AC, and AD). Various PSPD prediction and composition-based algorithms proposed in the literature are using DPC to classify protein sequences [26]. DPC is the composition of measurements for each of the 400 possible dipeptides produced by 20 amino acids.…”

Section: Dipeptide Composition (Dpc)mentioning

confidence: 99%

Pathway-Specific Protein Domains (PSPD) Discrimination by Using a Hybrid Feature Space Based on Deep Neural Network(DNN)

Ali¹,

Lei²,

Zhang³

et al. 2020

Preprint

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The Pathway-specific protein domains (PSPDs) are important tools in examining drug growth as they provide a fast, reliable, and inexpensive way of estimating complex new molecular targets in specific diseases. The protein architecture prevents the formation of a direct correlation between signal transduction behavior and cellular structure. Accordingly, protein–tissue factor pathway inhibitor 2 isotypes 1 precursors have been used to encode peptide sequence information into specific feature structures. The measurable structure-activity classification model obtained by machine learning technology can predict pathway-specific protein interactions and new signaling peptides. We introduce deep neural network (DNN)-based PSPDs, abbreviated as DNNPSPDs, as the first pathway-specific protein domain that is built based on five extant models, namely, the AAindex, pseudo-amino acid composition, amino acid composition, composition mood of pseudoamino acids, and dipeptide composition. A total of 900 proteins with undetermined roles collected from the PDB data base are tested to evaluate the predictive power of this model. Various combinations of the available feature selection technologies are also combined to process a hybrid function space. DNNPSPDs predicts PSPDs by using features that are automatically learned from primary protein sequences. The sequences of pathway-associated proteins are sequentially fed into and decoded in neural network layers. Several classifications are also employed. DNNPSPDs achieves a prediction accuracy of 0.957 at a Matthew’s correlation coefficient (MCC) of 91.86%, with DPC, and 2nd achieve high prediction score 0.936 at Matthew’s correlation coefficient (MCC) of 88.02%, accuracy which is probably better. In terms of ROC–AUC, DNNPSPDs achieves a ROC–AUC curve of 0.982, which is larger than that of the other machine learning classifiers. A study using an alternative dataset reveals that our primary pathways, as pathway-specific protein domains, have accurate and reliable associations, thereby proving the viability of the proposed DNNPSPDs.

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CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Cited by 30 publications

References 44 publications

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

Computational biology perspective: kernel methods and deep learning

Pathway-Specific Protein Domains (PSPD) Discrimination by Using a Hybrid Feature Space Based on Deep Neural Network(DNN)

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