MLACP: machine-learning-based prediction of anticancer peptides

Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan; Choi, Sun; Kim, Myeong Ok; Lee, Gwang

doi:10.18632/oncotarget.20365

Cited by 219 publications

(202 citation statements)

References 72 publications

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“…Results from the compositional analyses suggested that integrating the amino acid preference information would be helpful for differentiating between DHSs and non-DHSs, and so, we used these as input features for ML methods to improve classification. The major advantage of ML methods is their ability to consider multiple features simultaneously, often capturing hidden relationships [16][17][18][19][20][21][22][23].…”

Section: Compositional Analysismentioning

confidence: 99%

“…In the second step of the previous section, we used three different ML-based methods instead of SVM, including, RF, ET, and k-NN. A detailed description of the development of prediction models using these methods was provided in our recent studies [21,23]. For each ML-based method, we generated 33 prediction models using different sets of features, including individual composition, hybrid models, and features based on FIS cut-off.…”

Section: Comparison Of Three Ml-based Models With the Svm-based Modelmentioning

confidence: 99%

“…In the first step, we applied the RF algorithm to estimate the importance of each feature. A detailed description of how we estimated the importance of input features has been published previously [21,51,52]. Briefly, we used all the features as inputs for RF and carried out 10-fold cross-validation on the benchmark dataset.…”

Section: Feature Selectionmentioning

confidence: 99%

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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Manavalan

Shin

Lee

2017

Preprint

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DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin.Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application.Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di-and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.not peer-reviewed)

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Section: Compositional Analysismentioning

confidence: 99%

Section: Comparison Of Three Ml-based Models With the Svm-based Modelmentioning

confidence: 99%

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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Manavalan

Shin

Lee

2017

Preprint

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“…Cancer is the second leading cause of death in the world. Therefore, the screening of specific lectins from a large number of lectins is of great significance not only for the discovery of tumor markers and cancer treatment, but also for better understanding and conquering cancer (Balachandran et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide

Qian¹,

Wen²,

Han³

2020

Front. Genet.

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The cancerlectin plays an important role in the initiation, survival, growth, metastasis, and spread of cancer. Therefore, to study the function of cancerlectin is greatly significant because it can help to identify tumor markers and tumor prevention, treatment, and prognosis. However, plenty of studies have generated a large amount of protein data. Traditional prediction methods have been unable to meet the needs of analysis. Developing powerful computational models based on these data to discriminate cancerlectins and non-cancerlectins on a large scale has been treated as one of the most important topics. In this study, we developed a feature extraction method to identify cancerlectins based on fusion of g-gap dipeptides. The analysis of variance was used to select the optimal feature set and a support vector machine was used to classify the data. The rigorous nested 10-fold cross-validation results, demonstrated that our method obtained the prediction accuracy of 83.91% and sensitivity of 83.15%. At the same time, in order to evaluate the performance of the classification model constructed in this work, we constructed a new data set. The prediction accuracy of the new data set reaches 83.3%. Experimental results show that the performance of our method is better than the state-of-the-art methods.

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“…Likewise, several methods to predict therapeutic properties of a protein or peptide such as anti-cancer, anti-microbial, anti-bacterial, anti-fungal, anti-tubercular, anti-hypertensive, toxic, tumor homing etc. have been developed (Manavalan et al, 2017;Tyagi et al, 2013;Meher et al, 2017;Lata, Mishra, and G. P. S. Raghava, 2010;Agrawal et al, 2018;Usmani, Bhalla, et al, 2018;Kumar et al, 2015;Manavalan et al, 2018;Gupta et al, 2013;Sharma et al, 2013). These peptides/proteins have therapeutic properties thus play vital role in designing proteins/peptide-based drugs and vaccines (Dhanda et al, 2017;Usmani, Kumar, et al, 2018;Nagpal et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Computing wide range of protein/peptide features from their sequence and structure

Pande

Patiyal

Lathwal

et al. 2019

Preprint

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MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc. ResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary informationbased features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues. AvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

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MLACP: machine-learning-based prediction of anticancer peptides

Cited by 219 publications

References 72 publications

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide

Computing wide range of protein/peptide features from their sequence and structure

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