SUKHEN DAS MANDAL
Articles written in Journal of Biosciences
Volume 41 Issue 4 December 2016 pp 743-750 ARTICLE
Pluripotency is a unique property of stem cells that allows them to differentiate into all types of adult cells or maintainthe self-renewal property. PluriPred predicts whether a protein is involved in pluripotency from primary proteinsequence using manually curated pluripotent proteins as training datasets. Machine learning techniques (MLTs) suchas Support Vector Machine (SVM), Naïve Base (NB), Random Forest (RF), and sequence alignment techniqueBLAST were used in our study. The combination of SVM and PSI-BLAST was our proposed best model, whichobtained a sensitivity of 77.40%, specificity of 79.72%, accuracy of 79.2%, and area under the ROC curve was 0.82using 5-fold cross-validation. Furthermore, PluriPred gives the confidence of the prediction from training dataset’sSVM score distribution and p-value from BLAST. We validated our proposed model with the other existing highthroughputstudies using blind/independent datasets. Using PluriPred, 233 novel core and 323 novel extended corepluripotent proteins from mouse proteome, and 167 novel core and 385 extended core pluripotent proteins fromhuman proteome, were predicted with high confidence. The Web application of PluriPred is available from bicresources.jcbose.ac.in/ssaha4/pluripred/. Many pluripotent genes/proteins take part in protein-protein networks associatedwith stem cell, cancer, and developmental biology, and we believe that PluriPred will help in these research.
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