Sunil Pranit Lal

TL;DR: A new predictor called PSSM-Suc is proposed which employs evolutionary information of amino acids for predicting succinylated lysine residues using profile bigrams extracted from position specific scoring matrices and showed a significant improvement in performance over state-of-the-art predictors.

TL;DR: An approach that utilizes structural features of amino acids to improve lysine succinylation prediction, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient significantly improved.

TL;DR: A novel classification approach is proposed, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction, and made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues.

TL;DR: A novel computational predictor called ‘Success’ is proposed, which efficiently uses the structural and evolutionary information of amino acids for predicting succinylation sites and outperform three state-of-the-art predictors in succinylated residues detection.

TL;DR: A comprehensive review of the electroencephalogram (EEG) based MI-BCI system can be found in this article, where the authors discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.