Browsing "Physiology" by Keyword : Algorithms

All A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:
  • Sort by:
  • In order:
  • Results/Page
  • Authors/Record:

Showing results 1 to 13 of 13

Pub YearTitleAuthor(s)
20194mCpred-EL: An Ensemble Learning Framework for Identification of DNA N(4)-methylcytosine Sites in the Mouse GenomeBalachandran, Manavalan, Basith, Shaherin, 신태환, 이광
2023Computational prediction of protein folding rate using structural parameters and network centrality measures이광
2020i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genomeBalachandran, Manavalan
2020i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representationBalachandran, Manavalan
2021Integrative machine learning framework for the identification of cell-specific enhancers from the human genomeBalachandran, Manavalan, Basith, Shaherin, 이광
2019Iterative feature representations improve N4-methylcytosine site predictionBalachandran, Manavalan
2020Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screeningBalachandran, Manavalan, Basith, Shaherin, 신태환, 이광
2019mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representationBalachandran, Manavalan, Basith, Shaherin, 신태환, 이광
2021Meta-i6mA: An interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning frameworkBalachandran, Manavalan, Basith, Shaherin, 이광
2021NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learningBalachandran, Manavalan
2019Prediction of S-nitrosylation sites by integrating support vector machines and random forestBalachandran, Manavalan
2021StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptidesBalachandran, Manavalan
2021Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learningBalachandran, Manavalan
1

Browse