In genomics, the challenging task is to annotate the molecular machinery bi-products. While have insight into genome scale, genomes are structurally annotated into multiple components, further those were functionally interpreted by different functional terms by detail characterization. Most of those were transferred to the multiple organism through finding the homologues in sequence or phenotypes. This is the general phenomena in biological research.
Currently, in the data centric world, most of the data are converted into knowledge for the specific cluster, rather than deriving a common principle to all, by looking into specific clusters and sub-cluster, particularly in the field of molecular biology. Currently the peptides are getting detail functional annotation by using the classification machines with more specific features (which obtained from the given data set). In this page I am trying to elaborate the methods which developed by machine learning application to annotated the peptides in different application under the life tree.
- Plant Long Non-coding RNA predictions (PlncRRO)
- Plant Biosynthetic Gene Clusters Identifications (plantiSMASH)