Publications by authors named "Vijayaraghava Seshadri Sundararajan"
Article Synopsis
- Delayed detection of cancers, especially oral cancers, leads to poor patient outcomes, highlighting the need for better diagnostic methods.
- The study focuses on analyzing NCBI's oral cancer datasets to identify relevant attributes, such as genes and clinical significance, for improving the understanding of gingivobuccal cancer (GBC).
- By employing machine learning techniques, this research aims to uncover critical genes linked to GBC, potentially enhancing detection methods not just for GBC but for other oral cancers as well.
Hypothetical Proteins [HP] are the transcripts predicted to be expressed in an organism, but no evidence of it exists in gene banks. On the other hand, long non-coding RNAs [lncRNAs] are the transcripts that might be present in the 5' UTR or intergenic regions of the genes whose lengths are above 200 bases. With the known unknown [KU] regions in the genomes rapidly existing in gene banks, there is a need to understand the role of open reading frames in the context of annotation.
View Article and Find Full Text PDFBackground: Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted.
View Article and Find Full Text PDFHypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point classification system to validate protein interactions based on diverse features.
View Article and Find Full Text PDFThe demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.
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