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Achuthsankar S. Nair

Researcher at University of Kerala

Publications -  125
Citations -  1059

Achuthsankar S. Nair is an academic researcher from University of Kerala. The author has contributed to research in topics: Medicine & Gene. The author has an hindex of 13, co-authored 114 publications receiving 812 citations. Previous affiliations of Achuthsankar S. Nair include Shiv Nadar University.

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Journal Article

A coding measure scheme employing electron-ion interaction pseudopotential (EIIP)

TL;DR: Better discrimination between exon areas and non-coding areas of a number of genomes when the sequences are mapped to EIIP indicator sequences and the power spectra of the same are taken in a sliding Kaiser window, compared to the existing method using a rectangular window which utilizes binary indicator sequences.
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Green synthesis and characterization of zinc oxide nanoparticles using Cayratia pedata leaf extract.

TL;DR: The synthesis of Zinc oxide nanoparticles using a plant-mediated approach is presented in this paper, where the nanoparticles were successfully synthesized using the Nitrate derivative of the Zinc and plant extract of the indigenous medicinal plant Cayratia pedata.
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MTar: a computational microRNA target prediction architecture for human transcriptome

TL;DR: A novel machine learning architecture, MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets is found to be more comprehensive than the existing methods in predicting mi RNA targets, especially human transcritome.
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Biclustering of gene expression data using reactive greedy randomized adaptive search procedure

TL;DR: A new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (ReactiveGRASP) to detect significant biclusters from large microarray datasets is proposed and experimental results indicate that the Reactive GRasP approach outperforms the basic GRASp algorithm and Cheng and Church approach.
Proceedings ArticleDOI

Composition, Transition and Distribution (CTD) — A dynamic feature for predictions based on hierarchical structure of cellular sorting

TL;DR: A new feature vector is introduced for predicting proteins targeted to various compartments in the hierarchical structure of cellular sorting pathway from protein sequence based on the overall Composition, Transition and Distribution of amino acid attributes.