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Atmakuri Ramakrishna Rao

Researcher at Indian Agricultural Statistics Research Institute

Publications -  120
Citations -  2529

Atmakuri Ramakrishna Rao is an academic researcher from Indian Agricultural Statistics Research Institute. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 21, co-authored 109 publications receiving 1803 citations. Previous affiliations of Atmakuri Ramakrishna Rao include Tata Institute of Fundamental Research & Indian Council of Agricultural Research.

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Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

TL;DR: This study made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy, and achieved higher accuracy than several existing approaches, while compared using benchmark dataset.
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Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

TL;DR: The region harbouring Saltol, a major quantitative trait loci on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na+/K+ ratio measured at reproductive stage in this study.
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Interannual variability of Kelvin wave propagation in the wave guides of the equatorial Indian Ocean, the coastal Bay of Bengal and the southeastern Arabian Sea during 1993-2006

TL;DR: In this article, the observed variability of the Kelvin waves and their propagation in the equatorial wave guide of the Indian Ocean and in the coastal wave guides of the Bay of Bengal (BoB) and the southeastern Arabian Sea (AS) on seasonal to interannual time scales during years 1993-2006 is examined utilizing all the available satellite and in-situ measurements.
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Extensive in silico analysis of NF1 splicing defects uncovers determinants for splicing outcome upon 5′ splice‐site disruption

TL;DR: This study provides valuable predictors for the splicing pathway used upon 5′ss mutation, and underscores the importance of using RNA‐based techniques, together with methods to identify microdeletions and intragenic copy‐number changes, for effective and reliable NF1 mutation detection.