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Articles by Neeraja M. Krishnan
Fruit-In-Sight: A deep learning-based framework for secondary metabolite class prediction using fruit and leaf images
Citation: Krishnan NM, Kumar S, Panda B (2024) Fruit-In-Sight: A deep learning-based framework for secondary metabolite class prediction using fruit and leaf images. PLoS ONE 19(8): e0308708. https://doi.org/10.1371/journal.pone.0308708 Editor: Eugenio Llorens, Universitat Jaume 1, SPAIN Received: March 15, 2024; Accepted: July 29, 2024; Published: August 8, 2024 Copyright: © 2024 Krishnan et al.
Fruit-In-Sight: a deep learning-based framework for secondary metabolite class prediction using fruit and leaf images
Abstract Fruits produce a wide variety of secondary metabolites of great economic value. Analytical measurement of secondary metabolites is tedious, time-consuming and expensive. Additionally, metabolite concentration varies greatly from tree to tree, making it difficult to choose trees for fruit collection. The current study tested whether deep learning-based models can be developed using fruit and leaf images alone to predict a metabolite's concentration class (high or low).
Building flexible and robust analysis frameworks for molecular subtyping of cancers
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Building flexible and robust analysis frameworks for molecular subtyping of cancers
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Building flexible and robust analysis frameworks for molecular subtyping of cancers
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Photosystem genes in chloroplast and their interacting partners: A case for molecular adaptation to carnivory in Nepenthaceae.
New Results doi: https://doi.org/10.1101/2022.05.24.493228 Abstract Molecular adaptations are prevalent in carnivorous plants in response to habitat and environmental stress. We used the chloroplast genome and characterized the specific adaptations in the photosystem genes and their interacting partners in Nepenthes khasiana, a carnivorous pitcher plant.
CAFE MOCHA: An Integrated Platform for Discovering Clinically Relevant Molecular Changes in Cancer-An Example of Distant Metastasis- and Recurrence-Linked Classifiers in Head and Neck Squamous Cell Carcinoma | JCO Clinical Cancer Informatics
With large amounts of multidimensional molecular data on cancers generated and deposited into public repositories such as The Cancer Genome Atlas and International Cancer Genome Consortium, a cancer type agnostic and integrative platform will help to identify signatures with clinical relevance. We devised such a platform and showcase it by identifying a molecular signature for patients with metastatic and recurrent (MR) head and neck squamous cell carcinoma (HNSCC).
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