Niranjan M - Search Results

1

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Heinson AI, Gunawardana Y, Moesker B, Hume CC, Vataga E, Hall Y, Stylianou E, McShane H, Williams A, Niranjan M, Woelk CH. Heinson AI, et al. Among authors: niranjan m. Int J Mol Sci. 2017 Feb 1;18(2):312. doi: 10.3390/ijms18020312. Int J Mol Sci. 2017. PMID: 28157153 Free PMC article.

2

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes.

Gunawardana Y, Niranjan M. Gunawardana Y, et al. Among authors: niranjan m. Bioinformatics. 2013 Dec 1;29(23):3060-6. doi: 10.1093/bioinformatics/btt537. Epub 2013 Sep 16. Bioinformatics. 2013. PMID: 24045772

3

Outlier detection at the transcriptome-proteome interface.

Gunawardana Y, Fujiwara S, Takeda A, Woo J, Woelk C, Niranjan M. Gunawardana Y, et al. Among authors: niranjan m. Bioinformatics. 2015 Aug 1;31(15):2530-6. doi: 10.1093/bioinformatics/btv182. Epub 2015 Mar 29. Bioinformatics. 2015. PMID: 25819671

4

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction.

Heinson AI, Ewing RM, Holloway JW, Woelk CH, Niranjan M. Heinson AI, et al. Among authors: niranjan m. PLoS One. 2019 Dec 13;14(12):e0226256. doi: 10.1371/journal.pone.0226256. eCollection 2019. PLoS One. 2019. PMID: 31834914 Free PMC article.

5

Mining protein database using machine learning techniques.

Camargo Rda S, Niranjan M. Camargo Rda S, et al. Among authors: niranjan m. J Integr Bioinform. 2008 Aug 25;5(2). doi: 10.2390/biecoll-jib-2008-106. J Integr Bioinform. 2008. PMID: 20134071

6

CI-SpliceAI-Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites.

Strauch Y, Lord J, Niranjan M, Baralle D. Strauch Y, et al. Among authors: niranjan m. PLoS One. 2022 Jun 3;17(6):e0269159. doi: 10.1371/journal.pone.0269159. eCollection 2022. PLoS One. 2022. PMID: 35657932 Free PMC article.

7

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis.

Parkes GM, Niranjan M. Parkes GM, et al. Among authors: niranjan m. BMC Bioinformatics. 2019 Oct 29;20(1):536. doi: 10.1186/s12859-019-3150-5. BMC Bioinformatics. 2019. PMID: 31664894 Free PMC article.

8

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing.

Stumpf PS, Du X, Imanishi H, Kunisaki Y, Semba Y, Noble T, Smith RCG, Rose-Zerili M, West JJ, Oreffo ROC, Farrahi K, Niranjan M, Akashi K, Arai F, MacArthur BD. Stumpf PS, et al. Among authors: niranjan m. Commun Biol. 2020 Dec 4;3(1):736. doi: 10.1038/s42003-020-01463-6. Commun Biol. 2020. PMID: 33277618 Free PMC article.

9

Reducing the algorithmic variability in transcriptome-based inference.

Tuna S, Niranjan M. Tuna S, et al. Among authors: niranjan m. Bioinformatics. 2010 May 1;26(9):1185-91. doi: 10.1093/bioinformatics/btq104. Epub 2010 Mar 8. Bioinformatics. 2010. PMID: 20212019

10

Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile.

Liu W, Niranjan M. Liu W, et al. Among authors: niranjan m. Bioinformatics. 2012 Feb 1;28(3):366-72. doi: 10.1093/bioinformatics/btr658. Epub 2011 Nov 29. Bioinformatics. 2012. PMID: 22130592

To be in line with recent thinking on the subject, we also analyse this model with a spatial gradient of maternal mRNA, rather than being fixed at only the anterior pole. CONTACT: m.niranjan@southampton.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are availab …

To be in line with recent thinking on the subject, we also analyse this model with a spatial gradient of maternal mRNA, rather than being fi …

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