Akanksha Rajput

About

Computational biologist with over 10 years of proficiency in Python, R, multi-omics data analysis, prediction algorithm, cheminformatics, phylogenomics, database and webserver development. Experienced in the field of biofilms, antimicrobial resistance, and virology. Experimental skills include exploring bacterial systems like developing biofilms and checking the effect of inhibitors. Equally capable of working independently and as an adaptable team member with solid written and oral communication skills anchored by a strong publication record.

Employment

• Cincinnati Children's Hospital Medical Center Senior Bioinformatics Scientist
  2024 - Present
• St. Jude Children's Research Hospital Senior Computational Research Scientist
  2023 - Present
• University of California San Diego Postdoctoral Scholar
  2019 - 2023
• Institute of Microbial Technology CSIR Research Associate
  2018 - 2018
• Institute of Microbial Technology CSIR Project Assistant III
  2017 - 2018

Education

• Institute of Microbial Technology PhD
  2012 - 2017
• Banasthali University Bachelors
  2006 - 2009

Projects & Funding

Projects & funding information is unavailable.

Publications (35)

• Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning.
  mSystems 2024 DOI: 10.1128/msystems.01257-23
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• ppHiC: Interactive exploration of Hi-C results on the ProteinPaint web portal.
  Computational and structural biotechnology journal 2024 DOI: 10.1016/j.csbj.2024.09.020
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• Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing
  Viruses 2023 DOI: 10.3390/v16010045
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• Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family
  2023 DOI: 10.1101/2023.05.16.541042
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• Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa
  2022 DOI: 10.1101/2022.04.04.487074
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• Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators
  Nucleic Acids Research 2022 DOI: 10.1093/nar/gkac187
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• Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis
  Computers in Biology and Medicine 2021 DOI: 10.1016/j.compbiomed.2021.104677
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• Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning
  Molecular Diversity 2021 DOI: 10.1007/s11030-021-10291-7
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• Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators
  2021 DOI: 10.1101/2021.07.28.454220
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• DrugRepV: a compendium of repurposed drugs and chemicals targeting epidemic and pandemic viruses
  Briefings in Bioinformatics 2021 DOI: 10.1093/bib/bbaa421
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