Priyanka B

Priyanka B

To comprehend and learn the subject which utilize my talent and help me in emerging as an eminent...
India

About Me

To comprehend and learn the subject which utilize my talent and help me in emerging as an eminent scholar, where I can absorb maximum knowledge and excel up to the limit.

Experience

Project Associate

CSIR - Niist
Mar 2021 - Present · 5 years 3 months

Big data analyst (Microbial data) by using supervised learning algorithms

Identification of Genes and Pathways Involved in Hypocholestremic Drug responsiveness in Human cells by RNAi based Functional Pharmacogenomics

ICMR (Indian council of Medical Research)
Present

Project Fellow

JN-TBJRI
Nov 2016 - Dec 2017 · 1 year 1 month

Project Fellow

Tecnical Assistant

Univesity of Kerala
Jan 2016 - Nov 2016 · 10 months

IT technician

Integrative functional genomic investigation of gastric cancer targeted therapeutics

DBT (Department of Biotechnology)

Evolutionary route of host attacking genes in Rhizobium

CSIR (Council of Scientific and Industrial Research)

Identification and validation of EST-SSR markers in solanum lycopersicum

Department of Computational Biology and Bioinformatics

Transcriptome analysis using Blast and DIAMOND – A comparative study

Scigenome labs Pvt Ltd

Research Assistant

Department of Genomics, Madurai Kamaraj university
Dec 2017 - Jun 2020 · 2 years 6 months

Big data analyst (cancer) by using supervised learning algorithms

Skills

Python Statistics Machine Learning Deep Learning Biostatistics Problem solving Decision making Big-data Analysis Linear Regression Logistic Regression Decision Trees Random Forests K-Nearest Neighbors Naïve Bayes Support Vector Machines (SVM) Ensemble Techniques Principal Components Analysis K-Means Clustering Hierarchical Clustering Time Series Artificial Neural Network (ANN) KERAS Tensorflow 2.0 Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) NLTK spaCY Perl Windows Linux Big Data Analytics Random Forest Naive Bayes Support Vector Machines Ensemble Methods Principal Component Analysis Time Series Analysis Artificial Neural Networks Keras TensorFlow 2.0 Convolutional Neural Networks Recurrent Neural Networks Natural Language Processing spaCy
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