Prashantha Nagaraja

Biochemistry, Bioinformatics, Biotechnolog Trainer
Male, 26 Years| CHENNAI - 600026, India

Member since: Feb 22, 2009

Last active on: Sep 11, 2010 at 10:30 AM (EST)

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About Prashantha Nagaraja

I am a Prashantha Completed Bioichemistry and Bioinformatics Masters Degree and 2 years Teaching and 4 years Research Experience in this field.

Teaching Preferences

Online Teaching

One-on-One at $20-30 per hour

Group Teaching at $10-20 per hour

In Person Teaching

One-on-One at $30-40 per hour

Group Teaching at $30-40 per hour

Offers free trial classes

Teaches following Subjects/Exams
Biochemistry of Gene Expression (Bachelor of Science: Biochemistry)
Language of Instruction: English
Teaching Experience

3 Years Teaching and Research Experience

Center for Bioinformatics Research Institute, India

Jun 2006 - Aug 2010

Biochemistry, Bioinformatics, Biotechnology, Molecular Biology

Professional Experience
Research Associate
center for Bioinformatics Research Institue, Tamil nadu, India
Jun 2008 - Present

Director and Research Associate for Bioinformatics Research, Maintaining academic, and commercial projects in companies.

Education

Masters Degree

University of Madras, India

Jun 2006 - May 2008

Professional Exams & Certifications

Certificate Bioinformatics

Center for Bioinformatics Research Institute, India

Year Of Passing: 2008

Diploma in Bioinformatics

Tamil nadu, India

Year Of Passing: 2008

Publications and Research
Analysis of gene expression data using MATLAB Software
Prashantha.C.N*, Pricilla*,

DNA microarray technology is the latest and the most advanced tool for parallel measuring of the activity and interactions of thousands of genes. This modern technology promises new insight into mechanisms of living systems, for example only two high-density oligonucleotide microarrays are sufficient to inspect the whole human genome. However, it provides unprecedented amount of data that require application of advanced computational methods. The appropriate choice of data analysis technique depends both on data and on goals of an experiment. In this paper we focus on two promising methods: singular value decomposition and support vector machines. We discuss the possibility of application of these methods for different purposes; particularly for clustering, classification, feature selection and modeling of dynamics of gene expression. We use for testing presented approaches existing data sets, which are widely available via Internet, and one new tumor/normal thyroid microarray data set.

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