Data mining, Regression models, Response surface, Prediction models, Classification, Association Studies, QTL mapping, Marker Assisted Selection, GE Interaction

Data Modeling

We have outstanding experience in applying data modeling techniques from Pharmacogenomics to agriculture industry, from predict disease status from gene expression profiles, disease association studies, and evaluation of candidate markers to forecast adaptability of a new crop variety.

A very commonly used catch phrase today is extracting ‘knowledge from data’. Extracting knowledge or information from huge volumes of data generated from various sources over the years, needs statistical processes such as modeling, which involves search for consistent patterns and systematic relationships between variables, and validating the patterns by applying these to new datasets.

At Ocimum, we have an expert team of innovative and research driven statisticians who use new techniques to blend with conventional statistical analyses. Along with simple linear and logistic regression models, methods like Support Vector Machines (SVM) and neural networks are used to build models that are data-driven and solution-specific. We believe that constant interaction with caustomers help us understand individual requirements and improve the performance of our models.

We have had an opportunity to collaborate with the finest companies in the pharma, biotech and agriculture industry to provide effective solutions from their vast amounts of information rich data. In pharmacogenomics, modeling has been used to predict disease status from gene expression profiles, disease association studies, and evaluation of candidate markers. In agricultural industry, predictive models have been used to forecast adaptability of a new crop variety, crop response to fertilizers, heritability of traits and QTL mapping. With our expertise, we ensure that our models are informative with high predictive accuracy.

Using data models, we can do the following: