In Umbrella Genetics, we strive to leverage all available aspects of computational biology techniques for applications to data analytics for ag-biotech product development.

To this end- once a high-projected value(HPV) product development target is identified, we integrate available data across public and inhouse data sets from genetic, phenotypic and genomic domains to enable predictive modelling for germplasm improvement.

The preliminary data directed evaluation of HPV targets not only enables derisking and informed experimental design for product development, but also provides a framework where structured project design and execution practices can be applied.

Analytical framework development for crop improvement target discovery and validation.

Analytical pipeline design and implementation for piloted methods, custom designed for clients’ projects.

Genetic, genomic and phenotypic data integration and analysis from unbalanced data sets.

In silico simulation study design and execution for optimization of experimental parameters.

Descriptive Analytics: Insight into the past

Descriptive analysis or statistics does exactly what the name implies they “describe”, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.

This is often the first step in engineering Big-Data-Analytical systems.

Predictive Analytics: Understanding the future

Predictive analytics provide estimates about the likelihood of a future outcome. It is important to remember that, in this sense- prediction means assignment of a expected probability to each possible outcome. Another word that is often used for predictive analytics is “forecasts”. This is because the foundation of predictive analytics is based on probabilities.

These statistics try to take the data that you have, and fill in the missing data with best guesses, or inferences. They combine historical data found in various information management systems, such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), HR (Human Resources), as well as research specific enterprise solutions for information management such as LIMS (Laboratory Information Management Systems) and FIMS (Field Information Management Systems) to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets.

Prescriptive Analytics: Advise on possible outcomes

The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.

Prescriptive analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action, depending on the constrains on the system. Essentially they predict multiple futures and allow researchers to assess potential outcomes based upon their actions.

Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.

Prescriptive analytics are relatively complex to administer, however when implemented correctly, they can have a large impact on how businesses make decisions.
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