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Data Science in Biotechnology

Humans have been researching bio-technology since the moment we began breeding animals and customizing the crops for the same purpose. We can define  biotechnology as  a study of  biological systems , living organisms, or derivatives thereof, to create or manipulate products for specific use.

As of the 21st century, our technological tools like math, statistics and  computational resources have advanced and improved remarkably. 

Our biological information has also increased as we are knowing much more about the molecular relations at the genomic level and can make use of predictive models, for determination of the likely outcomes of customizing the cellular realm.  Due to variation in data it might be difficult to predict each and every possible outcome based on a set of internal and external features where an array of possible input factors are feeded. For crops, the prediction accuracy is higher as compared to complex biological systems, e.g. humans. But, the pharma industry is a great example of biotechnology as applied to human neurophysiology .

BioTech + Data Science

A biotechnologist is a research scientist who makes use of his statistical tools and technologies on molecular biology. If you are thinking that they are basically data scientists within a highly specific sector, you’re correct. Also, there is the ever-present trio that comes into the picture that is math, statistics, and programming. With regard to statistics, the focus is on biostatistics, which is a specialized form of statistics. But, a data scientist with good skills in statistics and mathematics, can jump into biostatistics easily.

How Data Science helps in Biotechnology?

From finding ways to cure cancer or any of the other fatal diseases to creating products that are safer for the environment, oceans, land, and air. While data science is continuing in its evolutionary path, it is not straying away from the fundamentals of : 

  1. Asking queries
  2. Collection of data
  3. Data Preparation
  4. Model Selection
  5. Model Testing and Fine Tuning
  6. Model deployment in a larger production environment
  7. Monitoring and optimizing the model

Data Science in Genome Sequencing

Data scientists might provide some high level advancements in medical biotechnology by utilizing genome sequencing for predicting a disease. A unique health profile can be created or generated based on both genomic and lifestyle data. If this model is deployed on a health and fitness application where the user can be alerted if certain foods or activities increase their risk of a particular disease, then early detection may reduce medical costs.

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