List of Applications of Big Data In Biotechnology

Plants, animals, and microbes have been used by humans for nutrition and development of products for consumption. Biotechnology is the application that uses living processes and biological systems and their derivatives to obtain and produce the products to increase the quality of human life. The ultimate goal of this field is to improve the product yield from living organisms either by employing principles of bio-engineering/bioprocess technology or by genetically modifying the organisms. One example is the production of bread or other bakery items from wheat flour after adding yeast as fermenting organisms.

Application of Big Data in Biotechnology

However, the field of science needs results, information, and statistics for research, to grow and discover something new. In the same way, biotechnology research relies on a lot of information. Lately, data analytics, business intelligence, and research and development are the most reliable tools for almost any field of research and growth. The possibilities of using the technology of analyzing large collections of information in Big Data database systems in medicine have been increasing in recent years. And all the development of the other applications of biotechnology like agriculture, genetic engineering can be made even better and efficient due to the applications of Big Data database for its research. Big Data is like a virtual library where an enormous space of data and information is stored and analyzed. And researching further on it in a particular way can help the biotech to improve and attain even more success in a short duration of time with less effort.

List of Applications of Big Data In Biotechnology

Biotechnology Could Largely Benefit By the Use of Big Data. Here are various applications of Big Data in the field of biotechnology.

1. Genomics

The Genome Project, especially for the human, took over a long time of worldwide research and support to identify the 20,000 plus genes and sequence of all 3 billion genome bases. This project costs billions of dollars globally, but today’s biotechnology companies use the Big Data database that can decode entire genomes for just thousands of dollars. The genomics market helps different data companies that use frameworks and tools to conduct huge and complicated computing tasks to analyze genetic, medical, and biological data. These companies often work with computer hardware giants to improve their application performance and their Big Data analysis results.

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2. Agriculture

Big data can also be quite an application in the field of agriculture. Data gathered from GPS technology are stored in the framework of Big Data, and multiple GPS enabled tractors can help farmers to cope with the changing environmental condition by implementing farming precisely. Data analytics is also changing the landscape of the biotech industry with its contribution to genetic research in creating genetically modified organisms. Such engineered crops can be modified with inputs from data collection from Big Data to improve crop yield, survive changing conditions, and disease-free plants are obtained.

3. Pharma Automation

As per almost every pharma company, it receives millions of compounds before selecting to appropriate for the pre-clinical trials. For the journey to successful drug discovery, it consumes an enormous amount of time and money. So there are many software tools that help inefficiency and less time for drug discovery. Big Data based modeling uses large size and storage like terabytes of data and information of different compounds and their characteristics. Therefore it acts as a virtual library that has information of millions of compounds to identify the compounds that will most likely experience success. These predictive modeling programs compare the trial criteria and desired outcomes against the target disease and chemical structures. Pharma automation reduces risks, saves money, and offers faster research-to-market cycles.

4. Healthcare

Technically the healthcare sector of biotechnology has lagged behind than others in the use of Big Data database. Healthcare stakeholders now have access to promising new threads of knowledge. This information in the form of Big Data gives complexity, diversity, and timelines. Pharmaceutical industry experts analyze big data to obtain insights. With these technological advances in the biotech industry have improved. Their ability to work with such data, even though files are enormous and have different database organizations that increase the condition and the rate of development of pharmaceutical healthcare.

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5. Crowdsourcing

According to Wikipedia, crowdsourcing is the sourcing model in which individuals or organizations obtain goods and services. These services include ideas and finances form a large, relatively open, and often rapidly-evolving group of internet users. It divides work between participants to achieve a cumulative result. Therefore it is commonly used outsourcing labor and entrepreneurial projects. Some pharma companies have created online gaming platforms that involve disease profiles, research challenges, and solving medical puzzles. With crowdsourcing, patients drove research works through the online surveys that empower the consumer to conduct their own studies and research, upload their own medical data and contribute knowledge about their condition and symptoms to benefit the whole medical community.

6. Business Development

Every day the body of information on scientific discoveries and pharma progress from the different sources, presenting an enormous flood of data for biopharma industries to sift through to find potential licensing opportunities. Some Big Pharma and biotech companies have turned to analytics and data-mining technologies to scour disparate Big Data sources and deliver the exact information they seek. This Big Data always grows and develops along with the business. Therefore Big Data increases the total revenue and profit of the business and thus develops biotech business.

7. Sentiment Analysis

Among the tools of Big Data, sentiment analysis is one that helps to analyze social networking posts and comments. Organizations primarily use it for marketing, advertising, and public relations research. For example, many companies use it to find the reaction of the consumer and get their feedback. However, social media platforms contain millions of health-related comments because health care consumers are sharing personal and public information about diseases and medical conditions. Some companies are creating an online group and community to centralize and uncover new discoveries and technologies. When used together with crowdsourcing, these tools provide sources of free labor and infinite information.

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8. Prevention of Drug Fraud

Every day in developing countries, fake drugs kill many people and affect their health condition. Due to this sad scenario, patients and their family has lost hope from different pharmaceutical companies and lose sales. World Health Organization estimates that 700,000 patients in Africa perish as a result of dummy versions of anti-malaria and tuberculosis meds, and the problem costs drug makers $75 billion annually. The killer problem has pushed the startup Sproxil to work with tech giant IBM ($IBM) to enable drug companies to analyze Big Data sources to spot patterns of counterfeit drug activity.

Sproxil aims to amass large amounts of transactional data with a system that enables patients to text-message codes from medicine bottles to learn whether the meds are authentic. With IBM’s visualization tech and other analytics, drugmakers can tap a large amount of data on drug transactions in real-time, according to Big Blue. Presumably, prescription drug frauds can be spotted.

9. Discovering Genetic Biomarkers

There are different genomic analysis tools that identify DNA code variants and the genetic biomarkers of disease risk factors. Some Big Data analytics and informatics systems are capable of integrating multiple data types together for enhanced results. The ability to correlate large data warehouses, phenotypic, and genomic data, provides a clearer understanding of disease factors, symptoms, and development. There are software solutions that allow researchers to view sequence alignments and disease data alongside objective findings. Therefore Big Data helps in discovering Biomarkers and helps in identifying and curing the diseases.

In Conclusion

The prime regions enlisting global biotech industries are in the US and Europe, where there are over 700 companies and over 200 thousand employees that generate around 140 billion U.S. dollars of revenue. In this 21st century, the health care sector is growing as another sector of a promising economy where technology for collecting and processing a large amount of data and information in Big Data database systems are applied. With the help of Big Data and its applications in the field of biotechnology, the growth and innovations in biotech would rise even more rapidly, alongside delivering all the great promises of its true potential.

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