The scenario of biotechnology is changing very rapidly. Similarly, information technology is also growing at an equal pace. The combination of biology along with information technology has created a new branch of biology which is called bioinformatics. Along with bioinformatics, the experiment biologists are generating huge amounts of data in the development of biotechnology. In this current research growth era, medical science is also not lagging. The medical professionals, along with the molecular biologists, are also contributing to the data science. Therefore, there is an urgent need to store and analyze the big data generated by scientists and medical professionals.
Biotechnology and data science
Presently, in the field of biotechnology, large amounts of data are generated and stored. A famous journal from the Oxford Academic entitled “Nucleic Acids Research” indicates that more than 2000 operating databases are present in the existing condition. Among them, 215 databases contain information about protein sequence and database. 175 databases reveal the relationship between human genes and diseases. Near about, 160 databases discuss nucleotide sequences (DNA). Approximately, 170 databases highlight the metabolic and signaling pathways. About 100 gene expressions discuss the topic regarding the RNA sequences. Near about 60 databases discuss gene expression and microarray. Approximately, 30 databases contain information about proteomics.
The scientists from the European Bioinformatics Institute (EBI) of the European Molecular Biology Laboratory (EMBL), Europe analyzed the data deposited every year. Most interestingly, they found that the data is roughly doubling every year. The significant growth in biotechnological data is a big challenge for organizations to store and analyzed. Furthermore, electronic health records and medical data are also important to store and analyzed, generating a growing demand for data analysts in the hospitals.
Big data storage – processer and server up-gradation
The only way to solve the huge amount of data storage problem is to upgrade the server. EBI expands the hardware and data storage. But, what will happen for the micro-processors? It also needs up-gradation for a faster computation process. However, researches are trying to understand how big data can be stored in very tiny space and quick data processing capacity can be extended. Google has started research in this direction. For this purpose, a new center has been opened at Purdue University, USA, and another at the University of Virginia, USA.
Big data integration and different software and protocols
Several databases offer machine‐readable interfaces, entitled as web services. They can permit the users to request automated data generation and the researcher can generate automated data through the data integration process. Several Web services utilized a range of protocols such as AJAX-Asynchronous JavaScript and XML; and SOAP –Simple Object Access Protocol.
Analysis of big data and amalgamation of big data with other fields
Manual data analysis is now an old fashioned way. Therefore, scientists are trying to statistically analyze a huge set of data through artificial intelligence (AI). It has been proven that AI is a much faster way for data analysis and calculation. A recent editorial from Nature journal suggested that particle physics needs to combine along with the field of genomics, neuroscience, and drug discovery to understand more about the molecular interaction and it will be much faster if we use AI.
AI employs several computation algorithms for more speedy calculations. For example, AI can use machine learning algorithms to analyze gene expression values. For high dimensional image analysis, AI uses deep learning methods. Bioprocesses and mathematical modeling can be optimized through the use of AI. Using big data analysis, a hospital scan monitors the patient progress and analyzes the response of their treatment plans, advocating the advantages of AI.
Drug discovery is another field where we can carry out databases scanning for the generation of a possible new drug candidate with the help of AI. The database scanning will be much faster if we use AI technology. Similarly, machine learning technology along with the AI can be used for Genome-Wide Association Studies (GWAS). There are plenty of examples where scientists are trying to utilize AI in the biotechnology field.
“Trained” manpower is needed
AI has already started to create a demand for more jobs. It has been analyzed that by 2022, approximately 60 million jobs will be created in the field of AI. The chunk portion of the job will be from the medical sciences and life sciences, thus creating more jobs in the field of biotechnology. Therefore, there is an urgent need to train the manpower more and more in this direction. It is the time when students should learn both AI and biotechnology so that we can fulfill the upcoming demand of the trained manpower.
In conclusion, we can say that big data analysis and AI will be the future of biotechnology research. Bioinformaticians need to know about big data analysis and AI. Therefore, it is the perfect time for biotechnologists that they should collaborate with computer scientists to explores big data using AI.
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