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A Survey of Biological Data in a Big Data Perspective
Gabriel Dall'Alba1,2, Pedro Lenz Casa1, Fernanda Pessi de Abreu1
1Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil.
Big Data
|April 8, 2022
Summary
Big data technologies, including cloud computing and machine learning, are revolutionizing biology. This survey explores their application in handling complex biological data for a deeper understanding of life mechanisms.
Area of Science:
- Bioinformatics and Computational Biology
- Data Science and Machine Learning
Background:
- The exponential growth of data necessitates advanced concepts like big data.
- Key technologies include cloud computing for infrastructure and Not Only SQL (NoSQL) for data storage.
- Machine learning algorithms (e.g., decision trees, support vector machines, neural networks, clustering) show promise for data analysis.
Purpose of the Study:
- To provide a concise survey of big data technologies applied in the biological sciences.
- To describe fundamental information technology concepts relevant to biological big data.
Main Methods:
- Review of big data technologies: cloud computing and NoSQL databases.
- Exploration of machine learning algorithms for biological data analysis.
- Discussion of information technology aspects: storage, analysis, and data sharing.
Main Results:
- Big data applications in biology leverage numerous biological databases.
- Biological big data presents challenges due to complexity and heterogeneity.
- Despite challenges, big data aids in understanding life's underlying mechanisms.
Conclusions:
- Big data technologies offer significant potential for advancing biological research.
- Addressing the complexity and heterogeneity of biological data is crucial for successful application.
- Continued development and application of these technologies promise deeper insights into life sciences.
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