Bioinformatics has become a scientific term in the world today. Ten years ago, people regarded biology and computer science as two completely different fields. The former can understand biology and its functions, while the latter can understand computers and basic theories. The two fields don't seem to intersect. However, this new field-bioinformatics is a perfect combination of computer science and biology. This combination of disciplines is also an inevitable trend. With the implementation of the 1990 Human Genome Project and the development of information technology, all kinds of biological analysis lead to "gene data explosion", resulting in a large number of biological data. It is very difficult to analyze by manual methods, which is an aspect that computer science can save. Various computing technologies are used to analyze biological data more accurately and effectively through automated processes. Therefore, it can be considered that bioinformatics is a subject that uses data science and technology to solve medical problems. It has rapidly developed into the most attractive and important frontier field of life science today. Bioinformatics provides a broader development world for high-quality talents majoring in biology, computer science, mathematics and information science.

Why study bioinformatics?

The main applications of bioinformatics can be found in the fields of precision medicine and preventive medicine. Precision medicine includes medical care technology customized for individual patients, including treatment and practice, and discovering individual models to improve medical level.

Bioinformatics has been proved to have great potential to identify diseases in advance, determine treatment methods and help people live a better life. With the inspiration and knowledge of computer science, the fields of gene technology, medicine and medical care can range from treating a single patient to curing the whole population.

Development Status of Bioinformatics

Nowadays, many biomedical research institutions are generating massive data, and they hope to understand these data through computational biologists. Because completing an experimental project of big data output will inevitably cost a lot of energy and capital investment, but if you can't analyze and understand the meaning behind these data, then this work can't be really completed.

Therefore, the future of biomedical research depends not only on experimental biologists who can design excellent experiments and produce high-quality data, but also on computational biologists who can effectively analyze and mine the generated data.