What is the prospect of bioinformatics in the country?

In fact, the barrier to becoming a credit student lies in computer proficiency. The difficulty for a person with a CS background to change careers to become a credit student is significantly lower than that of a pure life science major changing careers to become a credit student. Therefore, if you switch to CS and then return to biochemistry, the buffer period required is very short, but if biochemistry then switches to deep learning, the buffer period required is longer, but it varies from person to person, because there is already Python and a basic database. Compared with pure biology, The advantages are huge. If you want to do further scientific research in bioinformatics, the current prospects (short term) are still very positive, whether it is structural biology led by Shi Yigong (more important is the equipment, but there is also analysis), or the one led by BGI Genomics, as well as the currently very popular precision medicine, are closely related to bioinformatics. With the development of deep learning, intelligent diagnosis based on various databases is also related to bioinformatics. From another perspective, the future demand for biology majors should be multifaceted. In the future, biological research (or basic medical research) will require a lot of talents in computers, mathematics, etc. (such as quantitative biology). The efficiency of many experiments (basic cell culture, gel running) may be greatly improved in the future, and the manpower required for pure experiments may be saturated. According to the current speed of technological iteration in the United States, almost all technology industries require further training and learning every six months. Relatively speaking, For life sciences, people with a foundation in life sciences will face less pressure. (But only for most life science students) It must be admitted that finance and CS will still be the most attractive industries in the future, but if you can do well in biochemistry, then master medical statistics; statistics, python and omics knowledge Talented people do not need to worry about their prospects. However, during the PhD period, many people may lose patience due to boring wet experiments and a lot of repetitive work, so they still have to consider their own situation.