OFweek Medical Technology News: The amount of data in big data is naturally very large, no doubt, but the amount is not big, we are not talking about big data. What is more important in big data is its multi-dimensionality and completeness. With these two points, it is possible to link events that seem to be irrelevant, and to restore a comprehensive and complete description of things.
The benefits of data go far beyond cost and accuracy, and its advantages are multi-dimensional (or omnidirectional). In the past, data that computers were able to store and process was often limited, so only data related to the problem to be solved was collected. There were only a few dimensions, and seemingly unrelated dimensions were omitted. This limitation also determines how specific data is used, often with prior assumptions or conclusions, and then validated with data. Now, the advent of cloud computing allows us to store and process large numbers of data that are complex or even seemingly useless. The method of working has changed as a result. In addition to using data to validate existing conclusions, you can start with the data itself without any intrinsic thoughts and see what new conclusions the data itself can give. As a result, many new rules may be discovered. For example, the data in Baidu Encyclopedia is disorganized at first glance, but in fact there is a lot of internal connection between the data. Before analyzing these big data, there is no pre-hypothesis in the minds of product managers, and I don't know what conclusions I can draw. However, by analyzing these data, many new laws were discovered. I think that when Baidu insiders see these results in the first time, I am afraid they will be surprised.
We know that many diseases are related to genetic defects, but the principle of gene action is very complicated. A defect in a gene may cause a certain disease, but it is only possible.
Another difficulty in studying the relationship between human genes and disease is how to find genes that may be defective. Be aware that the data of a person's complete gene is very large. According to Academician Yang Huanming, the founder of Huada Gene, this amount of data is much larger than the average person imagined, in the order of PB (1015 bytes, or one million GB). If you only look at the size of the data, this person's data may have exceeded the amount of data Baidu knows. Of course, if you look at a person's genes, you can't know whether one of the genes is good or defective. Even if you find a few people, even dozens of people have enough genes, because the genes between each individual are Certain differences do not indicate that different genes are defects. To locate these possible defects, at least tens of thousands of people need genetic data. Before the advent of cloud computing, it was difficult for people to process such a large amount of data.
Collecting genetic data from a large number of people has also been a big problem in the past. Fortunately, there are always many solutions in the world that seem to be difficult to solve. There is a small company called 23andMe in the United States. It does things very interesting and smart. The company only needs $100 (not $2,000-5,000 for a full DNA test in the hospital) to collect your saliva, so you can roughly "read" your genes and then "roughly" tell The probability of getting various diseases in the future. Of course, the company's interpretation of the gene is not the same as the Huada gene's mapping of the entire gene. But even with a simpler genetic analysis, $100 is not enough. 23andMe actually attracts a large number of gene providers by this method. There are a large number of genes, and they can distinguish which gene fragments are normal and which have "possible" defects. For each person who provides the gene, they can list some of the possible defective genes in this person. Of course they also get the probability of each genetic defect.
Another thing that 23andMe and similar companies (including Google's health research department) are doing is linking genetic defects to diseases, and this data must go to research institutions and hospitals. In the past, the data of each hospital in this area was very limited, but if the data of thousands of large and small hospitals were collected, the probability of coexistence of disease and genetic defects could be estimated, and then there would be The probability that a genetic defect causes a disease is calculated. In the future, big data can accurately tell each of us the future health status through genetic testing, and effectively prevent diseases. (See this report: Genentech collaborates with 23andMe for genome-wide sequencing)
The reason I cite the example of the medical industry, because in addition to the IT industry, health care is the most enthusiastic Big Data industry. Of course, another reason is that Google and I are more enthusiastic about the industry and easier to give examples, but this does not mean that the application of big data is only concentrated in these two industries.
The healthcare industry is the largest industry in the United States. In 2013, its output accounted for about 15% of US GDP. If the cost cannot be reduced, this ratio will increase by about 20%. In such a large industry, although doctors used to deal with data (various test results and indicators) every day, unfortunately, in the past 50 or 60 years, doctors have used IT technology to improve medical standards. Power is not enough (except for medical imaging and other technologies). However, in the past decade, this situation has changed, the medical industry is actively contacting the IT industry, and hopes to solve the health care problems through big data. This shows the importance of big data from another side. So far, big data has brought many unexpected surprises to the medical industry. In 2012, the US media reported that the application of two big data in medical treatment is very telling.
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