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This type of technology has shown to be very useful in life science industries, such as by sorting different types of cancer cells in laboratories. Naturally, technology, which both serves a function, and removes the need for explicit programming, will affect a host of jobs in the life science industry.
There are several types of and , which are subcategories of . The basic principle dictates that is machine intelligence leading to the best outcome when given a problem. This sets up well for life science applications – can be taught to differentiate cells, be used for higher quality imaging techniques, and analysis of genomic data.
Deep learning is a favorite among the facets in biology. The structure of has its roots in the structure of the human brain, in that there are neural networks, which connect to one another through which the data is passed. At each layer, some data is extracted. For example, in cells, one layer may analyze cell membrane, the next some organelle, and so on until the cell can be identified.
In classic , data often required some conversion or manipulation into a more meaningful form, such as features or connections before it could be exposed to the model. With , this is not required. Genomic data, which entails a huge number of bases to be analyzed, can be fed directly to the model, where the computer has to find the meaningful features or relationships. While the then removes one job of analyzing genomic data, it also creates a new one – researchers are not in charge of the classification and cannot explain why the model predicts the way it does. This uncertainty in how the model works, opens the opportunity for researchers to find out why the system, for example, picked one gene over another.
Image analysis is one field of life science that is being affected by . While automatic imaging software has existed without , the addition of has allowed software to use several features, which may not initially be obvious.[…]