High technology: There is a way to train machines to predict the function of DNA sequences. Neural nets which is a type of artificial intelligence (AI) typically used to classify images.
Teaching the neural net to predict the function of short stretches of DNA. It allows for decoding larger patterns. The researchers hope to analyze more complex DNA sequences that regulate gene activity critical to development and disease.
Frequently picture showing
In fact, researchers can train a brain-like “neural net” computer to recognize objects. Testing the success of training requires showing the machine a new picture.
However, researchers meet a problem when they apply this technology to analyzing DNA patterns. Humans can’t recognize the patterns, so they may not be able to tell if the computer identifies the right thing.
In fact, neural nets learn and make decisions independently of their human programmers. Researchers refer to this hidden process as a “black box.” It is hard to trust the machine’s outputs if we don’t know what is happening in the box.
In genomics, it’s not so straightforward because genomic sequences aren’t in a form where humans really understand any of the patterns that these neural networks point to.
The new neural network pattern
In fact, there will be a new method to teach important DNA patterns. By allowing his neural network to build on the data to identify more complex patterns.
The discovery makes it possible to peek inside the black box and identify some key features that lead to the computer’s decision-making process.
There is another larger purpose in mind for the field of artificial intelligence.
Ways to improve a neural net
Interpretability refers to the ability of humans to decipher why machines give a certain prediction. Robustness is the ability to produce an answer even with mistakes in the data.
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