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How Machine Learning Is Transforming Bioscience Research
How Machine Learning Is Transforming Bio science Research
The relationship between biology and machine learning is not new and has existed for decades, even before data science and machine learning became fashionable. Fields like protein structure prediction, homology modeling and cheminformatics frequently employ tools from machine learning. PCA or dimensionality reduction/SVMs/clustering/random forest classifier, etc. are all a fundamental part of bioinformatics literature.
So, what is new?
For a long time machine learning was
defined by the ability to choose effective features, which is often (a)
labor intensive and (b) requires a need to understand or have an idea
about solutions, which limited the application of machine learning.
It is also important to keep in mind that biological data derived from
experiments are prone to error, hence domain specific knowledge is
almost always required, and biological or-omics data tend to be high
dimensional and sparse.

Figure 1: Four stages of traditional machine learning workflow, (a) preprocessing data, (b) identifying features, (c) developing a model and (d) evaluating results.
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That being said, even today, a lot of these
traditional concepts are very much applied to build useful predictive
models from vast sets of experimental data. But what really changed was the introduction of deep learning,
along with (a) access to newer information and technology, (b) an
exponential decrease in computing costs, (c) an exponential decrease in
cost for genome sequencing, (d) advances in lab instrumentation and (e) a
generation of trained scientists who understand the complexities of
biology and biological systems and also have the ability to go deep into
computer science.
Where is deep learning making inroads?
Life science research is vast and it is almost
impossible to provide a comprehensive answer to this question. A lot of
interesting work, ranging from biomedicine to understanding gene
regulation have been published in the last few years. To me, one of the more interesting areas of application is the drug
discovery space, such as predicting molecule toxicity and reactivity,
which is often a huge burden on the drug discovery pipeline or even drug repurposing.
In my personal opinion, it is the best time
to be a computational biologist, as we have access to innumerable
amount of resources and information; and biology is filled with
unanswered questions.
How is this innumerable amount of information stored and accessible?
ReplyDeleteComputer data storage is a complex subject, but it can be broken down into three basic processes. First, data is converted to simple numbers that are easy for a computer to store. Second, the numbers are recorded by hardware inside the computer. Third, the numbers are organized, moved to temporary storage and manipulated by programs, or software and can be displayed on a screen when powered by electricity.
DeleteThis software or programmes then makes it possible for data to be store on the cloud via internet connection and be accessible through the use of computer BOTS or CRAWLERS.