One of the fastest growing uses of computational intelligence is by the burgeoning field of genomics. To put it simply, genomics is the term we have created in order to process information about our genes. Genes, which are contained in each single cell in our body, determine how we look and how we grow. And, unlike computers software, our genes are passed on to our children. Unfortunately, unlike software, it’s not as simple as upgrading our genome.
Organism development, unfortunately, occurs in a non-deterministic manner and with varied results. Thus, to better understand our own biological makeup, scientists have built huge computing clusters which are sifting through the 3D structure of our genes in order to build libraries of genetic information which allow us to better understand related diseases and their risks.
In order to process this staggering amount of data, scientists are forced to use machines.
What are Applications of Machine Learning for the life Sciences?
Machine Learning (ML) is the name for the field that describes the application of algorithms to find patterns in large structured datasets. These algorithms are then used to either model or predict patterns in other data. These patterns can be anything from the most effective prescribing of medications to the best choice of a new drug.
One of the major problems facing life sciences is the sheer number of cells, tumors and genes which must be sifted through. Additionally, how these clusters of cells interact with one another is vast and complex and must be studied in order to understand the future directions of drug treatments and possible genetic disorders.
One of the ways to solve this problem is through the use of ML. ML algorithms use deep learning with the goal of taking structured data and finding the trends and features in that data. These algorithms, when applied to structured data, are capable of finding complex patterns in order to build predictive models and to predict new and unseen data.
Cancer is caused by a number of different factors, but understanding its root cause and understanding the interactions between these different factors is a daunting task. Researchers are using ML to better understand the growth and interactions of tumor cells. One of the most exciting ML ventures in this field is in the area of oncology. Using computer algorithms to allow us to better understand how cancerous cells interact, we can build better drugs and more understand how they interact with each other.
If we can successfully model these interactions, we can better understand how to affect the growth of the tumor, the weaknesses of the cancer and, hopefully, lead to more effective treatments.
Alzheimer’s and Parkinson’s Disease
Machine Learning can also be applied to understanding the progression of diseases of the brain. For example, one can use ML modules to analyze EEGs (electroencephalograms) and determine the type of neural patterns which can affect the brain by identifying the sections of the brain in which there are reduced neural patterns.
Similarly, there are several classes of drugs and medication which can be used as treatment. Typically, doctors prescribe a set of medications in order to increase blood flow through the brain, increase the amount of chemicals or cells which make communication more efficient. These medications, which target brain cells, can have a wide array of side effects. Therefore, doctors will usually prescribe a combination of drugs in order to keep the patient symptom free, while not causing other negative effects.
Through the use of computers, doctors could better understand the neural structures in the brain, as well as the interactions between the neurons. This sort of information can be used to better understand the effect of the drugs and to decide which combination of medications are best for a particular patient.
I could frankly go on for days about the applications of ML to the life sciences. However, the key takeaway for any scientist or manager who wishes to learn more about the field of AI and ML is that it is a field that is ever evolving, and much like genomics, it is a field which is becoming more than just a niche field containing a handful of data scientists.
The world is becoming ever more connected through wires and through the daily sharing of information. And, as the Internet of Things becomes more commonplace and devices become connected to each other, we are forced to develop novel methods to visualize, understand and process this increasing amount of information.
Machine learning is without a doubt, the solution to this problem.