The application of data science in biotech and life sciences is very versatile, so it’s vital to understand the perspectives better to get familiar with some cases of its successful implementation. Let’s move to the particular examples of AI application in biotech and life sciences.
Like any other medical treatment, the cure of ovarian cancer assumes an exclusively individual approach. Covariance models and classifiers, such as random forests, decently suit the task of choosing the best treatment for ovarian cancer patients.
Unlike popular views, chemotherapy is not the only treatment for cancer patients, nor is it the most effective way to treat this illness in individual cases. The models help perform genome sequencing and locate misplaced nucleotide bases. Further treatment decisions are made based on the available suppressors for these modifications.
Heart attacks are more likely to lead to hospitalization, and the probability of its repetition rises with age. Heart illnesses remain in the focus of the telehealth field. Nowadays, it has delivered numerous devices to track the current state of the patients daily.
Right now, the function of these devices is far from perfect. The systems rely on the notification features, which notify a user once a particular indicator reaches its limit. Unfortunately, this leads to many erroneous notifications. As long as heart attacks need urgent medical assistance, patients may have extra expenses on healthcare services. Besides, they may lose their faith in these devices.
For troubleshooting purposes, a classification algorithm based on Naïve Bayes has been introduced. By analyzing the most common features (e.g., gender, age, smoking habits, heart rate, etc.) the algorithm has managed to improve the performance of the heart monitor devices by more than 70%!
The value of machine learning for gene sequences research is quite obvious, and it is widely used in microbiome therapeutics development. The fun fact is that the human body contains the genes of all the viruses, bacteria, and other protozoa populating it. Many diseases are linked to the presence of these organisms in our bodies. That is why the research in the field is flourishing.
Exactly to link diseases to the genes of the protozoa causing them supervised machine learning and reinforcement learning algorithms are applied.
The next stage involves the actual process of drugs discovery and the further supply chain development for the newly produced drug. This stage is often accompanied by random forest algorithms application.
Finally, principal component analysis and supervised learning are implemented to predict the outcomes of the interaction between the developed drug and the target microbiome. The tricky part here is related to the absence of sufficient training datasets for the models.
3D printing is a sensation on its own, but a few people could predict its implementation in biotech years ago. One of the most ethically challenging issues, organ donation, is now being solved by 3D printing. While the field remains complex and young, data science plays a significant role in its development.
Bayesian optimization is applied to ensure the quality of the tissues produced with the biomaterials. This procedure supports the final decision – if the tissues produced are usable or not.
Another application of data science in 3D printing is the usage of Siamese network models. They are used to make printing faster. Then, convolutional neural networks are implemented to spot all the defects in tissues printed.
As you can see, each implementation of the machine learning methods is very goal-specific. These 8 examples are only an introduction to the world of data science applications in biotech and life sciences. You can assume it from a basic understanding of how complex our organisms are.
To support biotech and life science researches, new ML models and neural networks are trained. The advanced developers, who are working at the crossline of biotechnology and AI, are the right experts to ask the assistance if your project is in this domain.