Have you ever heard of Deep Learning? Deep Learning is a branch of machine learning that consists of training neural networks to be able to perform complex tasks by processing large amounts of data.
Over the years, Deep Learning has experienced an exponential increase in popularity, leaving its mark in different fields, including biological and biomedical research. In this blog, we will tell you how the use of this technology collaborates hand in hand with researchers in the advancement of biology.
What is Deep Learning?
Before talking about Deep Learning in the biology sector, we will explain how this technology is one of the foundations of Artificial Intelligence.
Deep Learning, also known as deep learning algorithms, provides machines with the ability to learn in order to obtain the desired information, based on training with already known data.
Unlike machine learning, which uses simpler criteria, deep learning uses neural networks. That is, based on data observed in the past, it is capable of predicting results in the future, thus imitating human behavior.
Deep Learning functionalities
But how can Deep Learning be used? Currently, there are a variety of applications of this technology, used daily by the entire population.
The following are some of the most popular applications of Deep Learning:
Natural language processing
What is Deep Learning used for in biology?
In biology, deep learning algorithms dive into data in a way that humans cannot, detecting features that would otherwise be impossible to capture. Researchers make use of such algorithms to make genomic connections, advance drug discovery, and even find links between different types of data, from genomics and imaging to electronic medical records.
Examples of Deep Learning applications in biology
Having commented on the most recognized applications of Deep Learning, we will now explain examples of applications of this technology in the field of biology.
Firstly, images such as X-rays, CT scans and MRIs are traditionally interpreted by a medical specialist. If we add to their diagnosis a Deep Learning algorithm that helps us to identify patterns and characteristics that are difficult for humans to see, we can detect symbols that help in the diagnosis of diseases.
An example of Deep Learning applied to medical imaging in dermatology is the SkinVision smartphone application, which analyzes our skin using images. Its main objective is to be able to find early signs of possible skin cancer. It also warns us when we should pay special attention to a mole, analyzes our skin type and gives us advice on how to keep it healthy.
The genome is the genetic information contained in the DNA of an organism. This technique is especially useful in its study, since the genome contains a large amount of information that is often difficult to interpret using traditional methods. Thus, with the implementation of Deep Learning algorithms, we can analyze large genomic datasets. With the aim of: predicting gene expression patterns, identifying disease-causing mutations and improving our understanding of the genetic basis of diseases.
The inclusion of Deep Learning in the study of the genome is so fruitful that there is even news about the discovery of an unknown human ancestor that, using traditional methods, could not have been found.
This technique has the potential to significantly accelerate the drug discovery process. In addition to helping researchers identify new treatments for a wide range of diseases.
Models can be trained on a variety of data types, including genetic data, proteomic data and chemical data. One of their main advantages is the ability to locate subtle patterns in complex data, which helps researchers identify drug candidates. For example, a model can surface associations of previously unknown molecules and predict their drug properties. One of the companies using Deep Learning for drug development is Valo Health.
Another application of Deep Learning in synthetic biology is the design of new biological circuits. A biological circuit is an application in which parts within a cell are designed to perform logical functions, mimicking electronic circuits. By training learning models with large sets of genetic and biological data, it is possible to predict how different components will interact and create new circuits that perform specific functions.
Finally, another application in the medical field is the analysis of patient data. Including their medical history, genetic information and lifestyle factors, to develop personalized treatment plans. With its use it is possible to know: which drugs are the most indicated for the patient, which diseases are more prone to develop and what steps should be followed to have a healthy life.
To conclude, Deep Learning is transforming the landscape of biological research and its impact is being felt in multiple areas. With the ability to analyze large amounts of data and identify patterns that were previously hidden, the use of this technology is helping to improve our understanding of biology and advance the development of more effective treatments. That is why, as this technology continues to evolve, we can expect to see even more exciting applications in the coming years.
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