Data science is a domain-agonistic field, which means that problems across several industries can be solved using it without being constrained to a certain business. The necessity for an appropriate group of individuals working with the data has become more pressing as more organizations shift toward becoming data-centric. The quantitative scientific discipline, whose luster has been stolen by biology, is one such area that is common in the present period. As a result, Biological Data Science has developed, allowing data professionals to use quantitative techniques to address issues in this sector.
The amount of data in the modern world is expanding quickly, and biology is one of the major drivers of this growth. Millions of data points on proteins, genes, tissues, etc. are frequently stored and integrated for systemic investigations in biological data science. However, established methods like computational biology find it difficult to identify useful patterns from this data due to its volume and complexity. Therefore, it is vital to employ cutting-edge methods in order to address such pressing issues on a worldwide basis.
To comprehend the underlying causes of many ailments and enhance health, a team of data scientists and professionals must make use of all these facts. The development of AlphaFold by the DeepMind team of scientists is one of the uses of data science in biology. It can anticipate the three-dimensional protein structures in the human body with high accuracy. This is a development in biological data science that will pave the way for a great deal more investigation in the future.
A biological data scientist needs the same fundamental abilities as data scientists in other fields. You can prepare for a career as a biological data scientist by doing the following:
1. Core Subject Knowledge: A general working knowledge of the fundamentals of biology, bioinformatics, and fundamental clinical science should be possessed by biomedical data scientists.
2. Programming: Proficient in at least one programming language (usually Python and/or R), biomedical data scientists should be .
3. Machine Learning/Statistics: Predictive analytics, modeling, and machine learning: While a variety of statistical techniques may be helpful, these three areas of expertise have become particularly crucial in the field of biomedical data science.
1-AlphaFold’s Protein 3d structure
Protein's three-dimensional structures are predicted by AlphaFold using a deep neural network. The AI successfully predicted the structures of more than 200 million proteins, which is nearly all of the proteins currently known to exist. This is a significant breakthrough in the field of "protein-folding problems," as they are known. Due to AlphaFold's capabilities, various medications and vaccinations to benefit humanity have been created by medical experts.
2-DNA Sequencing with Artificial Intelligence
A genome is an organism's whole collection of DNA. All living things have genomes, yet their sizes vary widely. For instance, the human genome is divided into 23 chromosomes, which is similar to organizing an encyclopedia into 23 volumes. The total number of characters (individual DNA "base pairs") in each human genome would exceed 6 billion. So it’s a huge compilation.
About 6 billion characters or letters make up the human genome. If you imagine the genome (the entire DNA sequence) as a book, it contains around 6 billion "A," "C," "G," and "T" letters. Now, each person has a distinct genome. However, the majority of human genomes share similar features, according to scientists.
Genomic science is a data-driven field that heavily relies on machine learning to identify patterns in data and derive new biological theories. However, more potent machine learning models are needed to be able to draw new conclusions from the volume of genomics data that is growing exponentially. Deep learning has effectively rebuilt industries like computer vision and natural language processing by utilizing massive data sets. It has evolved into the method of choice for a variety of genomics modeling applications, including determining how genetic variation affects gene regulation processes like DNA receptivity and splicing.
3-Drug Discovery
Future drug developers may be able to locate new therapeutic targets with the help of AI, which will also help them create more precise molecules.
Exscientia (Nasdaq: EXAI), based in Oxford, UK, is a leader in using AI in the development of small-molecule drugs. Through generative AI design, the business has expanded its AI-based platform to create new therapeutic antibodies. The business announced the entry into clinical trials of the first AI-designed medication candidate in early 2020.
The "clinical-stage biotechnology company Recursion, based in Salt Lake City," claims to have one of the largest biological and chemical datasets in the world, with a focus on disorders associated with gene mutations.
4-BioMaker Identification
Finding a quantifiable biological sign that can aid in the diagnosis, prognosis, or monitoring of a disease or treatment in medicine is the process of identifying a biomarker. By examining biological and clinical data from patients and finding patterns and connections that may be too complicated for humans to notice, artificial intelligence (AI) can assist in the identification of biomarkers. On the basis of this data, neural networks (a component of AI) can be taught to identify particular biomarkers linked to a given illness or condition, which can subsequently be utilized to create more precise diagnostic tests or individualized treatments.
We are happy to help you 24/7