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What is coding?
It is defined as the set of rules assigned to perform a function either automatically or manually. These rules are performed through a particular software and they are the key features of this process. This field of science is also known as computational biology as it requires coding skills to specialize in it.
What is Bioinformatics?
It is the home of information about the fundamental molecules of the living world like RNA, DNA and proteins. It involves the analyzing, processing and interpreting of these basic data structures. This field is very vast and there are numerous divisions available within it.
Computational biology:
It essentially requires both the computational skills as well as biological data to complete the tasks. The most intricate computational skills include coding skills, technological idea of the current trending software's and knowledge about recent trends in biotechnology. This department involves gathering information on recent updates in software, technology and science. Without this, a scientist will be in great trouble.
The basics in computer science are not necessarily enough for these tasks. Instead, one should learn the applications in computers to master these skills. These computer applications are none other than the programming languages. The reason behind learning these skills is not only to be updated in today's world of science and technology but also to enable the scientists to perform these tasks within a few seconds. While, the manual performance of these tasks may take long hours to complete.
Coding and Bioinformatics:
These are two different subjects but, when combined together form an astonishing development in technology. Basically, it requires many devices like Laptops, computers, calculators etc., to access and interpret the information. The algorithms are the fundamentals of coding and without this, coding is not possible.
The coding involves the usage of coding platforms like python, R, C, C++ and java. These software enable the users to operate user friendly. The programming languages like python, C etc., can be learned within months to work on it. Very often we use the term bioinformatics in our research in biotechnology. As this is very easily done and comprises a vast area of information to interpret. Some of the very frequently performed research areas in bioinformatics include analyzing the protein structures, interpTereting the genomes etc., The coding is generally used to analyze a large set of biological data.
The analysis of protein and gene expression, protein interactions, genome sequencing and evolutionary relationships are done with the help of programming languages. The very easiest programming languages to learn are python and R. As they are designed in such a way that users would love to use them. These programming languages speed up the processing of the biological data to arrive at a point of conclusion for the aimed research.
AI / ML in biology:
Artificial Intelligence / Machine Learning is a recently developed tool to access biological data at an unimagined speed and automation. These tools perform tasks which are not manually done by human beings. They do or do not require coding skills to perform these tasks. Scientists use AI tools in major research areas of personalized medicine and drug discovery.
It is the recent trend in today’s modern world and many students are eagerly learning it to create an impact in the field of science and technology. These tools are also used for generating 3D models of proteins and their interactions. The major advantage of AI/ML is that it is not labour-intensive and it is completely automated using technology. It has a prominent effect in the field of science by providing a vast space for the scientists to work. It is performed with ease and care and there is no need to worry about the time lapse required for the task to be completed as it is ultimately fast.
Computational tools in AI/M for biology:
The first and most promising tool is for visualizing and analyzing the biological data with the help of AI/ML. These tools include Seaborn, nglview and matplotlib. The second intriguing tool is for the designing of drugs and its discovery . These AI/ML tools include RDKit, Reinvent4 and DeepChem. The third groundbreaking tool is for predicting the disease and its diagnosis. These tools include PathAI and CheXNet. The fourth beneficial tool is for personalizing the medicine. These tools are GENCODE and OpenCRAVAT.
The fifth fascinating tool is for stating the new purpose of the existing drug. These tools are DrugRepurposingHub and CLUE. The sixth advantageous tool is for discovering the causal agents of disease and its development. One such tool is XGBoot. The seventh strenuous tool is for going through the three dimensional structure of proteins and its effect in designing of drugs and its discovery. Such tools are AF2Complex, AlphaFold2, DMPFold2 and RFDiffusion.
