Bioinformatics is an interdisciplinary field that deals with the science of collecting and analyzing complex biological data. It can be considered as a combination of biology, computer science, mathematics, and statistics. Artificial intelligence (AI) is a new facet of computer science which deals with the creation of intelligent machines. Well, Machine Learning is one of the most important aspects of artificial intelligence. It refers to the ability of the machine to learn without any human supervision.
Artificial Intelligence has found increasing applications in the field of bioinformatics. Machine Learning is a very powerful tool in bioinformatics which is highly useful in the prediction and pattern detection based on large datasets.
Common Applications of AI in Bioinformatics
Bioinformatics involves the collection, organization, and analysis of a huge amount of biological data. Handling of such a huge amount of data is beyond human beings and AI can help us to a great extent in this voluminous work. The following are some of the fields in Bioinformatics where AI usage is of great value.
DNA sequencing is the process of determining the order of the four nucleotides – Adenine, Thymine, Guanine, and Cytosine in the DNA. Each organism has its own specific nucleotide base sequence. Although the double helix nature of the DNA was discovered long back in 1953, it was not possible to carry out complete DNA sequencing considering the huge data crunching involved. This was made possible by the advancement of Artificial Intelligence and the associated data handling ability. Using these advancements, the mapping of the human genome was completed in 2003. The DNA sequencing has also brought forth a new discipline called pharmacogenomics which is leading the way to more personalized medicines.
Proteins are the molecules responsible for all the biological processes in a cell. Proteins are made of polypeptides. Polypeptides are nothing but chains of amino acids. These polypeptide chains fold into the final three-dimensional structure to form a functional protein. Proteins are grouped into different families according to their biological function. Since most proteins have a similar primary structure and a common evolutionary origin; it’s very difficult to classify proteins. Even unrelated families of proteins can have a similar structure. However, this can be achieved by making use of Artificial Intelligence and its immense computational power. There are several approaches to doing this. One method is to make a computer program that compares the unidentified amino acid sequence to the known sequences of the proteins and returns the classification of the target protein.
Accurate analysis and classification of proteins are of fundamental importance as they are responsible for most of the key functions in an organism. The information can also reveal the protein’s catalytic role and biological function.
Analysis of Gene Expressions
Gene expression is the process by which the genetic code is converted into functional products like proteins. Gene expression involves two main stages namely transcription and translation. It’s a tightly regulated process that allows a cell to respond to its changing environment.
Gene expression microarrays commonly called gene chips have made it possible to measure and analyze the gene expression. This would not have been possible without the help of AI. In the field of cancer research, the advent of microarrays and RNA sequencing coupled with artificial intelligence have proved its potential in the detection and classification of tumors at a molecular level. In other words, it helps in better diagnosis of the emperor of all maladies.
It is the process of identifying the locations of genes and all of the coding regions in a genome. It also determines the functions of these genes.
Genome annotation necessarily involves some level of automation. It is not possible to manually analyze each of the several thousand protein sequences encoded in a genome. Such tasks are facilitated by advanced machine learning software. The major advantage of machine learning software when it comes to genome annotation is their ability to automatically identify patterns in a huge amount of data.
Computer Aided Drug Design (CADD)
Computer Aided Drug Design is a specialized discipline that makes use of computational methods to simulate drug-receptor interactions. CADD is very much dependent on information technology, databases, and computational resources. Only AI can manage these tasks efficiently. The CADD can be used in various stages of the drug discovery like the hit identification using virtual screening or the lead optimization stages.
Knowledge Discovery and Data Mining in Biological Databases
Artificial Intelligence can help to bring new insights into a continuously growing and voluminous biological data. A huge body of biological data is now available and its effective utilization requires the extraction of useful data.
Knowledge Discovery from Databases (KKD) is an emerging field that combines techniques from database management, artificial intelligence, and statistics. The KDD methodology complements the laboratory experiments and can accelerate new discoveries in biological sciences.
Metabolic Pathway Determination
The Metabolic pathway refers to a linked series of a chemical reaction occurring within a cell. These are further classified into anabolic pathways and catabolic pathways. There is AI software available that can effectively track these complex processes.
Machine Learning: The Future of Bioinformatics
It’s said that the data is the new oil in the 21st century. This is true in the field of biological sciences also. Without artificial intelligence and machine learning, this huge data is of no use. Definitely, the future of bioinformatics is closely associated with and dependent on the developments in the field of machine learning.
AI is changing every aspect of the world as we know it. Like any other powerful technology, AI has its potential for danger. However, we have to be hopeful that its benefits will far outweigh its negative effects. As far as Bioinformatics is concerned, it looks like artificial intelligence is definitely a great blessing. As we have seen, It has endless possibilities in the field ranging from the diagnosis of dangerous diseases and the discovery of new and more efficient drugs to new knowledge discovery from the ever-expanding biological database.