Understanding Bioinformatics as the application of Machine Learning

  • Neelam Tyagi
  • Oct 01, 2019
  • Machine Learning
Understanding Bioinformatics as the application of Machine Learning title banner

Machine learning is an adaptive process that improves models or computers from their experience, it enables computers to increase their efficiency. Because of its specific characteristics, it is widely used in real-life applications.


A breakthrough in machine learning would be worth ten Microsofts. -Bill Gates


This blog contains a real-life scenario in Bioinformatics. Starting from introduction to machine learning and Bioinformatics as one of its applications, we bring your attention to how Machine learning tools such as ANN, PCA, RNN can be useful in Bioinformatics and many more to the solution of biological data.


Most of the time this data is unstructured which needs to be handled and organized very precisely. Also, we see some applications of neural networks in Bioinformatics.




Machine learning is purely associated with computational statistics, it not only focuses on different prediction-making using statistics but also ties to mathematical optimization which further delivers procedure, theory and application domain in the individual field.


Machine learning has many characteristics, one is used to decrease false-positive rates, and it has the ability of computing machine in order to increase the performance based on past data.


As we have seen in previous blogs that Machine learning has different applications and can be implemented based on business problems. Bioinformatics is also one of another application of Machine Learning. Also, it can be seen in many research that Machine Learning tools play a vital role in the field of Bioinformatics.



How Can Machine Learning help in Bioinformatics?


Let’s have a small glimpse of Bioinformatics, here we discover what is Bioinformatics? How this is useful? And the role of Machine Learning in Bioinformatics.


  1. It is the interdisciplinary field of molecular biology and genetics, computer science, mathematics, and statistics. It uses computation to get relevant information from biological data through different methods to explore, analyze, manage and store data.
  2. It is mainly used for the identification of genes and nucleotides for a better understanding of disease based on genes.
  3. In other words, you can understand Bioinformatics as a hybrid science that connects biological data with analytical advanced technique to withdraw meaningful information for various scientific research including biomedicine also.
  4. It is fed with high-output generating data including determination of genomic sequence and examining gene patterns.
  5. Classification of gene sequence has an important role to understand the principle within nucleic acid and protein sequence.
  6. In Bioinformatics, data is collected, stored, manipulated, in addition, this includes modeling of data for analysis, data visualization and foresight by the deployment of algorithms and software. 


Sometimes Machine Learning is combined with mining of data, which covers deep data analysis of unsupervised learning and supervised learning. Here, supervised learning is used to determine and discovered a biological database that helps in finding laws in gene sequences.


We know that various computational techniques are used for adaption and fault tolerance or error limits which made them engaging for investigation in Bioinformatics.


Similarly in Machine Learning, a computational technique used to classify networks, to explore and learn then adapt to changing circumstances and therefore improving the performance of the machine, i.e. this technique trains the network for better performance and enhancing the accuracy of the system-network.



Let’s Have a Look at Various Issues in Bioinformatics


The Study of DNA and Protein sequences includes signs regarding functioning and subproblems such as classification of homologs, varied sequences alignment, searching sequence patterns, and evolutionary analyses.


All of these problems covered under sequence analysis, and hence machine learning algorithms are preferred for the same.  (Referring you here to visit the blog: What are Model Parameters and Evaluation Metrics used in Machine Learning?)


The structures of protein represent three-dimensional data, problems associated with it are;


  1. Structure prediction (having a secondary and tertiary protein structure)

  2. Analysis of structures of protein for marks of a functioning

  3. Alignment of structures. 

The image is showing the basic animated structure of DNA

Animated Structure of DNA

Gene expression data usually is expressed in matrices form and its analysis comprises statistical analysis, classification, and clustering strategies.


Many biological networks such as Gene Regulatory Network, protein-protein interaction networks etc, are displayed on graphs and the various associated problems such as building and interpretation of massive-range networks are solved using graph-theoretic methods.


Moreover, classification becomes a difficult task in handling biological data, this is not possible by traditional methods of analysis, so Artificial Neural Network is widely used as a Machine Learning tool in Bioinformatics.


Neural networks are a component of soft computing, they provide learning capability to network-system. The architecture of the neural network consists of one input layer, one or more numbers of hidden layers and one output layer.


An issue with Biological sequence


In Bioinformatics, neural networks produce the properties of prediction and analysis or classification of genes in several classes. In terms of Biological sequence, this is one of the main issues correlated with sequencing difficulties such as RNA, protein-sequence, DNA, etc.


An issue with Genome sequence:

In Genome Sequencing, genome refers to a complete set of chromosomes that determines an organism, improvements in sequencing strategies give opportunities in bioinformatics for organizing, processing and interpreting the sequences. Each sequencing faces challenges in experimenting with the design, interpretation, and analysis of data.


  1. In gene findings and genome annotation, Introns and exons are nucleotide sequences within a gene, gene findings suggest for predicting introns and exons in DNA-sequence segments whereas genome annotation analyzes the repetitive DNA which is copied from the same or nearly the same sequence within the genome.

    “Bioinformaticians are not anti-social; We are just genome friendly.”

  2. In Sequence comparison, it provides a base for many Bioinformatics tools and allows the conclusions of the function, design, and progression of genes and genomes.   



Steps in solving Sequence Analysis


While modeling biological processes at the molecular level and making conclusions from the stored data, the following steps are considered for Bioinformatics solutions;


  1. Collect statistics from biological data

  2. Build a computational model

  3. Solve a computational modeling problem

  4. Test and evaluate computational algorithms



What are the Applications of Neural Networks in Bioinformatics?


With the exponential growth of biological data, one needs to pay attention to the efficient storage and management of information, also to extract relevant information from this data. (Have a glance at top big data technologies that concerns above fact)


Further, appropriate computational methods must be applied for transforming this heterogeneous data into useful information.These computational tools and methods or you would say machine learning tools allow grasping more described data and provide knowledge in the form of testable models by which we are able to obtain predictions of the system.


There are several biological domains where machine learning tools can be utilized for extracting the information from data, following are applications of neural network in bioinformatics;


  1. In the recognition of coding region of genes

  2. In the identification of genes problems

  3. Identification and analysis of signals generated from regulatory sites

  4. Sequence, classification, and features detection

  5. Expression of genetic and genomic data

  6. Image and signal processing


Nowadays, Bioinformatics shows wide applications in the field of medicine, like, to obtain the association between gene sequence and diseases, to divine or picturise protein structure from amino acid sequence, to assist in designing novel-drug, to monitor medical care of patients based on their DNA sequences. 




As we enter the era of artificial intelligence and big data, machine learning is taking central place for business applications. Machine learning is also producing promising results with great advances in Bioinformatics. In this blog, an extensive review of Bioinformatics and the role of machine learning are described. We saw the issue of sequence analysis in Bioinformatics and valuable insight Bioinformatics as a starting point. For more blogs in Analytics and new technologies do read Analytics Steps, follow Analytics Steps, and connect with us at  Facebook, Twitter, and LinkedIn.



  • vivek.vikash

    Oct 03, 2019

    literally enjoyed reading this blog , the information given is very firm and authentic .Kudos to Analyticssteps.

    Neelam Tyagi

    Oct 03, 2019

    Hey.. Thank you Vivek