Understanding Bioinformatics as the application of Machine Learning

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

 

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.

 

During the blog discussion, we will understand about machine learning and Bioinformatics as one of its applications, we also bring your attention to how Machine learning tools such as ANN, PCA, RNN can be useful in Bioinformatics and other appraohes for the solution of biological data where 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.

 

 

Introduction

 

Being an application of AI, machine learning provides the system to learn and improve automatically from experience, without being explicitly programmed. It focuses on the augmentation of computer programs that can access data,use it and learn themselves.

 

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 incorporates procedure, theory and application in the individual field.

 

Machine learning has many characteristics, one is used to decrease false-positive rates, and it provides the ability to computer systems for increasing their performance based on past data.

 

Machine learning has different applications and can be implemented based on business problems. Bioinformatics is also one of another application of Machine Learning. And, in various reserach studies, it has been observed that Machine Learning tools play a vital role in the field of Bioinformatics.

 

(Must check: Machine Learning Tutorial)

 

 

About Bioinformatics

 

Let’s have a small glimpse of 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. 

 

Now, let's understand the role of Machine Learning in resolving issues in Bioinformatics.

 

 

ML to resolve issues in Bioinformatics

 

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. Let's understand the problem briefly;

 

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. 

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Animated Structure of DNA

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

 

(Most related: Introduction to Deep Learning and Neural Networks)

 

 

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

 

 

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.

 

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. 

 

Conclusion

 

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.