Data Analytics in Bioinformatics

A Machine Learning Perspective

Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang

• Publisher: Wiley-Scrivener
• Year: 2021
• Print ISBN: 9781119785538
• eBook ISBN: 9781119785606
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Part 1
THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS

1
Introduction to Supervised Learning

Rajat Verma, Vishal Nagar and Satyasundara Mahapatra*

PSIT, Kanpur, Uttar Pradesh, India

Abstract

Artificial Intelligence (AI) has enhanced its importance through machines in the field of present business scenario. AI delineates the intelligence illustrated by machines and performs in a contrasting manner to the natural intelligence signified by all living objects. Today, AI is popular due to its Machine Learning (ML) techniques. In the field of ML, the performance of a machine depend upon the learning performance of that machine. Hence, the improvement of the machine’s performance is always proportional to its learning behavior. These Learning behaviors are obtained from the knowledge of living object’s intelligence. An introductory aspect of AI through a detailed scenario of ML is presented in this chapter. In the journey of ML’s success, data is the only requirement. ML is known because of its execution through its diverse learning approaches. These approaches are known as supervised, unsupervised, and reinforcement. These are performed only on data, as its quintessential element. In Supervised, attempts are done to find the relationship between the independent variables and the dependent variables. The Independent variables are the input attributes whereas the dependent variables are the target attributes. Unsupervised works are contrary to the supervised approach. The former (i.e. unsupervised) deals with the formation of groups or clusters, whereas the latter (i.e. supervised) deals with the relationship between the input and the target attributes. The third aspect (i.e. reinforcement) works through feedback or reward. This Chapter focuses on the importance of ML and its learning techniques in day to day lives with the help of a case study (heart disease) dataset. The numerical interpretation of the learning techniques is explained with the help of graph representation and tabular data representation for easy understanding.

Keywords: Artificial intelligence, machine learning, supervised, unsupervised, reinforcement, knowledge, intelligence

1.1 Introduction

In today’s world, businesses are moving towards the implementation of automatic intelligence for decision making. This is only possible with the help of a well-known intelligence technique otherwise known as Artificial Intelligence (AI). This intelligence technique also plays a vital role in the field of research, which is nothing but taking decisions instantly. The dimension of AI is bifurcated into sub-domains such as Machine Learning (ML) and Artificial Neural Networks (ANN) [1]. The term ML is also termed as augmented analytics [2] and depicts the development of machine’s performances. This is achieved through the previous experiences obtained by the machines, but the traditional learning (i.e. the intelligence used in the mid-1800s) works not so efficiently if compared with the ML [3]. In traditional learning, the user deals with data and programs as an input attribute and provides the output or results whereas, in the case of ML the user provides the data and output or desired results as an input attribute and produces the program or rules as an output attribute. This means that data is more important rather than the programs. This is so because the business world depends on the accuracy level of the program which is used for decision making. The block diagram of Traditional learning is shown below in Figure 1.1 for easy understanding.

Traditional Learning is a manual process whereas the functioning of ML is an automated one. Due to ML, the accuracy of analytic worthiness is increased in different diversified domains. These domains are utilized for the preparation of data (raw facts and figures), Outlier Detection (Automatic), Natural Language Interfaces (NLI), and Recommendations, etc. [4]. Due to these domains, the bias factor for taking decisions on a business problem is decreased.

Figure 1.1 Traditional learning.

Figure 1.2 Machine learning.

ML is a sub-group of AI and its primary work is allowing systems to learn automatically with the help of data or observations obtained from the environment through different devices [5]. The block-diagram of ML is shown below in Figure 1.2.

ML-based algorithms perform predictions as well as decisions by using mathematical models that are based on some training data [6–8]. Few popular implementations of Machine Learning are Filtering of E-mails [9], Medical Diagnosis [10], Classification [11], Extraction [12], etc. ML works for the growth of the accuracy level of the computer programs. This was done by accessing data from the surrounding, learn the data automatically, and enhancing the capacity of decision making. The main objective of ML is to minimize human intervention and assistance while performing any task. The next section of this chapter highlights the process of learning along with its different methodologies.

1.2 Learning Process & its Methodologies

In AI, Learning means a process to train a machine in such a way so that the machine can take decisions instantly. Hence, the performance of that machine is upgraded because of its accuracy. When a machine performs in its working environment it may get either success or failure. From these successes or failures machines are gaining experience itself. These newly gained experience, improve the machines through their actions and forms an optimal policy for the working environment. This process is known as learning from experience. This process of learning is possible in an unknown working environment. A general block diagram learning architecture f...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• Acknowledgement
• Part 1: THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS
• Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION
• Part 3: MACHINE LEARNING AND HEALTHCARE APPLICATIONS
• Part 4: BIOINFORMATICS AND MARKET ANALYSIS
• Index
• End User License Agreement