Prediction of Cancer from Gene Expression Data

by Ansuman Kumar

Cancer is one of the dangerous diseases caused by abnormal division of

cells and uncontrolled exponential growth of cells. Cancer cells usually behave

dierently from the normal cells and can spread to other parts of the

body. This spreading process of cancer cells to other parts of the body is called

metastasis [1]. Cancer arises from the conversion of normal cells into cancerous

cells in a multistage process that generally progresses from a pre-cancerous

cells to a malignant tumor.

Cancer is the second-leading cause of death worldwide and an approximately

9.6 million people die every year from cancer according to the Union for International

Cancer Control (UICC), Switzerland (https: //www.worldcancerday.org/

what-cancer). Early classication of cancer sub-type classes has a great importance

in serving better diagnosis to the patients. Therefore, cancer sub-types

(classes) prediction at initial stage has become a vital area of research in the

eld of machine learning and medical science worldwide to the researchers and

scientists. There exist dierent clinical approaches to diagnosis of cancer which

are described.

Apart from the clinical approaches of predicting cancer, computational biologists

suggest complementary and relatively inexpensive solution for cancer prediction,

and primary (early) diagnosis using modern technology like machine

learning [3] and soft computing [4] etc. to apply on microarray gene expression

data [5]. Machine learning [3] technology provides set of computer models that

automatically learn from data and experience. Whereas, soft computing [6] is

a collection of methodologies which exploit the tolerance for imprecision and

uncertainty to achieve tractability, robustness, and low solution cost. Microarray

technology [5] records thousands of genes simultaneously. Number of genes

present in microarray data is normally very large as compared to the number

of samples [7]. Also the clinically labeled samples are very few. Moreover the

cancer subtypes exist in microarray gene expression data are often vague, indiscernible,

ambiguous, and overlapping in nature [8]. Therefore, it is important

to construct robust classiers in this complex (vague, indiscernible, ambiguous)

scenario that would achieve high accuracy in classifying cancerous samples [9]

in presence of limited training samples. Detailed description about machine

learning, soft computing and microarray technology are provided.

• Format: Paperback
• Language: English
• ISBN: 320845726X
• ISBN13: 9783208457265
• Release Date: September 2022
• Publisher: Mohd Abdul Hafi
• Length: 172 Pages
• Weight: 0.52 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"