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Computational Intelligence-Based Gene Expression Analysis in Colorectal Cancer: A Review

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

Abstract

Key challenges in cancer gene expression analysis are to collectively identify the gene (tumor suppressor, proto-oncogenes, and mismatch repair) or sets of genes that are differentially expressed significantly in cancer and normal samples. High throughput expression techniques provide quantitative data about the expression of thousands of genes per biological sample. However, such technologies have some common problems including but not limited to dataset complexity, data integration, and noise. Data generated from the latest technologies are growing at an explosive rate; therefore, it becomes essential to dig out useful information from this data and create biological knowledge. Moreover, traditional data analysis is sometimes not effective to extract valuable information from biological datasets. More recently, to overcome these issues, computational intelligence methods such as artificial intelligence (AI) and machine learning (ML) have been widely applied to study gene co-expression networks, differential gene expression analysis, pathway analysis, and predicting biomarkers and therapeutic targets in cancer. In this chapter, we attempt to describe how computational intelligence-based algorithms can contribute to this field to generate quality knowledge needed in cancer biology. Special emphasis is given on colorectal cancer (CRC) studies, wherein apart from expression biomarkers, diagnostic potential of AI-based analysis has also been investigated. We have highlighted algorithms widely used in identifying unique gene expression signatures in CRCs. AI and ML-based methodologies could help us identify high-risk genes or gene sets and their aberrant expression associated with them. In the future, this would help us improve diagnosis and prognosis for better monitoring and management of CRC.

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Abbreviations

AB:

Ada boost

AI:

Artificial intelligence

ANN:

Artificial neural network

BRAF:

v-Raf murine sarcoma viral oncogene homolog B1

CRC:

Colorectal cancer

DT:

Decision tree

DL:

Deep learning

FNN:

Fuzzy neural network

LR:

Logistic regression

LDA:

Linear discriminant analysis

ML:

Machine learning

NB:

Naive Bayes

SOM:

Self-organizing map

SVM:

Support vector machine

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Authors and Affiliations

  1. Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Ankit Srivastava, Sandhya Rai, Manish Pratap Singh & Sameer Srivastava

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  1. Ankit Srivastava
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  2. Sandhya Rai
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  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Srivastava, A., Rai, S., Singh, M.P., Srivastava, S. (2022). Computational Intelligence-Based Gene Expression Analysis in Colorectal Cancer: A Review. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_22

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