Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

Abstract

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, by integrating the investigation and analysis of clinical and omics profiles. The high dimensionality and class imbalance of the majority of available data sets represent a serious challenge to the development of computational methods and tools for cancer diagnosis and biomarker discovery. Taking into account multi-omics data further complicates the undertaking. In this paper, we describe a five-step integrative architecture for dealing with the three aforementioned problems by incorporating proteomics data, proteinprotein interaction networks, and signaling pathways in order to identify protein biomarkers with a direct association to cancerous patients' overall survival (OS) and progression free interval (PFI). The core parts of this architecture are a cluster based grey wolf optimization algorithm (CB-GWO) for feature selection and a deep stacked canonical correlation autoencoder (DSCC-AE) for clinical endpoint prediction. A thorough experimental study was carried out to evaluate the performance of the proposed optimization algorithm for feature selection, as well as the performance of the deep learning model in terms of Mathew coefficient correlation (MCC) and Area under the curve (AUC) on breast, lung, colon, and rectum cancers. The results were compared to other methods in the literature. The results are very promising and show the effectiveness of the proposed framework and its ability to outperform the other algorithms and models in terms of AUC (0.91) and MCC (0.64).

Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review

“…Alzaqebah et al [58] implemented an initialization phase based on Information Gain (IG), a feature with a high IG value means it is significant for classifying the instance instead of random initialization. Similarly, Zenbout et al [59] modified initialization phase using the k-means clustering method. Features are grouped into clusters.…”

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

“…The approaches for cancer detection, prognosis, and therapy have been significantly enhanced by cutting-edge methods, including high-throughput sequencing, screening technologies, and artificial intelligence. This has led the medical domain to gradually shift towards precision medicine, achieved by combining the analysis of clinical and omics data [ 7 ]. The functioning and rules of the numerous relationships among omics technologies are part of biological processes [ 8 ].…”