Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination

Abir, Fuad Al; Shovan, S.M.; Hasan, Md. Al Mehedi; Sayeed, Abu; Shin, Jungpil

doi:10.1039/d1mo00467k

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Wrapper methods can be categorized as deterministic or randomized search algorithms. Classification models that have been used with wrapper methods include k-Nearest Neighbors [72], Random Forests [73,76], Support Vector Machines [75,77], and others. Since this feature engineering approach requires training and evaluating a multitude of classifiers by considering different subsets of candidate features, wrapper methods are substantially more time-consuming and computationally demanding than filter methods.…”

Section: Wrapper Methodsmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

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Section: Wrapper Methodsmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Hybrid Filter Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

Oyegbile,

Saeed,

Bamansoor

2024

Lecture Notes on Data Engineering and Communications Technologies

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From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

Mukherjee,

Abraham,

Singh

et al. 2024

Mol Biotechnol

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In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

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Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination

Abstract: RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq...

Cited by 4 publications

References 40 publications

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Hybrid Filter Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

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