Feature selection methods on gene expression microarray data for cancer classification: A systematic review

E Alhenawi, R Al-Sayyed, A Hudaib… - Computers in biology and …, 2022 - Elsevier
This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

Feature selection methods and genomic big data: a systematic review

K Tadist, S Najah, NS Nikolov, F Mrabti, A Zahi - Journal of Big Data, 2019 - Springer
In the era of accelerating growth of genomic data, feature-selection techniques are believed
to become a game changer that can help substantially reduce the complexity of the data …

Deep learning approach for microarray cancer data classification

HS Basavegowda, G Dagnew - CAAI Transactions on …, 2020 - Wiley Online Library
Analysis of microarray data is a highly challenging problem due to the inherent complexity in
the nature of the data associated with higher dimensionality, smaller sample size …

Automated detection of mechanical damage in flaxseeds using radiographic imaging and machine learning

M Nadimi, LG Divyanth, J Paliwal - Food and Bioprocess Technology, 2023 - Springer
The growing demand for flaxseed as a source of healthy edible oil mandates the need for
adopting novel strategies for preserving its quantity and quality. Mechanical damage during …

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: statistical approach vs machine learning approach

MS Tan, PL Cheah, AV Chin, LM Looi… - Computers in biology and …, 2021 - Elsevier
Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the
most common cause of dementia in the elderly. As the number of elderly individuals …

Optimized gene selection and classification of cancer from microarray gene expression data using deep learning

SH Shah, MJ Iqbal, I Ahmad, S Khan… - Neural Computing and …, 2020 - Springer
Cancer is the major leading reason of death around the world. However, the early
identification and prediction of a cancer type is very critical for patient's health. Recently …

A new hybrid seagull optimization algorithm for feature selection

H Jia, Z Xing, W Song - IEEE access, 2019 - ieeexplore.ieee.org
Hybrid algorithms have attracted more and more attention in the field of optimization
algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection …

Optimizing cancer diagnosis: A hybrid approach of genetic operators and Sinh Cosh Optimizer for tumor identification and feature gene selection

MM Emam, EH Houssein, NA Samee… - Computers in Biology …, 2024 - Elsevier
The identification of tumors through gene analysis in microarray data is a pivotal area of
research in artificial intelligence and bioinformatics. This task is challenging due to the large …

Exploration and enhancement of classifiers in the detection of lung cancer from histopathological images

K Shanmugam, H Rajaguru - Diagnostics, 2023 - mdpi.com
Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often
diagnosed late due to delayed symptoms. To catch it early, researchers are developing …

[HTML][HTML] Cross-evaluation of a parallel operating SVM–CNN classifier for reliable internal decision-making processes in composite inspection

S Meister, M Wermes, J Stueve, RM Groves - Journal of Manufacturing …, 2021 - Elsevier
In the aerospace industry, automated fibre laying processes are often applied for
economical composite part fabrication. Unfortunately, the current mandatory visual quality …