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Machine learning methods for cancer classification using gene expression data: A review
F Alharbi, A Vakanski - Bioengineering, 2023 - mdpi.com
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 …
that can spread in different parts of the body. According to the World Health Organization …
Advances and development of prostate cancer, treatment, and strategies: A systemic review
The most common type of cancer in the present-day world affecting modern-day men after
lung cancer is prostate cancer. Prostate cancer remains on the list of top three cancer types …
lung cancer is prostate cancer. Prostate cancer remains on the list of top three cancer types …
A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data
Breast cancer is considered one of the significant health challenges and ranks among the
most prevalent and dangerous cancer types affecting women globally. Early breast cancer …
most prevalent and dangerous cancer types affecting women globally. Early breast cancer …
Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification
Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and
classification of PRC become essential to enhance the quality of healthcare services. A …
classification of PRC become essential to enhance the quality of healthcare services. A …
Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic …
Cancer remains a leading reason of mortality, with the current global death toll estimated at
10 million and projected to surpass 16 million by 2040 as reported by the World Health …
10 million and projected to surpass 16 million by 2040 as reported by the World Health …
Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy
This investigation aimed to assess the effectiveness of different classification models in
diagnosing prostate cancer using a screening dataset obtained from the National Cancer …
diagnosing prostate cancer using a screening dataset obtained from the National Cancer …
OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features
W Ye, X Chen, P Li, Y Tao, Z Wang, C Gao… - Frontiers in …, 2023 - frontiersin.org
Background Early stroke prognosis assessments are critical for decision-making regarding
therapeutic intervention. We introduced the concepts of data combination, method …
therapeutic intervention. We introduced the concepts of data combination, method …
Multi-omics based artificial intelligence for cancer research.
With significant advancements of next generation sequencing technologies, large amounts
of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and …
of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and …
Hybrid arithmetic optimization algorithm with deep transfer learning based microarray gene expression classification model
BS Gowri, SAH Nair, KPS Kumar - International Journal of Information …, 2024 - Springer
Microarray gene expression (MGE) data classification is a vital challenge in biomedical and
genomics research, designed to understand the difficult relations among genes and …
genomics research, designed to understand the difficult relations among genes and …
Machine-learning algorithm-based risk prediction and screening-detected prostate cancer in a benign prostate hyperplasia cohort
CC Chang, JK Chiou, CJ Lin, K Lu, JR Li… - Anticancer …, 2024 - ar.iiarjournals.org
Background/Aim: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study
was to use machine learning-based methodology to predict PCa risk in patients with benign …
was to use machine learning-based methodology to predict PCa risk in patients with benign …