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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 …
[HTML][HTML] NK cell infiltration is associated with improved overall survival in solid cancers: A systematic review and meta-analysis
S Nersesian, SL Schwartz, SR Grantham… - Translational …, 2021 - Elsevier
The immune landscape of a tumor is highly connected to patient prognosis and response to
treatment, but little is known about how natural killer (NK) cells predict overall survival (OS) …
treatment, but little is known about how natural killer (NK) cells predict overall survival (OS) …
A survey of machine learning approaches applied to gene expression analysis for cancer prediction
Machine learning approaches are powerful techniques commonly employed for develo**
cancer prediction models using associated gene expression and mutation data. This …
cancer prediction models using associated gene expression and mutation data. This …
An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification
Abstract Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the
molecular level that aids in distinguishing between its two prominent subtypes—Lung …
molecular level that aids in distinguishing between its two prominent subtypes—Lung …
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 …
[HTML][HTML] Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics …
Simple Summary Recent high-throughput technologies such as transcriptomics, proteomics,
and metabolomics have allowed progress in understanding biological systems at different …
and metabolomics have allowed progress in understanding biological systems at different …
Enhancing lung cancer classification and prediction with deep learning and multi-omics data
Lung adenocarcinoma (LUAD), a prevalent histological type of lung cancer and a subtype of
non-small cell lung cancer (NSCLC) accounts for 45–55% of all lung cancer cases. Various …
non-small cell lung cancer (NSCLC) accounts for 45–55% of all lung cancer cases. Various …
[HTML][HTML] Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer
Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the
unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast …
unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast …
Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis
H MotieGhader, P Tabrizi-Nezhadi… - Scientific Reports, 2022 - nature.com
Lung cancer is the most common cancer in men and women. This cancer is divided into two
main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) …
main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) …
From data to cure: A comprehensive exploration of multi-omics data analysis for targeted therapies
A Mukherjee, S Abraham, A Singh, S Balaji… - Molecular …, 2024 - Springer
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards
understanding underlying disease mechanisms, placing a strong emphasis on molecular …
understanding underlying disease mechanisms, placing a strong emphasis on molecular …