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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 in machine learning processing of big data from disease diagnosis sensors
S Lu, J Yang, Y Gu, D He, H Wu, W Sun, D Xu, C Li… - ACS …, 2024 - ACS Publications
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical
task in the fields of chemistry, biology, and medicine. The complexity of biological systems …
task in the fields of chemistry, biology, and medicine. The complexity of biological systems …
En-MinWhale: An ensemble approach based on MRMR and Whale optimization for Cancer diagnosis
According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for~
10 million fatalities at the end of 2020. The most common types of cancers include Lung …
10 million fatalities at the end of 2020. The most common types of cancers include 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 …
Hybrid black widow optimization with iterated greedy algorithm for gene selection problems
Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited
expressiveness, and low informativeness in gene expression datasets obtained by DNA …
expressiveness, and low informativeness in gene expression datasets obtained by DNA …
Classification techniques for arrhythmia patterns using convolutional neural networks and internet of things (IoT) devices
The rise of Telemedicine has revolutionized how patients are being treated, leading to
several advantages such as enhanced health analysis tools, accessible remote healthcare …
several advantages such as enhanced health analysis tools, accessible remote healthcare …
[HTML][HTML] Optimizing microarray cancer gene selection using swarm intelligence: recent developments and an exploratory study
Microarray data represents a valuable tool for the identification of biomarkers associated
with diseases and other biological conditions. Genes, in particular, are a type of biomarker …
with diseases and other biological conditions. Genes, in particular, are a type of biomarker …
RNN-CNN based cancer prediction model for gene expression
One of those illnesses that is most deadly to people is cancer. The only way to prevent any
harm to humanity is by its early discovery and treatment. Various types of tests are …
harm to humanity is by its early discovery and treatment. Various types of tests are …
Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case
Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data
produced by devices such as mass spectrometry requires platforms to identify and quantify …
produced by devices such as mass spectrometry requires platforms to identify and quantify …