DeepGx: deep learning using gene expression for cancer classification
This paper aims to explore the problems associated in solving the classification of cancer in
gene expression data using deep learning model. Our proposed solution for the cancer …
gene expression data using deep learning model. Our proposed solution for the cancer …
Deep learning based tumor type classification using gene expression data
The differential analysis is the most significant part of RNA-Seq analysis. Conventional
methods of the differential analysis usually match the tumor samples to the normal samples …
methods of the differential analysis usually match the tumor samples to the normal samples …
Deep learning for multi-tissue cancer classification of gene expressions (GeneXNet)
T Khorshed, MN Moustafa, A Rafea - IEEE Access, 2020 - ieeexplore.ieee.org
Cancer classification using gene expressions is extremely challenging given the complexity
and high dimensionality of the data. Current classification methods typically rely on samples …
and high dimensionality of the data. Current classification methods typically rely on samples …
Lightweight convolutional neural network for breast cancer classification using RNA-seq gene expression data
Gene expressions are considered among the most used features in cancer classification.
The available gene expression data has a small number of samples and a relatively big …
The available gene expression data has a small number of samples and a relatively big …
DeePathology: deep multi-task learning for inferring molecular pathology from cancer transcriptome
Despite great advances, molecular cancer pathology is often limited to the use of a small
number of biomarkers rather than the whole transcriptome, partly due to computational …
number of biomarkers rather than the whole transcriptome, partly due to computational …
Deep learning-based identification of cancer or normal tissue using gene expression data
Background: Deep learning has proven to show outstanding performance in resolving
recognition and classification problems. As increasing amounts of cancer and normal gene …
recognition and classification problems. As increasing amounts of cancer and normal gene …
A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
Data-driven model with predictive ability are important to be used in medical and healthcare.
However, the most challenging task in predictive modeling is to construct a prediction model …
However, the most challenging task in predictive modeling is to construct a prediction model …
A stacking ensemble deep learning approach to cancer type classification based on TCGA data
Cancer tumor classification based on morphological characteristics alone has been shown
to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most …
to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most …
Identification of 12 cancer types through genome deep learning
Y Sun, S Zhu, K Ma, W Liu, Y Yue, G Hu, H Lu… - Scientific reports, 2019 - nature.com
Cancer is a major cause of death worldwide, and an early diagnosis is required for a
favorable prognosis. Histological examination is the gold standard for cancer identification; …
favorable prognosis. Histological examination is the gold standard for cancer identification; …
Deep learning for cancer type classification and driver gene identification
Background Genetic information is becoming more readily available and is increasingly
being used to predict patient cancer types as well as their subtypes. Most classification …
being used to predict patient cancer types as well as their subtypes. Most classification …