ReviewMachine learning and deep learning methods that use omics data for metastasis prediction
Under a Creative Commons license
open access
Keywords
Cancer
Metastasis
Machine learning
Deep learning
Artificial intelligence
Abbreviations
Acc
Accuracy
AE
autoencoder
ANN
Artificial Neural Network
AUC
area under the curve
BC
Betweenness centrality
BH
Benjamini-Hochberg
BioGRID
Biological General Repository for Interaction Datasets
CCP
compound covariate predictor
CEA
Carcinoembryonic antigen
CNN
convolution neural networks
CV
cross-validation
DBN
deep belief network
DDBN
discriminative deep belief network
DEGs
differentially expressed genes
DIP
Database of Interacting Proteins
DNN
Deep neural network
DT
Decision Tree
EMT
epithelial-mesenchymal transition
GA
Genetic Algorithm
GANs
generative adversarial networks
GEO
Gene Expression Omnibus
HCC
hepatocellular carcinoma
HPRD
Human Protein Reference Database
FC
fully connected
k-CV
k-fold cross validation
KNN
K-nearest neighbor
LIMMA
linear models for microarray data
LOOCV
Leave-one-out cross-validation
LR
Logistic Regression
L-SVM
linear SVM
MCCV
Monte Carlo cross-validation
MLP
multilayer perceptron
mRMR
minimum redundancy maximum relevance
NPV
negative predictive value
PCA
Principal component analysis
PPI
protein-protein interaction
PPV
positive predictive value
RC
ridge classifier
RF
Random Forest
RFE
recursive feature elimination
RMA
robust multi‐array average
RNN
recurrent neural networks
Se
sensitivity
SGD
stochastic gradient descent
SMOTE
synthetic minority over-sampling technique
Sp
specificity
SVM
Support Vector Machine
TCGA
The Cancer Genome Atlas
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© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.