Table 1.

Summary of deep learning methods, their relevant applications and brief technical descriptions of each DL model

Application	DL method	Reference	Description
Microscopy-based assessment of cancer	CNN	Ruy et al. [40] Nir et al. [41] Ström et al. [42] Ehteshami Bejnordi et al. [43] Vuong et al. [44] El Achi and Khoury [45]	Trained CNNs on pathology images to predict grading of prostate [40–42], breast [43], colon cancer [44] and lymphoma [45]
	CNN & explainability	Hägele et al. [46]	LRP used to assigned feature contribution for cancer grade for each pixel of WSIs
	Semantic segmentation	Poojitha and Lal Sharma [47]	A semantic segmentation technique called GAN was used to segment tissue maps for prostate cancer grade prediction
Molecular subtyping	MLP	DeepCC [48]	Gene set enrichment analysis used to transform gene expression input into functional spectra
	CNN	imCMS [49], Sirinukuwattana et al. [50], Stalhammar et al. [51], Couture et al. [52] Woerl et al. [53]	Models trained on histopathology images to classify molecular subtypes of of lung [49], colorectal [50], breast [51, 52] and bladder cancer [53]
	GCNN	Rhee et al. [18]	Utilised a hybrid GCNN model to organise input gene expression profiles into STRING PPI network [16] and predict breast cancer molecular subtypes
	Multimodal learning	Islam et al. [54]	Two CNN models used to predict breast cancer molecular subtypes from CNAs and gene expression; Outputs of the last fully connected layer of each model concatenated for a final subtype prediction
Cancer of unknown primary	MLP	Jiao et al. [55]	Model trained to predict origins of 24 cancer types using somatic mutation patterns and driver genes
	CNN	SCOPE [56], CUP-AI-Dx [57]	Both studies trained models to predict different cancer types from gene expression
	RNN & explainability	TOAD [58]	RNN-based model called Attention was trained on WSIs to predict metastasis and cancer origin; Attention algorithm reveal image regions contributing most to predictions were mostly cancer cells
Prognosis prediction	MLP	Cox-nnet [59], DeepSurv [60], RankedDeepSurv [61]	Cox regression used as the last layer of MLP models for prognosis prediction
	MLP & AEs	AECOX [62]	AE used to “compress” gene expression into low-dimensional embedding vector and used as an input for Cox-regression
	Explainability	PASNET [63], Cox-PASNET [64]	A pathway layer used between the input and the hidden layers with each node representing a known pathway; Analysis of weight differences in pathway layers reveal clinically actionable genetic traits
		MesoNet [65]	Histopathology images split into tiles and scored by survival prediction contributions; Scores used to identify top-contributing regions, reviewed by pathologists
	GCNN & explainability	Chereda et al. [19]	Combine GCNN and explainability method LRP to identify biologically and therapeutically relevant genes in predicting metastasis of breast cancer
	Explainability with multimodal learning	PAGE-Net [66]	CNN used to compress features from WSIs; Cox-PASNet used to incorporate gene pathway and provide cross-modal analysis with image features extracted by CNN
		PathME [67]	AEs used to compress features from four omics modalities, which are combined to predict survival; SHAP used to assign each omics feature survival prediction contribution score
Precision Oncology	MLP	HER2RNA [68]	Transcriptomic profiles inferred from histopathology images divided into tiles; Predictions added up for all tiles and compared with ‘ground truth’ transcriptomic profiles
	CNN	Image2TMB [69]	Ensemble of three CNNs to extract features from histopathological images at different resolutions (x5, x10 and x20); Extracted features are combined to infer TMB
		Kather et al. [70]	TCGA histopathology images used to predict mutational status of key genes, molecular subtypes and gene expression of standard biomarkers
Tumour microenvironment	MLP	Scaden [71]	Ensemble of three models with different filter sizes to predict TME composition from gene expression; Predictions from the models are averaged into a final prediction
	Explainability with MLP	MethylNet [72]	MLP and AE used to ‘compress’ CpG beta values into an embedding vector for predicting TME composition; SHAP used to assign feature contribution to each CpG site
	Semantic segmentation	Saltz et al. [20]	Semantic segmentation model used on H&E images to localise spatial heterogeneity patterns of TIL and necrosis
Spatial transcriptomics	CNN	ST-Net [73]	Images split into tiles centred on spatial transcriptomics spots; Tiles used to train a CNN to predict expression of 250 target genes
Pharmacogenomics	CNN	CDRscan [74]	Two models used to extract features from somatic mutational fingerprints and molecular profiles of drugs (cell lines); Feature vectors combined to predict efficacy of drugs based on genomic profiles
	MLP	DeepSynergy [75]	Cell line gene expression and chemical features of drugs in drug combinations used as input; Predicts ‘synergy score’ between the drug combinations and transcriptomic profiles
	GCNN	Jiang et al. [76]	Utilised graph structure to integrate protein-protein, drug-drug and drug-protein interactions to predict synergistic drug combination for specific cell lines
	Multimodal learning	DeepDR [77]	Collection of ten AEs to integrate ten drug-disease networks, which predict drug-disease associations
	CNN	DeepDTI [78]	Protein sequence and drug fingerprint as input to predict drug protein-binding sites

AE: autoencoder, CNA: copy number alterations, CNN: convolutional neural network, DL: deep learning, GCNN: graph convolutional neural network, H&E: haematoxylin and eosin, LRP: layer-wise relevance propagation, MLP: multilayer perceptron, RNN: recurrent neural netowrk, SHAP: SHapley Additive exPlanations, TIL: tumour-infiltrating lymphocytes, TMB: tumour mutational burden, WSI: whole slide image