Table 6.

Deep learning algorithms in genomics and their original development and applications

ANN Algorithms	Natural Language Processing (NLP)	Feedforward neural network	Convolutional neural network (CNN)	Recurrent neural networks (RNNs)	Bidirectional long short-term memory networks (BLSTMs)	Long short-term memory networks (LSTMs)	Gated recurrent unit (GRU)
Algorithm Inventor	Applied in dictionary look-up system developed at Birkbeck College, London	Frank Rosenblatt	It was named as “neocognitron “ by Fukushima	Rumelhart, Hinton and Williams	Schuster and Paliwal	Hochreiter and Schmidhuber	Cho et al
Year of Development	1948	1958	1980	1986	1997	1997	2014
Year of Initial Genomics’ Function	1996	1993	2015	2005	2015	2015	2017
First User in Genomics	Schuler et al	S Eskiizmililer	Alipanahi et al	Maraziotis, Dragomir and Bezerianos	Quang and Xie	Quang and Xie	Angermueller et al
First Genomic Application	Entrez databases	Karyotyping architecture based on Artificial Neural Networks	DeepBind	Predicting the complicated causative associations between genes from microarray datasets based on recurrent neuro-fuzzy technique	DanQ model	DanQ model	DeepCpG
Genomic Function Exemplar(s)	Genetic counsellors AI-based chatbots and EPIs prediction	Karyotyping, Prenatal diagnostic for early detection of aneuploidy syndrome	Prediction of variant impacts on expression and disease risk, predicting drug response of tumours from genomic profiles, and pharmacogenomics	Predicting transcription factor binding sites, for Alignment and SNV identification	DNA function predictions and prediction of protein localisation, predict miRNA precursor	Enhancer–promoter interaction (EPI) prediction	Enhancers and methylation states predictions
Landmark References	[128, 169, 170]	[171–173]	[97, 111, 174–176]	[24, 116, 118, 177, 178]	[122, 123, 179, 180]	[16, 121, 123]	[126, 181]