Table 2.

Challenges posed for ML and DL in biomedicine, existing strategies to overcome these challenges and proposed solutions by integrating ML techniques with established bioinformatics approaches.

Problem	Bottleneck	Example Solutions	Potential Integrated ML/DL and Bioinformatics Solutions
Small and dependent datasets	Data availability	Restricting the number of parameters [27,190]	Neural network architectures for small and sparse datasets
		Separating training and test sets by phylogenetic similarity [27]	Methods to evaluate data dependency by protein and sequence similarities
Biological sequence representation	Methodological	NLP with neural networks-based modeling [191,192,193,194]	Incorporating amino acid substitution and codon usage matrices to representation frameworks
			Incorporating conserved domain databases to the training framework
Incorporation of different data types	Methodological	Integration of multi-omics datasets through existing network topologies
Reproducibility	Acceptance	Documentation and deposition of the processed data [195]	-
		Benchmarking of the processing pipeline and optimized parameters [196]	-
Interpretability	Acceptance	Incorporation of established bioinformatic methods and databases with ML and DL frameworks [128,196]
		Generation of interpretable DL models [197,198,199]