Incorporating Machine Learning into Established Bioinformatics Frameworks
Abstract
:1. Introduction
1.1. Integrating Machine-Learning into Molecular Evolution Research
1.2. Integrating Machine-Learning with Protein Structure Analysis
1.3. Integrating ML into Systems Biology
1.4. Integrating ML with Genomics and Biomarker Analysis for Disease Research
1.5. Key Challenges and Future Directions
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] |
2. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Bioinformatics Area | Problem Category | Goal | ML Method | Bioinformatic Tools |
---|---|---|---|---|
Molecular evolution | Biological sequence clustering | Protein family prediction | CNN | Clusters of Orthologous Groups (COGs) and G protein-coupled receptor (GPCR) dataset [30] |
Protein function prediction | deep RNN | BLAST and HMMER search [32] | ||
Anti-CRISPR proteins identification | Random forest | MSA and PSI-BLAST [24] | ||
EXtreme Gradient Boosting | K-mer based clustering (CD-HIT), BLAST [25] | |||
Viral pathogenicity feature identification | SVM | MSA, phylogenetic tree construction [20,21] | ||
Alignment free biological sequence analysis | Identification of viral genomes | RNN | BLAST, Sequence clustering, HHPRED [27] | |
CNN | BLAST [28] | |||
protein structure analysis | Post translational modifications | Phosphorylation sites prediction | KNN | Local sequence similarity [53] |
CNN | K-mer based clustering (CD-HIT), BLAST [55] | |||
Glycosylation sites prediction | ensemble SVM | curated glycosylated protein database (O-GLYCBASE) [54] | ||
Protein structure prediction | Protein contact prediction | CNN | MSA [72] | |
Prediction of distances between pairs of residues | CNN | MSA, HHPRED, PSI-BLAST [77] | ||
systems biology | inference of biological networks | Gene regulatory network prediction | SVM | GeneNetWeaver, RegulonDB [81] |
Protein-protein interaction network prediction | SVM | Domain affinity and frequency tables [90] | ||
Elastic-net regression | Protein descriptors [91] | |||
Analysis of biological networks | Drug target prediction | K-means | Network analysis tools [98] | |
Drug side effect prediction | SVM | Genome scale metabolic modeling [112] | ||
Drug Synergism prediction | Random Forest Ensemble | A chemical-genetic interaction matrix [117] | ||
Multi-omics integration | Cancer subtype prediction | Neighborhood based clustering | Similarity based integration [141] | |
Drug response prediction | logistic regression | Cancer hallmarks datasets, pathway data [144] | ||
biomarker analysis for disease research | Disease-associated genes investigation | Pulmonary sarcoidosis genes identification | Hierarchical clustering | Differential expression analysis [150] |
Identification of miRNA-disease association | NMF | Disease semantic information and miRNA functional information [151] | ||
Disease-phenotype visualization | t-SNE | OMIM database and human disease networks [154] | ||
Biomarker discovery | Cancer diagnosis | SVM | Reference gene selection [170] | |
Biomarker signature identification | SVM | Network-based gene selection [167] | ||
Cancer outcome prediction | Random forest | Evolutionary conservation estimation [181] |
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Auslander, N.; Gussow, A.B.; Koonin, E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int. J. Mol. Sci. 2021, 22, 2903. https://doi.org/10.3390/ijms22062903
Auslander N, Gussow AB, Koonin EV. Incorporating Machine Learning into Established Bioinformatics Frameworks. International Journal of Molecular Sciences. 2021; 22(6):2903. https://doi.org/10.3390/ijms22062903
Chicago/Turabian StyleAuslander, Noam, Ayal B. Gussow, and Eugene V. Koonin. 2021. "Incorporating Machine Learning into Established Bioinformatics Frameworks" International Journal of Molecular Sciences 22, no. 6: 2903. https://doi.org/10.3390/ijms22062903