Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection
Abstract
:1. Introduction
2. Results
2.1. Drug–Target Prediction
2.1.1. Metastases Dataset
2.1.2. Parkinson’s Disease Dataset
2.2. Drug Synergism
2.2.1. Regression Analysis of Synergy Scores
2.2.2. Prediction of Drug Synergism
2.2.3. Comparison with NMTF
3. Discussion
4. Materials and Methods
4.1. Data
4.1.1. Semantic Multipartite Graph
- The set of nodes N can be split into n subsets , such that if and ; furthermore, each subset is linked to a semantic category (e.g., genes or pathways);
- It is possible to assign a semantic meaning to connections between elements of two sets (e.g., the gene represented by being a target of the drug represented by ).
4.1.2. Data Sources
4.2. Prediction of Unknown Links
4.3. Methods
4.3.1. NMTF for Semantic Bipartite Graphs
4.3.2. NMTF for Semantic Multiparite Graphs
- If X is connected to Y, which is connected to Z, and if <> and <> are the factorization of their association matrices and , we need to impose that ;
- If X is connected to both Y and Z, and if <> and <> are the factorization of their association matrices and , we need to impose that ;
- If both Y and Z are connected to X, and if <> and <> are the factorization of their association matrices of their association matrices and , we need to impose that .
4.3.3. Semantic Embeddings and N-Tower Architecture
4.3.4. Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADRA1D | Alpha-1D adrenergic receptor |
AEs | Autoencoders |
AI | Artificial intelligence |
ATC | Anatomical Therapeutic Chemical |
AUROC | Area Under the Receiver Operating Characteristic curve |
DL | Deep learning |
DRD2 | Dopamine Receptor D2 |
FDA | Food and Drug Administration |
GABRB1 | Gamma-aminobutyric acid type A receptor subunit beta1 |
GO | Gene Ontology |
HTR2A | 5-hydroxytryptamine receptor 2 |
IL-12 | Interleukin 12 |
LOO | Leave one out |
LR | Logistic regression |
ML | Machine learning |
MPA | Medroxyprogesterone acetate |
NMTF | Non-Negative Matrix Tri-Factorization |
NN | Neural networks |
NR3C1 | Nuclear Receptor Subfamily 3 Group C Member 1 |
PD | Parkinson’s disease |
PTSG1 | Prostaglandin-Endoperoxide Synthase 1 |
RF | Random forest |
SL | Synthetic Lethality |
SVM | Support vector machine |
SVD | Singular Value Decomposition |
TNF-alpha | Tumor Necrosis Factor alpha |
XGBoost | eXtreme Gradient Boosting |
ZIP | Zero Interaction Potency |
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Dataset | Task | Two-Tower Architecture | NMTF | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Embedding Size = 10 | Embedding Size = 25 | ||||||||||
LR | RF | XG | LR | RF | XG | S | G | D | G+D | ||
Cancer | Full | 0.682 | 0.904 | 0.923 | 0.734 | 0.937 | 0.941 | 0.632 | 0.596 | 0.796 | 0.666 |
LOO Genes | 0.683 | 0.786 | 0.767 | 0.660 | 0.828 | 0.816 | - | 0.735 | - | 0.700 | |
LOO Drugs | 0.664 | 0.881 | 0.881 | 0.707 | 0.899 | 0.904 | - | - | 0.755 | 0.763 | |
PD | Full | 0.703 | 0.923 | 0.926 | 0.744 | 0.912 | 0.928 | 0.657 | 0.656 | 0.773 | 0.701 |
LOO Genes | 0.675 | 0.835 | 0.828 | 0.675 | 0.858 | 0.818 | - | 0.784 | - | 0.751 | |
LOO Drugs | 0.684 | 0.865 | 0.872 | 0.715 | 0.868 | 0.881 | - | - | 0.716 | 0.742 |
Random Forest | XGBoost | ||
---|---|---|---|
Drug | Pred. Target | Drug | Pred. Target |
Mirtazapine | DRD2 | Amitriptyline | DRD2 |
Dosulepin | DRD2 | Olsalazine | PTGS1 |
Doxepin | DRD2 | Norgestrel | NR3C1 |
Trazodone | DRD2 | Oxyphenbutazone | PTGS1 |
Citalopram | DRD2 | Trazodone | DRD2 |
MPA | NR3C1 | Glutamic acid | ABCC8 |
Flupentixol | HRH1 | Zonisamide | CACNA1S |
Vortioxetine | DRD2 | Pizotifen | DRD2 |
Butriptyline | DRD2 | Chlorpromazine | CHRM4 |
Norgestrel | NR3C1 | Dimetindene | CHRM1 |
Random Forest | XGBoost | ||
---|---|---|---|
Drug | Pred. Target | Drug | Pred. Target |
Prochlorperazine | HTR2A | Fostamatinib | TAB1 |
Promethazine | HTR2A | Guanabenz | ADRA1D |
Olanzapine | ADRA2B | Topiramate | GABRB1 |
Fluoxetine | HTR2A | Amitriptyline | ADRA2B |
Trifluoperazine | HTR2A | Zopiclone | GABRB1 |
Methoxamine | ADRA2B | Butabarbital | GRIN2A |
Midodrine | ADRA2B | Methysergide | ADRA2B |
Olanzapine | ADRA1D | Rotigotine | HTR2A |
Milnacipran | HTR2A | Zopiclone | GABRA4 |
Rotigotine | HTR2A | Zopiclone | GABRG2 |
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Messa, L.; Testa, C.; Carelli, S.; Rey, F.; Jacchetti, E.; Cereda, C.; Raimondi, M.T.; Ceri, S.; Pinoli, P. Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection. Int. J. Mol. Sci. 2024, 25, 9576. https://doi.org/10.3390/ijms25179576
Messa L, Testa C, Carelli S, Rey F, Jacchetti E, Cereda C, Raimondi MT, Ceri S, Pinoli P. Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection. International Journal of Molecular Sciences. 2024; 25(17):9576. https://doi.org/10.3390/ijms25179576
Chicago/Turabian StyleMessa, Letizia, Carolina Testa, Stephana Carelli, Federica Rey, Emanuela Jacchetti, Cristina Cereda, Manuela Teresa Raimondi, Stefano Ceri, and Pietro Pinoli. 2024. "Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection" International Journal of Molecular Sciences 25, no. 17: 9576. https://doi.org/10.3390/ijms25179576