Optimisation Models for Pathway Activity Inference in Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. A Novel Optimisation-Based Pathway Activity Inference Model
2.3. Comparison of Pathway Activity Inference Approaches
2.4. Method Evaluation
2.4.1. Classification
2.4.2. Robustness against Noise in Data
2.4.3. Survival Analysis
2.5. Sample Classification through DIOPTRA
2.5.1. Pathway and Gene Ranking
2.5.2. Assessing Sample Classification through DIOPTRA
3. Results
3.1. Classification Comparison
3.2. Robustness Comparison
3.3. Survival Comparison
3.4. DIOPTRA Prediction Performance and Identification of Biologically Relevant Pathways
4. Discussion
4.1. Computational Efficiency Improvements in DIOPTRA
4.2. Exploration of Top-Ranked Pathways
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Oh, E.S.; Petronis, A. Origins of human disease: The chrono-epigenetic perspective. Nat. Rev. Genet. 2021, 22, 533–546. [Google Scholar] [CrossRef]
- Mancarella, D.; Plass, C. Epigenetic signatures in cancer: Proper controls, current challenges and the potential for clinical translation. Genome Med. 2021, 13, 23. [Google Scholar] [CrossRef]
- Franks, P.W.; Melén, E.; Friedman, M.; Sundström, J.; Kockum, I.; Klareskog, L.; Almqvist, C.; Bergen, S.E.; Czene, K.; Hägg, S.; et al. Technological readiness and implementation of genomic-driven precision medicine for complex diseases. J. Intern. Med. 2021, 290, 602–620. [Google Scholar] [CrossRef] [PubMed]
- Van den Berge, K.; Roux de Bézieux, H.; Street, K.; Saelens, W.; Cannoodt, R.; Saeys, Y.; Dudoit, S.; Clement, L. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 2020, 11, 1201. [Google Scholar] [CrossRef] [Green Version]
- Budinska, E.; Popovici, V.; Tejpar, S.; D’Ario, G.; Lapique, N.; Sikora, K.O.; Di Narzo, A.F.; Yan, P.; Hodgson, J.G.; Weinrich, S.; et al. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J. Pathol. 2013, 231, 63–76. [Google Scholar] [CrossRef]
- Zhou, X.G.; Huang, X.L.; Liang, S.S.; Tang, S.M.; Wu, S.K.; Huang, T.T.; Mo, Z.N.; Wang, Q.Y. Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis. Oncotargets Ther. 2018, 11, 2815–2830. [Google Scholar] [CrossRef] [Green Version]
- Domany, E. Using High-Throughput Transcriptomic Data for Prognosis: A Critical Overview and Perspectives. Cancer Res. 2014, 74, 4612–4621. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schena, M.; Shalon, D.; Davis, R.W.; Brown, P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270, 467–470. [Google Scholar] [CrossRef] [Green Version]
- Palukuri, M.V.; Marcotte, E.M. Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks. PLoS ONE 2021, 16, e0262056. [Google Scholar] [CrossRef] [PubMed]
- Garofano, L.; Migliozzi, S.; Oh, Y.T.; D’Angelo, F.; Najac, R.D.; Ko, A.; Frangaj, B.; Caruso, F.P.; Yu, K.; Yuan, J.; et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat. Cancer 2021, 2, 141–156. [Google Scholar] [CrossRef]
- Giudice, L. Simpati: Patient classifier identifies signature pathways based on similarity networks for the disease prediction. Bioinformatics 2021. [Google Scholar] [CrossRef]
- Karagiannaki, I.; Pantazis, Y.; Chatzaki, E.; Tsamardinos, I. Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. In Discovery Science; Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S., Eds.; Series Title: Lecture Notes in Computer Science; Springer International Publishing: Cham, Swtizerland, 2020; Volume 12323, pp. 246–261. [Google Scholar] [CrossRef]
- Graham, D.B.; Xavier, R.J. Pathway paradigms revealed from the genetics of inflammatory bowel disease. Nature 2020, 578, 527–539. [Google Scholar] [CrossRef] [PubMed]
- Guo, Z.; Zhang, T.; Li, X.; Wang, Q.; Xu, J.; Yu, H.; Zhu, J.; Wang, H.; Wang, C.; Topol, E.J.; et al. Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinform. 2005, 6, 58. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wagle, M.C.; Kirouac, D.; Klijn, C.; Liu, B.; Mahajan, S.; Junttila, M.; Moffat, J.; Merchant, M.; Huw, L.; Wongchenko, M.; et al. A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types. NPJ Precis. Oncol. 2018, 2, 7. [Google Scholar] [CrossRef] [Green Version]
- Huang, S.; Yee, C.; Ching, T.; Yu, H.; Garmire, L.X. A novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer. PLoS Comput. Biol. 2014, 10, e1003851. [Google Scholar] [CrossRef] [PubMed]
- Liu, K.Q.; Liu, Z.P.; Hao, J.K.; Chen, L.; Zhao, X.M. Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinform. 2012, 13, 126. [Google Scholar] [CrossRef] [Green Version]
- Chen, X.; Wang, L. Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer. J. Comput. Biol. 2009, 16, 265–278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Drier, Y.; Sheffer, M.; Domany, E. Pathway-based personalized analysis of cancer. Proc. Natl. Acad. Sci. USA 2013, 110, 6388–6393. [Google Scholar] [CrossRef] [Green Version]
- van de Stolpe, A.; Holtzer, L.; van Ooijen, H.; Inda, M.A.d.; Verhaegh, W. Enabling precision medicine by unravelling disease pathophysiology: Quantifying signal transduction pathway activity across cell and tissue types. Sci. Rep. 2019, 9, 1603. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, E.; Chuang, H.Y.; Kim, J.W.; Ideker, T.; Lee, D. Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput. Biol. 2008, 4, e1000217. [Google Scholar] [CrossRef]
- Holland, C.H.; Szalai, B.; Saez-Rodriguez, j. Transfer of regulatory knowledge from human to mouse for functional genomics analysis. Biochim. Biophys. Acta (BBA) 2020, 1863, 194431. [Google Scholar] [CrossRef] [PubMed]
- Yang, L.; Ainali, C.; Tsoka, S.; Papageorgiou, L.G. Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC Bioinform. 2014, 15, 390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, Y.; Theofilatos, K.; Papageorgiou, L.G.; Tsoka, S. Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS. In Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology, Xi’an China, 22–24 May 2020; Association for Computing Machinery: New York, NY, USA, 2020. ICBBT 2020. pp. 25–31. [Google Scholar] [CrossRef]
- Yang, R.; Daigle, B.J.; Petzold, L.R.; Doyle, F.J. Core module biomarker identification with network exploration for breast cancer metastasis. BMC Bioinform. 2012, 13, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bild, A.H.; Yao, G.; Chang, J.T.; Wang, Q.; Potti, A.; Chasse, D.; Joshi, M.B.; Harpole, D.; Lancaster, J.M.; Berchuck, A.; et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439, 353–357. [Google Scholar] [CrossRef]
- Tomczak, K.; Czerwińska, P.; Wiznerowicz, M. Review The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Współczesna Onkol. 2015, 1A, 68–77. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Xiang, J.; Tang, L.; Li, J.; Lu, Q.; Tian, G.; He, B.S.; Yang, J. Identifying Breast Cancer-Related Genes Based on a novel Computational Framework Involving KEGG Pathways and PPI Network Modularity. Front. Genet. 2021, 12, 596794. [Google Scholar] [CrossRef]
- Wilson, H.E.; Stanton, D.A.; Montgomery, C.; Infante, A.M.; Taylor, M.; Hazard-Jenkins, H.; Pugacheva, E.N.; Pistilli, E.E. Skeletal muscle reprogramming by breast cancer regardless of treatment history or tumor molecular subtype. NPJ Breast Cancer 2020, 6, 18. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.; Park, Y.; Kim, S. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods 2017, 124, 13–24. [Google Scholar] [CrossRef]
- Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2016: Cancer Statistics, 2016. Cancer J. Clin. 2016, 66, 7–30. [Google Scholar] [CrossRef] [Green Version]
- Siegel, R.L.; Miller, K.D.; Goding Sauer, A.; Fedewa, S.A.; Butterly, L.F.; Anderson, J.C.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2020. Cancer J. Clin. 2020, 70, 145–164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shahriyari, L. Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma. Briefings Bioinform. 2019, 20, 985–994. [Google Scholar] [CrossRef] [Green Version]
- Guinney, J.; Dienstmann, R.; Wang, X.; de Reyniès, A.; Schlicker, A.; Soneson, C.; Marisa, L.; Roepman, P.; Nyamundanda, G.; Angelino, P.; et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 2015, 21, 1350–1356. [Google Scholar] [CrossRef] [PubMed]
- Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J. Clin. Oncol. 2009, 27, 1160–1167. [Google Scholar] [CrossRef]
- Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef] [PubMed]
- Mathews, J.C.; Nadeem, S.; Levine, A.J.; Pouryahya, M.; Deasy, J.O.; Tannenbaum, A. Robust and interpretable PAM50 reclassification exhibits survival advantage for myoepithelial and immune phenotypes. NPJ Breast Cancer 2019, 5, 30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yersal, O.; Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 2014, 5, 412. [Google Scholar] [CrossRef] [PubMed]
- Kanehisa, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32, 277D–280. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bussieck, M.R.; Meeraus, A. General Algebraic Modeling System (GAMS). In Modeling Languages in Mathematical Optimization; Pardalos, P.M., Hearn, D.W., Kallrath, J., Eds.; Series Title: Applied Optimization; Springer US: Boston, MA, USA, 2004; Volume 88, pp. 137–157. [Google Scholar] [CrossRef]
- Earl, M.; D’Andrea, R. Iterative MILP methods for vehicle-control problems. IEEE Trans. Robot. 2005, 21, 1158–1167. [Google Scholar] [CrossRef] [Green Version]
- Hänzelmann, S.; Castelo, R.; Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013, 14, 7. [Google Scholar] [CrossRef] [Green Version]
- Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 2005, 102, 15545–15550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lim, S.; Lee, S.; Jung, I.; Rhee, S.; Kim, S. Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Briefings Bioinform. 2018, 21, 36–46. [Google Scholar] [CrossRef]
- Fernandez, A.; Garcia, S.; Herrera, F.; Chawla, N.V. SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. J. Artif. Intell. Res. 2018, 61, 863–905. [Google Scholar] [CrossRef]
- Hao, J.; Ho, T.K. Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. J. Educ. Behav. Stat. 2019, 44, 348–361. [Google Scholar] [CrossRef]
- Segura-Lepe, M.P.; Keun, H.C.; Ebbels, T.M.D. Predictive modelling using pathway scores: Robustness and significance of pathway collections. BMC Bioinform. 2019, 20, 543. [Google Scholar] [CrossRef]
- Ishwaran, H.; Kogalur, U.B.; Blackstone, E.H.; Lauer, M.S. Random survival forests. Ann. Appl. Stat. 2008, 2. [Google Scholar] [CrossRef]
- Sebastian, P. scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn. J. Mach. Learn. Res. 2020, 21, 1–6. [Google Scholar]
- Longato, E.; Vettoretti, M.; Di Camillo, B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J. Biomed. Inform. 2020, 108, 103496. [Google Scholar] [CrossRef] [PubMed]
- Temate-Tiagueu, Y.; Seesi, S.A.; Mathew, M.; Mandric, I.; Rodriguez, A.; Bean, K.; Cheng, Q.; Glebova, O.; Măndoiu, I.; Lopanik, N.B.; et al. Inferring metabolic pathway activity levels from RNA-Seq data. BMC Genom. 2016, 17, 542. [Google Scholar] [CrossRef] [Green Version]
- Kim, S.Y.; Jeong, H.H.; Kim, J.; Moon, J.H.; Sohn, K.A. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies. Biol. Direct 2019, 14, 8. [Google Scholar] [CrossRef]
- Lundberg, S.M.; Lee, S.I. A Unified Approach to Interpreting Model Predictions. In Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017; Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2017; Volume 30. [Google Scholar]
- Lundberg, S.M.; Erion, G.; Chen, H.; DeGrave, A.; Prutkin, J.M.; Nair, B.; Katz, R.; Himmelfarb, J.; Bansal, N.; Lee, S.I. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2020, 2, 56–67. [Google Scholar] [CrossRef]
- Welz, P.S.; Benitah, S. Molecular Connections Between Circadian Clocks and Aging. J. Mol. Biol. 2020, 432, 3661–3679. [Google Scholar] [CrossRef] [PubMed]
- Welsh, D.K.; Yoo, S.H.; Liu, A.C.; Takahashi, J.S.; Kay, S.A. Bioluminescence Imaging of Individual Fibroblasts Reveals Persistent, Independently Phased Circadian Rhythms of Clock Gene Expression. Curr. Biol. 2004, 14, 2289–2295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bevinakoppamath, S.; Ramachandra, S.C.; Yadav, A.K.; Basavaraj, V.; Vishwanath, P.; Prashant, A. Understanding the Emerging Link Between Circadian Rhythm, Nrf2 Pathway, and Breast Cancer to Overcome Drug Resistance. Front. Pharmacol. 2022, 12, 719631. [Google Scholar] [CrossRef]
- Kim, J.A. Peroxisome Metabolism in Cancer. Cells 2020, 9, 1692. [Google Scholar] [CrossRef] [PubMed]
- Cavaletti, G.; Alberti, P.; Marmiroli, P. Chemotherapy-induced peripheral neurotoxicity in the era of pharmacogenomics. Lancet Oncol. 2011, 12, 1151–1161. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Li, W.; Shen, S.; Yang, X.; Lu, B.; Zhang, X.; Lu, P.; Shen, Y.; Ji, j. Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis. J. Transl. Med. 2019, 17, 390. [Google Scholar] [CrossRef] [Green Version]
- Chen, P.; Li, C.; Li, X.; Li, J.; Chu, R.; Wang, H. Higher dietary folate intake reduces the breast cancer risk: A systematic review and meta-analysis. Br. J. Cancer 2014, 110, 2327–2338. [Google Scholar] [CrossRef]
- Tagde, P.; Kulkarni, G.T.; Mishra, D.K.; Kesharwani, P. Recent advances in folic acid engineered nanocarriers for treatment of breast cancer. J. Drug Deliv. Sci. Technol. 2020, 56, 101613. [Google Scholar] [CrossRef]
- Wang, S.; Yang, J.; You, L.; Dai, M.; Zhao, Y. GSTM3 Function and Polymorphism in Cancer: Emerging but Promising. Cancer Manag. Res. 2020, 12, 10377–10388. [Google Scholar] [CrossRef] [PubMed]
- Wolff, M.S.; Britton, j.A.; Wilson, V.P. Environmental risk factors for breast cancer among African-American women. Cancer 2003, 97, 289–310. [Google Scholar] [CrossRef]
- Ambrosone, C.B.; Coles, B.F.; Freudenheim, J.L.; Shields, P.G. Glutathione-S-transferase (GSTM1) Genetic Polymorphisms Do Not Affect Human Breast Cancer Risk, Regardless of Dietary Antioxidants. J. Nutr. 1999, 129, 565S–568S. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Weber, B.; Nathanson, K. Low penetrance genes associated with increased risk for breast cancer. Eur. J. Cancer 2000, 36, 1193–1199. [Google Scholar] [CrossRef] [PubMed]
- Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lei, X.; Zhang, Y.; Cheng, S.; Wu, F.X.; Pedrycz, W. Topology potential based seed-growth method to identify protein complexes on dynamic PPI data. Inf. Sci. 2018, 425, 140–153. [Google Scholar] [CrossRef]
- Cline, M.S.; Smoot, M.; Cerami, E.; Kuchinsky, A.; Landys, N.; Workman, C.; Christmas, R.; Avila-Campilo, I.; Creech, M.; Gross, B.; et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007, 2, 2366–2382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Potter, C.P.S.; Harris, A.L. Diagnostic, prognostic and therapeutic implications of carbonic anhydrases in cancer. Br. J. Cancer 2003, 89, 2–7. [Google Scholar] [CrossRef] [Green Version]
- Sutherland, R.; Meeson, A.; Lowes, S. Solute transporters and malignancy: Establishing the role of uptake transporters in breast cancer and breast cancer metastasis. Cancer Metastasis Rev. 2020, 39, 919–932. [Google Scholar] [CrossRef]
- Jelski, W.; Szmitkowski, M. Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) in the cancer diseases. Clin. Chim. Acta 2008, 395, 1–5. [Google Scholar] [CrossRef]
Dataset | Tumour or Normal Label | Molecular Subtype Label |
---|---|---|
COAD | Tumour: 480 Normal: 41 | CMS1: 85 |
CMS2: 165 | ||
CMS3: 58 | ||
CMS4: 120 | ||
BRCA | Tumour: 1091 Normal: 120 | LumA: 579 |
LumB: 217 | ||
Basal: 191 | ||
Her2: 82 | ||
Normal-Like: 22 |
Classifier | BRCA | COAD |
---|---|---|
DIOPTRA | 0.67 (0.064) | 0.75 (0.043) |
DIOPTRA+KNN | 0.74 (0.046) | 0.76 (0.068) |
DIOPTRA+RF | 0.84 (0.031) | 0.85 (0.060) |
KEGG Pathway Name | No. Gene | Top Genes and Weights 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pancreatic secretion | 104 | CA2 | 4.36 | CPA1 | 0.72 | CHRM3 | 0.69 | PRSS2 | 0.53 | CPA2 | 0.33 |
Circadian rhythm | 32 | RORB | 3.24 | ROR1 | 0.78 | PRKAA2 | 0.49 | RORA | 0.49 | CUL1 | 0.47 |
Peroxisome | 87 | AGXT | 5.39 | HAO2 | 3.21 | ACSL6 | 0.23 | PEX11A | 0.10 | IDH2 | 0.08 |
Chemical carcinogenesis | 81 | GSTM5 | 6.07 | PTGS2 | 0.71 | GSTA1 | 0.66 | GSTA2 | 0.44 | CYP1A1 | 0.30 |
Platinum drug resistance | 73 | GSTM5 | 6.71 | GSTA2 | 1.30 | GSTA1 | 0.82 | CDKN2A | 0.21 | GSTT2B | 0.15 |
Drug metabolism cytochrome P450 | 70 | GSTM5 | 6.33 | GSTA1 | 0.91 | GSTA2 | 0.69 | UGT2B11 | 0.22 | FMO2 | 0.21 |
Folate biosynthesis | 30 | TPH1 | 4.11 | PAH | 3.88 | ALPL | 0.79 | MOCOS | 0.34 | FPGS | 0.20 |
Drug metabolism other enzymes | 79 | GSTM5 | 5.98 | GSTA1 | 0.93 | GSTA2 | 0.65 | XDH | 0.55 | GSTT2B | 0.21 |
Cocaine addiction | 50 | SLC18A2 | 3.56 | DRD1 | 1.16 | GRIN2A | 0.93 | CREB3L3 | 0.51 | SLC18A1 | 0.41 |
Carbon metabolism | 118 | AGXT | 3.468 | HAO2 | 2.178 | ALDOB | 1.962 | PHGDH | 0.605 | PSAT1 | 0.147 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, Y.; Liu, S.; Papageorgiou, L.G.; Theofilatos, K.; Tsoka, S. Optimisation Models for Pathway Activity Inference in Cancer. Cancers 2023, 15, 1787. https://doi.org/10.3390/cancers15061787
Chen Y, Liu S, Papageorgiou LG, Theofilatos K, Tsoka S. Optimisation Models for Pathway Activity Inference in Cancer. Cancers. 2023; 15(6):1787. https://doi.org/10.3390/cancers15061787
Chicago/Turabian StyleChen, Yongnan, Songsong Liu, Lazaros G. Papageorgiou, Konstantinos Theofilatos, and Sophia Tsoka. 2023. "Optimisation Models for Pathway Activity Inference in Cancer" Cancers 15, no. 6: 1787. https://doi.org/10.3390/cancers15061787