Mediaboss Marketing

Khwaja, A. et al. Acute myeloid leukaemia. Nat. Rev. Dis. Prim. 2, Article 16010 (2016).

Kurtz, S. E. et al. Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1703094114 (2017).

Day, D. & Siu, L. L. Approaches to modernize the combination drug development paradigm. Genome Med. 8, 115 (2016).

Article PubMed PubMed Central Google Scholar

ONeil, J. et al. An unbiased oncology compound screen to identify novel combination strategies. Mol. Cancer Ther. 15, 11551162 (2016).

Article PubMed Google Scholar

Jia, J. et al. Mechanisms of drug combinations: interaction and network perspectives. Nat. Rev. Drug Discov. 8, 111128 (2009).

Article CAS PubMed Google Scholar

Nair, R., Salinas-Illarena, A. & Baldauf, H.-M. New strategies to treat AML: novel insights into AML survival pathways and combination therapies. Leukemia 35, 299311 (2021).

Article CAS PubMed Google Scholar

Tyner, J. W. & Others, A. Functional genomic landscape of acute myeloid leukaemia. Nature 562, 526531 (2018).

Article CAS PubMed PubMed Central Google Scholar

Schenone, M., Dank, V., Wagner, B. K. & Clemons, P. A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232240 (2013).

Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682690 (2008).

Article CAS PubMed Google Scholar

Calzolari, D. et al. Search algorithms as a framework for the optimization of drug combinations. PLoS Comput. Biol. 4, e1000249 (2008).

Article PubMed PubMed Central Google Scholar

Feala, J. D. et al. Systems approaches and algorithms for discovery of combinatorial therapies. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 181193 (2010).

Article PubMed Google Scholar

Wong, P. K. et al. Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm. Proc. Natl Acad. Sci. USA 105, 51055110 (2008).

Article CAS PubMed PubMed Central Google Scholar

Menden, M. P. et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat. Commun. 10, 2674 (2019).

Article PubMed PubMed Central Google Scholar

Preuer, K. et al. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 34, 15381546 (2018).

Article CAS PubMed Google Scholar

Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570575 (2012).

Article CAS PubMed PubMed Central Google Scholar

Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603607 (2012).

Article CAS PubMed PubMed Central Google Scholar

Lundberg, S. M. & Lee, S.-I. in Advances in Neural Information Processing Systems (eds Guyon, I., Von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., & Garnett, R.) 47654774 (Curran Associates, Inc., 2017).

Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 5667 (2020).

Article PubMed PubMed Central Google Scholar

Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 31453153 (PMLR, 2017).

Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In Proc. 34th International Conference on Machine Learning, PMLR (eds Precup, D. & Teh, Y. W.) 33193328 (JMLR.org, 2017).

Shapley, L. S. A value for n-person games. Class. game theory 69 (1997).

Aas, K., Jullum, M. & Lland, A. Explaining individual predictions when features are dependent: more accurate approximations to Shapley values. Artif. Intell. 298, 103502 (2021).

Article Google Scholar

Koo, P. K. & Ploenzke, M. Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat. Mach. Intell. 3, 258266 (2021).

Article PubMed PubMed Central Google Scholar

Schreiber, J. & Singh, R. Machine learning for profile prediction in genomics. Curr. Opin. Chem. Biol. 65, 3541 (2021).

Article CAS PubMed Google Scholar

Covert, I., Lundberg, S. & Lee, S.-I. Explaining by removing: a unified framework for model explanation. J. Mach. Learn. Res. 22, 190 (2021).

Google Scholar

Kim, N. et al. Prediction of the sequence-specific cleavage activity of Cas9 variants. Nat. Biotechnol. 38, 13281336 (2020).

Article CAS PubMed Google Scholar

Kim, H. K. et al. Predicting the efficiency of prime editing guide RNAs in human cells. Nat. Biotechnol. 39, 198206 (2021).

Article CAS PubMed Google Scholar

Schultebraucks, K. et al. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat. Med. 26, 10841088 (2020).

Article CAS PubMed Google Scholar

Hyland, S. L. et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat. Med. 26, 364373 (2020).

Article CAS PubMed Google Scholar

Meier, F. et al. Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nat. Commun. 12, Article 1185 (2021).

Bar, N. et al. A reference map of potential determinants for the human serum metabolome. Nature 588, 135140 (2020).

Article PubMed Google Scholar

Rodriguez-Perez, R. & Bajorath, J. Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values. J. Med. Chem. 63, 87618777 (2019).

Article PubMed Google Scholar

Rodriguez-Perez, R. & Bajorath, J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34, 10131026 (2020).

Article CAS PubMed PubMed Central Google Scholar

Tang, Y.-C. & Gottlieb, A. Explainable drug sensitivity prediction through cancer pathway enrichment. Sci. Rep. 11, Article 3128 (2021).

Braithwaite, B. et al. Detection of medications associated with Alzheimers disease using ensemble methods and cooperative game theory. Int. J. Med. Inform. 141, 104142 (2020).

Article CAS PubMed Google Scholar

Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199231 (2001).

Article Google Scholar

Dong, J. & Rudin, C. Variable importance clouds: a way to explore variable importance for the set of good models. Preprint at https://doi.org/10.48550/arXiv.1901.03209 (2019).

Hooker, S., Erhan, D., Kindermans, P.-J. & Kim, B. A benchmark for interpretability methods in deep neural networks. In 33rd Conference on Neural Information Processing Systems (eds Wallach, H., Larochelle, H., Beygelzimer, A., d'Alch-Buc, F., Fox, E. & Garnett, R.) (Curran Associates, Inc., 2019).

Song, L., Bedo, J., Borgwardt, K. M., Gretton, A. & Smola, A. Gene selection via the BAHSIC family of algorithms. Bioinformatics 23, i490i498 (2007).

Article CAS PubMed Google Scholar

Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301320 (2005).

Article Google Scholar

Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389422 (2002).

Article Google Scholar

Avsec, . et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354366 (2021).

Article CAS PubMed PubMed Central Google Scholar

Maslova, A. et al. Deep learning of immune cell differentiation. Proc. Natl Acad. Sci. USA 117, 2565525666 (2020).

Article CAS PubMed PubMed Central Google Scholar

Farzaneh, N., Williamson, C. A., Gryak, J. & Najarian, K. A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication. npj Digit. Med. 4, 78 (2021).

Article PubMed PubMed Central Google Scholar

Breiman, L. Random forests. Mach. Learn. 45, 532 (2001).

Article Google Scholar

Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785794 (ACM, 2016).

King, R. D., Orhobor, O. I. & Taylor, C. C. Cross-validation is safe to use. Nat. Mach. Intell. 3, 276 (2021).

Article Google Scholar

Shwartz-Ziv, R. & Armon, A. Tabular data: deep learning is not all you need. Inf. Fusion 81, 8490 (2022).

Article Google Scholar

Gurska, L. M., Ames, K. & Gritsman, K. Signaling pathways in leukemic stem cells. Adv. Exp. Med. Biol. 1143, 139 (2019).

Article CAS PubMed PubMed Central Google Scholar

Kumar, A. R., Sarver, A. L., Wu, B. & Kersey, J. H. Meis1 maintains stemness signature in MLL-AF9 leukemia. Blood 115, 36423643 (2010).

Article CAS PubMed PubMed Central Google Scholar

Liu, J. et al. Meis1 is critical to the maintenance of human acute myeloid leukemia cells independent of MLL rearrangements. Ann. Hematol. 96, 567574 (2017).

Article CAS PubMed Google Scholar

Pei, S. et al. Monocytic subclones confer resistance to venetoclax-based therapy in patients with acute myeloid leukemia. Cancer Discov. 10, 536551 (2020).

Article CAS PubMed PubMed Central Google Scholar

Takam Kamga, P. et al. Prognostic impact of notch signaling in acute myeloid leukemia (AML). Blood 132, 5242 (2018).

Article Google Scholar

Kranc, K. R. et al. Cited2 is an essential regulator of adult hematopoietic stem cells. Cell Stem Cell 5, 659665 (2009).

Article CAS PubMed PubMed Central Google Scholar

Korthuis, P. M. et al. CITED2-mediated human hematopoietic stem cell maintenance is critical for acute myeloid leukemia. Leukemia 29, 625635 (2015).

Tanaka, M. et al. Targeted disruption of oncostatin M receptor results in altered hematopoiesis. Blood 102, 31543162 (2003).

Article CAS PubMed Google Scholar

Zhao, X., Li, Y. & Wu, H. A novel scoring system for acute myeloid leukemia risk assessment based on the expression levels of six genes. Int. J. Mol. Med. 42, 14951507 (2018).

Go here to read the rest:
Uncovering expression signatures of synergistic drug responses via ... - Nature.com