XAI-enabled neural network analysis of metabolite spatial distributions

Amstalden van Hove ER, Smith DF, Heeren RM. A concise review of mass spectrometry imaging. J Chromatogr A. 2010;1217(25):3946–54. https://doi.org/10.1016/j.chroma.2010.01.033.

Article  CAS  PubMed  Google Scholar 

Miyamoto S, Hsu CC, Hamm G, Darshi M, Diamond-Stanic M, Decleves AE, et al. Mass spectrometry imaging reveals elevated glomerular ATP/AMP in diabetes/obesity and identifies sphingomyelin as a possible mediator. EBioMed. 2016;7:121–34. https://doi.org/10.1016/j.ebiom.2016.03.033.

Article  Google Scholar 

O’Hurley G, Sjöstedt E, Rahman A, Li B, Kampf C, Pontén F, et al. Garbage in, garbage out: a critical evaluation of strategies used for validation of immunohistochemical biomarkers. Mol Oncol. 2014;8(4):783–98. https://doi.org/10.1016/j.molonc.2014.03.008.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vaysse PM, Heeren RMA, Porta T, Balluff B. Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations. Analyst. 2017;142(15):2690–712. https://doi.org/10.1039/c7an00565b.

Article  CAS  PubMed  Google Scholar 

Mikolov T, Deoras A, Povey D, Burget L, Černocký J. Strategies for training large scale neural network language models. In: IEEE Workshop on Automatic Speech Recognition & Understanding. 2011. pp. 11–5. https://doi.org/10.1109/ASRU.2011.6163930.

Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1915–29. https://doi.org/10.1109/TPAMI.2012.231.

Article  PubMed  Google Scholar 

Tompson J, Jain A, LeCun Y, Bregler C. Joint training of a convolutional network and a graphical model for human pose estimation. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 1. Montreal: MIT Press; 2014. p. 1799–807. https://doi.org/10.48550/arXiv.1406.2984.

Szegedy C, Wei L, Yangqing J, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015. p. 7–12. https://doi.org/10.1109/CVPR.2015.7298594.

Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure–activity relationships. J Chem Inf Model. 2015;55(2):263–74. https://doi.org/10.1021/ci500747n.

Article  CAS  PubMed  Google Scholar 

Leung MK, Xiong HY, Lee LJ, Frey BJ. Deep learning of the tissue-regulated splicing code. Bioinf. 2014;30(12):i121–9. https://doi.org/10.1093/bioinformatics/btu277.

Article  CAS  Google Scholar 

Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Sci. 2015;347(6218):1254806. https://doi.org/10.1126/science.1254806.

Article  CAS  Google Scholar 

Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nat. 2013;500(7461):168–74. https://doi.org/10.1038/nature12346.

Article  CAS  Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep learning. Nat. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.

Article  CAS  Google Scholar 

Poggio T, Mhaskar H, Rosasco L, Miranda B, Liao Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review. Int J Autom Comput. 2017;14(5):503–19. https://doi.org/10.1007/s11633-017-1054-2.

Article  Google Scholar 

Grohs P, Hornung F, Jentzen A, Pv W. A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. ArXiv. 2018:abs/1809.02362. https://doi.org/10.48550/arXiv.1809.02362.

Behrmann J, Etmann C, Boskamp T, Casadonte R, Kriegsmann J, Maass P. Deep learning for tumor classification in imaging mass spectrometry. Bioinf. 2018;34(7):1215–23. https://doi.org/10.1093/bioinformatics/btx724.

Article  CAS  Google Scholar 

Mittal P, Condina MR, Klingler-Hoffmann M, Kaur G, Oehler MK, Sieber OM, et al. Cancer tissue classification using supervised machine learning applied to MALDI mass spectrometry imaging. Cancers (Basel). 2021;13(21). https://doi.org/10.3390/cancers13215388

Klein O, Kanter F, Kulbe H, Jank P, Denkert C, Nebrich G, et al. MALDI-imaging for classification of epithelial ovarian cancer histotypes from a tissue microarray using machine learning methods. Proteomics Clin Appl. 2019;13(1):e1700181. https://doi.org/10.1002/prca.201700181.

Article  CAS  PubMed  Google Scholar 

Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82–97. https://doi.org/10.1109/MSP.2012.2205597.

Article  Google Scholar 

Hochreiter S. Untersuchungen zu dynamischen neuronalen Netzen. 1991. https://doi.org/10.48550/arXiv.2102.04906.

Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw. 1994;5(2):157–66. https://doi.org/10.1109/72.279181.

Article  CAS  PubMed  Google Scholar 

Bengio Y, Glorot X. Understanding the difficulty of training deep feed forward neural networks. Proc AISTATS. 2010. http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE. 2016. https://doi.org/10.48550/arXiv.1512.03385.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Sci. 2014. https://doi.org/10.48550/arXiv.1409.1556.

Bishop CM. Neural networks for pattern recognition. Agric Eng Int Cigr J Sci Res Dev Manu Pm. 1995;12(5):1235–42. https://doi.org/10.7551/mitpress/4923.001.0001.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128(2):336–59. https://doi.org/10.1109/ICCV.2017.74.

He Y, Zhang X, Sun J. Channel pruning for accelerating very deep neural networks. 2017. https://doi.org/10.48550/arXiv.1707.06168.

Zhuang Z, Tan M, Zhuang B, Liu J, Guo Y, Wu Q, et al. Discrimination-aware channel pruning for deep neural networks. Neural Inf Process Syst. 2018. https://doi.org/10.48550/arXiv.1810.11809.

Alvarez JM, Salzmann M. Learning the number of neurons in deep networks. 2016. https://doi.org/10.48550/arXiv.1611.06321.

Zhuang L, Li J, Shen Z, Gao H, Zhang C. Learning efficient convolutional networks through network slimming. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2017. https://doi.org/10.48550/arXiv.1708.06519.

Hu H, Peng R, Tai YW, Tang CK. Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. 2016. https://doi.org/10.48550/arXiv.1607.03250.

Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Oxford University Press. 2019. https://doi.org/10.1093/nar/gky1106.

Luo L, Ma W, Liang K, Wang Y, Su J, Liu R, Liu T, Shyh-Chang N. Spatial metabolomics reveals skeletal myofiber subtypes. Sci Adv. 2023;9(5):eadd0455. https://doi.org/10.1126/sciadv.add0455.

Chaurand P, Norris JL, Cornett DS, Mobley JA, Caprioli RM. New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry. J Proteome Res. 2006;5(11):2889–900. https://doi.org/10.1021/pr060346u.

Zhu Y, Zang Q, Luo Z, He J, Zhang R, Abliz Z. An organ-specific metabolite annotation approach for ambient mass spectrometry imaging reveals spatial metabolic alterations of a whole mouse body. Anal Chem. 2022;94(20):7286–94. https://doi.org/10.1021/acs.analchem.2c00557.

Dilillo M, Ait-Belkacem R, Esteve C, Pellegrini D, Nicolardi S, Costa M, et al. Ultra-high mass resolution MALDI imaging mass spectrometry of proteins and metabolites in a mouse model of glioblastoma. Sci Rep. 2017;7(1):603. https://doi.org/10.1038/s41598-017-00703-w.

Buchberger AR, Delaney K, Johnson J, Li L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal Chem. 2017. https://doi.org/10.1021/acs.analchem.7b04733.

Pere R, Bram H, Esteban DC, Oscar Y, Mcdonnell LA, Jesús B, et al. rMSIproc: an R package for mass spectrometry imaging data processing. Bioinf. (11):11. https://doi.org/10.1093/bioinformatics/btaa142.

Wishart DS, Djoumbou FY, Ana M, Guo AC, Liang K, Rosa VF, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2017;(D1):D1. https://doi.org/10.1093/nar/gkx1089.

Safavian SR, Landgrebe D, editors. A survey of decision tree classifier methodology. Syst Man Cybern. 1991. https://doi.org/10.1109/21.97458.

Zhang H, Ling CX. Geometric properties of naive Bayes in nominal domains. Eur Conf Mach Learn. 2001. https://doi.org/10.1016/j.inffus.2009.09.007.

Guo G, Wang H, Bell D, Bi Y, Greer K. KNN model-based approach in classification. In: OTM confederated international conferences On the move to meaningful internet systems. 2003. https://doi.org/10.1007/978-3-540-39964-3_62.

Liaw A, Wiener M. Classification and regression by randomForest. R News. 2001;2:18–22. https://doi.org/10.1021/ci034160g.

Maldonado S, López J. Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification. Appl Soft Comput. 2018:94–105. https://doi.org/10.1016/j.asoc.2018.02.051.

Powers D. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. 2020. https://doi.org/10.48550/arXiv.2010.16061.

Jiang Z, Guang L, Li L, Shyh-Chang N. Putting stem cells on a low-fat diet switches their pluripotent state. Cell Stem Cell. 2019;25:3–5. https://doi.org/10.1016/j.stem.2019.06.002.

Article  CAS  PubMed  Google Scholar 

Yao Z, Chen Y, Cao W, Shyh-Chang N. Chromatin-modifying drugs and metabolites in cell fate control. Cell Prolif. 2020;53(11):e12898. https://doi.org/10.1111/cpr.12898.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lagerwaard B, Hoek M, Hoeks J, Grevendonk L, Boer V. Propionate hampers differentiation and modifies histone propionylation and acetylation in skeletal muscle cells. Mech Ageing Dev. 2021;196:111495. https://doi.org/10.1016/j.mad.2021.111495.

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