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Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

, , , , , , и . Bioinform., 36 (Supplement-1): i300-i308 (2020)

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Martin Vitek

Lucie Vitek

Üstün Olgay

Helga Walter-Vitek

Olga Munteanu

Rapidly changing automotive global value chains in Hungary: multinational vs. local firm perspectives. Uni Leipzig, (2009)
 

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A Bayesian Active Learning Experimental Design for Inferring Signaling Networks., , , и . RECOMB, том 10229 из Lecture Notes in Computer Science, стр. 134-156. (2017)Investigating usability of MSstatsQC software., , , , и . CoRR, (2020)Statistical Inference of Peroxisome Dynamics., , , и . RECOMB, том 10812 из Lecture Notes in Computer Science, стр. 54-74. Springer, (2018)A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images., , , и . Bioinform., (февраля 2023)Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics, , , , , , , , , и 4 other автор(ы). BMC Bioinformatics, 9 (1): 542 (2008)Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems., , и . NeurIPS, стр. 14211-14221. (2019)Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments., , , , , , , и . Bioinform., 31 (14): 2418-2420 (2015)Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations., , , , , , и . Bioinform., 36 (Supplement-1): i300-i308 (2020)Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size., , и . Bioinform., 29 (10): 1275-1282 (2013)Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs., , , , и . BMC Bioinform., 13 (S-16): S6 (2012)