Items where Subject is "Mathematical and Computer Sciences > Artificial Intelligence > Machine Learning"

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Bleich, Amnon and Linnemann, Antje and Benjamin, Jaidi and Bjoern, H. Diem and Conrad, T. O. F. (2023) Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification. Machine Learning and Knowledge Extraction, 5 (4). ISSN 2504-4990

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Fiedler, G. M. and Leichtle, A. and Kase, J. and Baumann, S. and Ceglarek, U. and Felix, K. and Conrad, T. O. F. and Witzigmann, H. and Weimann, A. and Schütte, Ch. and Hauss, J. and Büchler, M. and Thiery, J. (2009) Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated With Pancreatic Cancer. Clinical Cancer Research, 15 (11). pp. 3812-3819. ISSN 1078-0432

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Gaskin, Thomas and Conrad, T. O. F. and Pavliotis, Grigorios A. and Schütte, Ch. (2024) Neural parameter calibration and uncertainty quantification for epidemic forecasting. PLoS ONE, 19 (10). ISSN 1932-6203

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Iravani, Sahar and Conrad, T. O. F. (2019) Deep Learning for Proteomics Data for Feature Selection and Classification. Lecture Notes in Computer Science, 11713 .

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Jayrannejad, Fahrnaz and Conrad, T. O. F. (2017) Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining. Springer Lecture Notes in Artificial Intelligence .

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Leichtle, A. and Ceglarek, U. and Weinert, P. and Nakas, C. T. and Nuoffer, J.-M. and Kase, J. and Conrad, T. O. F. and Witzigmann, H. and Thiery, J. and Fiedler, G. M. (2013) Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma. Metabolomics, 9 (3). pp. 677-687. ISSN 1573-3890

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Mardt, A. and Pasquali, L. and Wu, H. and Noé, F. (2018) VAMPnets: Deep learning of molecular kinetics. Nat. Comm., 9 . p. 5.

Marzban, Forough and Conrad, T. O. F. and Marzban, Pouria and Sodoudi, Sahar (2018) Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin, Germany. International Journal of Advanced Remote Sensing and GIS, 7 (1). ISSN 2320-0243

Melnyk, Kateryna and Weimann, K. and Conrad, T. O. F. (2023) Understanding microbiome dynamics via interpretable graph representation learning. Scientific Reports, 13 (2058). ISSN 2045-2322

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Noé, F. and Olsson, S. and Köhler, J. and Wu, H. (2019) Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science, 365 (6457). eaaw1147.

Nüske, F. and Wu, H. and Wehmeyer, C. and Clementi, C. and Noé, F. (2017) Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias. J. Chem. Phys., 146 . 094104.

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Scherer, M. K. and Husic, B.E. and Hoffmann, M. and Paul, F. and Wu, H. and Noé, F. (2018) Variational Selection of Features for Molecular Kinetics. SFB 1114 Preprint in arXiv:1811.11714 . pp. 1-12. (Unpublished)

Shao, Borong and Carlo, Cannistraci and Conrad, T. O. F. (2017) Epithelial Mesenchymal Transition Network-based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data. Genomics and Computational Biology, 3 (3). ISSN 2365-7154

Shao, Borong and Bjaanæs, Maria and Helland, Åslaug and Schütte, Ch. and Conrad, T. O. F. (2019) EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma. PLoS ONE, 14 (1). ISSN 1932-6203

Strenziok, R. and Hinz, S. and Wolf, C. and Conrad, T. O. F. and Krause, H. and Lingnau, A. and Lein, M. and Miller, K. and Schrader, M. (2008) Serum proteomic profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry in testicular germ cell cancer patients. European Urology Supplements, 7 (3). p. 83.

Strenziok, R. and Hinz, S. and Wolf, C. and Conrad, T. O. F. and Krause, H. and Miller, K. and Schrader, M. (2009) Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry: serum protein profiling in seminoma patients. World J of Urology, 28 (2). pp. 193-197.

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Weimann, K. and Conrad, T. O. F. (2024) FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems. International Journal of Data Science and Analytics, 19 (1).

Weimann, K. and Conrad, T. O. F. (2024) Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification. IEEE Journal of Biomedical and Health Informatics, 28 (11). ISSN 2168-2194

Weimann, K. and Conrad, T. O. F. (2023) Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks. Computing in Cardiology (CinC) 2023, 50 .

Weimann, K. and Conrad, T. O. F. (2024) Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance. IEEE . (Submitted)

Weimann, K. and Conrad, T. O. F. (2021) Transfer Learning for ECG Classification. Scientific Reports, 11 (5251). ISSN 2045-2322

Wu, Ho and Noé, Frank (2019) Variational approach for learning Markov processes from time series data. Journal of Nonlinear Science, 30 . pp. 23-66. ISSN 1432-1467 (online)