New statistical algorithms for clinical proteomics. - Tim Conrad 001 (Computational Proteomics, 2005)

Beating the Noise: New Statistical Methods for Detecting Signals in MALDI-TOF Spectra Below Noise Level. - Tim O. F. Conrad; Alexander B. Leichtle; Andre Hagehülsmann; Elmar Diederichs; Sven Baumann; Joachim Thiery; Christof Schütte (CompLife, 2006)

New statistical algorithms for the analysis of mass spectrometry time-of- flight mass data with applications in clinical diagnostics (Neue statistische Algorithmen zur Analyse von Massenspektrometrie Time-Of- Flight Massendaten mit Anwendungen in der klinischen Diagnostik) - Tim Conrad 001 (2008)

Finding Modules in Networks with Non-modular Regions. - Sharon Bruckner; Bastian Kayser; Tim O. F. Conrad (SEA, 2013)

Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining. - Fahrnaz Jayrannejad; Tim O. F. Conrad (BIRS-IMLKE, 2015)

Minimum-overlap Clusterings and the Sparsity of Overcomplete Decompositions of Binary Matrices. - Víctor Mireles; Tim O. F. Conrad (ICCS, 2015)

Epithelial-Mesenchymal Transition Regulatory Network-Based Feature Selection in Lung Cancer Prognosis Prediction. - Borong Shao; Tim O. F. Conrad (IWBBIO, 2016)

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. - Tim O. F. Conrad; Martin Genzel; Nada Cvetkovic; Niklas Wulkow; Alexander B. Leichtle; Jan Vybíral; Gitta Kutyniok; Christof Schütte (BMC Bioinform., 2017)

Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture. - Christian Knauth; Boran Adas; Daniel Whitfield; Xuesong Wang; Lydia Ickler; Tim Conrad 001; Oliver Serang (CoRR, 2017)

Deep Learning for Proteomics Data for Feature Selection and Classification. - Sahar Iravani; Tim O. F. Conrad (CD-MAKE, 2019)

Convergent discretisation schemes for transition path theory for diffusion processes. - Nada Cvetkovic; Tim O. F. Conrad; Han Cheng Lie (CoRR, 2019)

Learning Chemical Reaction Networks from Trajectory Data. - Wei Zhang 065; Stefan Klus; Tim O. F. Conrad; Christof Schütte (SIAM J. Appl. Dyn. Syst., 2019)

GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. - Kateryna Melnyk; Stefan Klus; Grégoire Montavon; Tim O. F. Conrad (Appl. Netw. Sci., 2020)

Dictionary learning for transcriptomics data reveals type-specific gene modules in a multi-class setting. - Mona Rams; Tim Conrad 001 (it Inf. Technol., 2020)

A Convergent Discretization Method for Transition Path Theory for Diffusion Processes. - Nada Cvetkovic; Tim Conrad 001; Han Cheng Lie (Multiscale Model. Simul., 2021)

Understanding microbiome dynamics via interpretable graph representation learning. - Kateryna Melnyk; Kuba Weimann; Tim O. F. Conrad (CoRR, 2022)

Forget Embedding Layers: Representation Learning for Cold-start in Recommender Systems. - Kuba Weimann; Tim O. F. Conrad (CoRR, 2022)

Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks. - Kuba Weimann; Tim Conrad 001 (CinC, 2023)

Bravo MaRDI: A Wikibase Knowledge Graph on Mathematics. - Moritz Schubotz; Eloi Ferrer; Johannes Stegmüller; Daniel Mietchen; Olaf Teschke; Larissa Pusch; Tim Conrad 001 (Wikidata@ISWC, 2023)

Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification. - Amnon Bleich; Antje Linnemann; Benjamin Jaidi; Björn H. Diem; Tim O. F. Conrad (CoRR, 2023)

Bravo MaRDI: A Wikibase Powered Knowledge Graph on Mathematics. - Moritz Schubotz; Eloi Ferrer; Johannes Stegmüller; Daniel Mietchen; Olaf Teschke; Larissa Pusch; Tim O. F. Conrad (CoRR, 2023)

Making Mathematical Research Data FAIR: A Technology Overview. - Tim Conrad 001; Eloi Ferrer; Daniel Mietchen; Larissa Pusch; Johannes Stegmüller; Moritz Schubotz (CoRR, 2023)

Neural parameter calibration and uncertainty quantification for epidemic forecasting. - Thomas Gaskin; Tim Conrad 001; Grigorios A. Pavliotis; Christof Schütte (CoRR, 2023)

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data. - Sahar Iravani; Tim O. F. Conrad (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA. - Erhun Giray Tuncay; Riza Cenk Erdur; Tim Conrad 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification. - Amnon Bleich; Antje Linnemann; Benjamin Jaidi; Björn H. Diem; Tim O. F. Conrad (Mach. Learn. Knowl. Extr., 2023)

Vahana.jl - A framework (not only) for large-scale agent-based models. - Steffen Fürst; Tim Conrad 001; Carlo Jaeger; Sarah Wolf (CoRR, 2024)

Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering. - Larissa Pusch; Tim O. F. Conrad (CoRR, 2024)

Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models. - Sahar Iravani; Tim O. F. Conrad (CoRR, 2024)

Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning. - Amnon Bleich; Antje Linnemann; Björn H. Diem; Tim O. F. Conrad (CoRR, 2024)

Clustering Time-Snapshots of Temporal Networks: From Synthetic Data to Real-World Applications. - Filip Blaskovic; Tim O. F. Conrad; Stefan Klus; Natasa Djurdjevac Conrad (CoRR, 2024)

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification. - Kuba Weimann; Tim O. F. Conrad (IEEE J. Biomed. Health Informatics, 2024)

Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance. - Kuba Weimann; Tim O. F. Conrad (Comput. Biol. Medicine, 2025)

Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach. - Inan Bostanci; Tim Conrad 001 (J. Artif. Soc. Soc. Simul., 2025)

A Hybrid ABM-PDE Framework for Real-World Infectious Disease Simulations. - Kristina Maier; Tim O. F. Conrad (CoRR, 2025)

FELRec: efficient handling of item cold-start with dynamic representation learning in recommender systems. - Kuba Weimann; Tim O. F. Conrad (Int. J. Data Sci. Anal., 2025)