403 results on '"Krauthammer, Michael"'
Search Results
2. Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
3. Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes
4. Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data
5. Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
6. Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
7. Attention-based Multi-task Learning for Base Editor Outcome Prediction
8. Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
9. Boosting Radiology Report Generation by Infusing Comparison Prior
10. SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
11. Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
12. DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
13. Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort
14. Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy
15. Predicting prime editing efficiency and product purity by deep learning
16. Progressive Transformer-Based Generation of Radiology Reports
17. AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions
18. Patient Similarity Analysis with Longitudinal Health Data
19. AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
20. The Association of MUC16 Mutation with Tumor Mutation Burden and Its Prognostic Implications in Cutaneous Melanoma.
21. Neural networks versus Logistic regression for 30 days all-cause readmission prediction
22. Innate acting memory Th1 cells modulate heterologous diseases
23. Live slow-frozen human tumor tissues viable for 2D, 3D, ex vivo cultures and single-cell RNAseq
24. Simple Contrastive Representation Learning for Time Series Forecasting
25. Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study
26. Iterations for Propensity Score Matching in MonetDB
27. Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study
28. Fragmentstein—facilitating data reuse for cell-free DNA fragment analysis
29. Publishing without Publishers: a Decentralized Approach to Dissemination, Retrieval, and Archiving of Data
30. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases.
31. Fragmentstein—Facilitating data reuse for cell-free DNA fragment analysis
32. The state of melanoma: challenges and opportunities.
33. Global copy number profiling of cancer genomes
34. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases
35. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
36. Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning
37. Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey
38. Iterations for Propensity Score Matching in MonetDB
39. Spitz nevi and Spitzoid melanomas: exome sequencing and comparison with conventional melanocytic nevi and melanomas
40. AutoDiscern: rating the quality of online health information with hierarchical encoder attention-based neural networks
41. Publishing Without Publishers: A Decentralized Approach to Dissemination, Retrieval, and Archiving of Data
42. Suppl. Figure S12 from ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma
43. Suppl.Figure S16 from ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma
44. Table ST9 from ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma
45. Supplementary Data from ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma
46. Supplementary Tables 1-3 from Phosphoproteomic Screen Identifies Potential Therapeutic Targets in Melanoma
47. Supplementary Figure 1 from Phosphoproteomic Screen Identifies Potential Therapeutic Targets in Melanoma
48. Supplementary Table 4 from Phosphoproteomic Screen Identifies Potential Therapeutic Targets in Melanoma
49. Supplementary Table 5 from Phosphoproteomic Screen Identifies Potential Therapeutic Targets in Melanoma
50. Data from Phosphoproteomic Screen Identifies Potential Therapeutic Targets in Melanoma
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