Recent news
Our partners Emmanuelle Rial-Sebbag and Delphine Azéma from Inserm discuss Ethical Legal and Social Aspects of Personalised Medicine in the article "Patients’ Expectations to Personalised Medicine – It Requires Ethical Consideration" by Pernille Tranberg in dataethics.eu.
More details in the article "Ethics of Personalised Medicine: Importance of the Multidisciplinary Approach in KidneySign Project" is available in Proteomics .
Publications
Selected publications
Publications from KidneySign partners relevant to the project (presented in alphabetical order):
| Argiles A, Siwy J, Duranton F, et al. CKD273, a new proteomics classifier assessing CKD and its prognosis. PLoS One. 2013;8(5):e62837. doi:10.1371/journal.pone.0062837 |
| Bae J, Helldin T, Riveiro M, Nowaczyk S, Bouguelia MR, Falkman G. Interactive Clustering: A Comprehensive Review. ACM Comput Surv. 2020;53(1):1-39. doi:10.1145/3340960 |
| Boor P, Ostendorf T, Floege J. Renal fibrosis: novel insights into mechanisms and therapeutic targets. Nat Rev Nephrol. 2010;6(11):643-656. doi:10.1038/nrneph.2010.120 |
| Bouteldja N, Klinkhammer BM, Bülow RD, et al. Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology. J Am Soc Nephrol. 2021;32(1):52-68. doi:10.1681/ASN.2020050597 |
| Brey P, Macnish K, Ryan M. Guidelines for the Ethical Development of AI and Big Data Systems: An Ethics by Design approach. SHERPA. Published online 2020. doi:10.21253/DMU.12301322.V1 |
| Catanese L, Siwy J, Mavrogeorgis E, et al. A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease. Proteomes. 2021;9(3):32. doi:10.3390/proteomes9030032 |
| Decramer S, Wittke S, Mischak H, et al. Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis. Nat Med. 2006;12(4):398-400. doi:10.1038/nm1384 |
| Delfin-Rossaro A, Chassang G, Cambon-Thomsen A, et al. Ethics and Data Access (F+) Training module (FAIRplus project). https://rise.articulate.com/share/GGh_EjcuIvnbO23NvBu73yEjDio2_7Yb#/ |
| Gajjala PR, Bruck H, Noels H, et al. Novel plasma peptide markers involved in the pathology of CKD identified using mass spectrometric approach. J Mol Med. 2019;97(10):1451-1463. doi:10.1007/s00109-019-01823-8 |
| Glorieux G, Mullen W, Duranton F, et al. New insights in molecular mechanisms involved in chronic kidney disease using high-resolution plasma proteome analysis. Nephrol Dial Transplant. 2015;30(11):1842-1852. doi:10.1093/ndt/gfv254 |
| Hermann J, Brehmer K, Jankowski V, et al. Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling. Proteomics Clin Appl. 2021;15(1):e1900143. doi:10.1002/prca.201900143 |
| Hermann J, Noels H, Theelen W, et al. Sample preparation of formalin-fixed paraffin-embedded tissue sections for MALDI-mass spectrometry imaging. Anal Bioanal Chem. 2020;412(6):1263-1275. doi:10.1007/s00216-019-02296-x |
| Jankowski V, Saritas T, Kjolby M, et al. Carbamylated sortilin associates with cardiovascular calcification in patients with chronic kidney disease. Kidney International. Published online November 2021:S0085-2538(21)01034-6. doi:10.1016/j.kint.2021.10.018 |
| Kaye J, Terry SF, Juengst E, et al. Including all voices in international data-sharing governance. Hum Genomics. 2018;12(1):13. doi:10.1186/s40246-018-0143-9 |
| Kerschbaum J, Rudnicki M, Dzien A, et al. Intra-individual variability of eGFR trajectories in early diabetic kidney disease and lack of performance of prognostic biomarkers. Sci Rep. 2020;10(1):19743. doi:10.1038/s41598-020-76773-0 |
| Klein J, Caubet C, Camus M, et al. Connectivity mapping of glomerular proteins identifies dimethylaminoparthenolide as a new inhibitor of diabetic kidney disease. Sci Rep. 2020;10(1):14898. doi:10.1038/s41598-020-71950-7 |
| Laget J, Duranton F, Argilés À, Gayrard N. Renal insufficiency and chronic kidney disease – Promotor or consequence of pathological post-translational modifications. Molecular Aspects of Medicine. Published online February 2022:101082. doi:10.1016/j.mam.2022.101082 |
| Lycke M, Ulfenborg B, Kristjansdottir B, Sundfeldt K. Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers. J Clin Med. 2020;9(2). doi:10.3390/jcm9020299 |
| Magalhães P, Pejchinovski M, Markoska K, et al. Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies? Sci Rep. 2017;7(1):16915. doi:10.1038/s41598-017-17083-w |
| Marcišauskas S, Ulfenborg B, Kristjansdottir B, Waldemarson S, Sundfeldt K. Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2. J Proteomics. 2019;196:57-68. doi:10.1016/j.jprot.2019.01.017 |
| Pena MJ, Jankowski J, Heinze G, et al. Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes. J Hypertens. 2015;33(10):2123-2132. doi:10.1097/HJH.0000000000000685 |
| Perco P, Pena M, Heerspink HJL, Mayer G, BEAt-DKD Consortium. Multimarker Panels in Diabetic Kidney Disease: The Way to Improved Clinical Trial Design and Clinical Practice? Kidney Int Rep. 2019;4(2):212-221. doi:10.1016/j.ekir.2018.12.001 |
| Prischl FC, Rossing P, Bakris G, Mayer G, Wanner C. Major adverse renal events (MARE): a proposal to unify renal endpoints. Nephrology Dialysis Transplantation. 2021;36(3):491-497. doi:10.1093/ndt/gfz212 |
| Rial-Sebbag E. [Chapter 4. Governing Big Data for Health, national and international issues]. J Int Bioethique Ethique Sci. 2017;28(3):39-50. doi:10.3917/jib.283.0039 |
| Schanstra JP, Zürbig P, Alkhalaf A, et al. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides. J Am Soc Nephrol. 2015;26(8):1999-2010. doi:10.1681/ASN.2014050423 |
| Sommer J, Seeling A, Rupprecht H. Adverse Drug Events in Patients with Chronic Kidney Disease Associated with Multiple Drug Interactions and Polypharmacy. Drugs Aging. 2020;37(5):359-372. doi:10.1007/s40266-020-00747-0 |
| Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Briefings in Bioinformatics. Published online January 28, 2022:bbab569. doi:10.1093/bib/bbab569 |
| Ulfenborg B, Karlsson A, Riveiro M, Andersson CX, Sartipy P, Synnergren J. Multi-assignment clustering: Machine learning from a biological perspective. J Biotechnol. 2021;326:1-10. doi:10.1016/j.jbiotec.2020.12.002 |
| Ulfenborg B, Karlsson A, Riveiro M, et al. A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells. PLoS One. 2017;12(6):e0179613. doi:10.1371/journal.pone.0179613 |
| Ulfenborg B. nolas. GitLab. 2021;nolas:https://gitlab.com/algoromics/nolas. |
| Ulfenborg B. Vertical and horizontal integration of multi-omics data with miodin. BMC Bioinformatics. 2019;20(1):649. doi:10.1186/s12859-019-3224-4 |
| van der Wouden CH, Böhringer S, Cecchin E, et al. Generating evidence for precision medicine: considerations made by the Ubiquitous Pharmacogenomics Consortium when designing and operationalizing the PREPARE study. Pharmacogenet Genomics. 2020;30(6):131-144. doi:10.1097/FPC.0000000000000405 |
| Vlahou A, Hallinan D, Apweiler R, et al. Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics? Hypertension. Published online February 15, 2021. doi:10.1161/HYPERTENSIONAHA.120.16340 |