Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Autor: Dea Gogishvili, Eleonora M. Vromen, Sascha Koppes-den Hertog, Afina W. Lemstra, Yolande A. L. Pijnenburg, Pieter Jelle Visser, Betty M. Tijms, Marta Del Campo, Sanne Abeln, Charlotte E. Teunissen, Lisa Vermunt

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)

Abstract Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CS

Externí odkaz: https://doaj.org/article/df9cdcb95df74b00868389130e51501d

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

Autor: Ekaterina Mavrina, Leighann Kimble, Katharina Waury, Dea Gogishvili, Nerea Gómez de San José, Shreyasee Das, Salomé Coppens, Bárbara Fernandes Gomes, Sára Mravinacová, Anna Lidia Wojdała, Katharina Bolsewig, Sherif Bayoumy, Felicia Burtscher, Pablo Mohaupt, Eline Willemse, Charlotte Teunissen, the MIRIADE consortium

Publikováno v: Frontiers in Neurology, Vol 13 (2022)

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to ac

Externí odkaz: https://doaj.org/article/6739cd827fa54f0a95864a402184d3e9

Ten quick tips for sequence-based prediction of protein properties using machine learning

Autor: Katharina Waury, Anton Feenstra, Qingzhen Hou, Dea Gogishvili

Publikováno v: Hou, Q, Waury, K, Gogishvili, D & Feenstra, K A 2022, ' Ten quick tips for sequence-based prediction of protein properties using machine learning ', PLoS Computational Biology, vol. 18, no. 12, e1010669, pp. 1-15 . https://doi.org/10.1371/journal.pcbi.1010669
PLoS Computational Biology, 18(12):e1010669, 1-15. Public Library of Science

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa4794c05d1a210f7cb458d01ec53110
https://doi.org/10.1371/journal.pcbi.1010669

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Autor: Dea Gogishvili, Ellen Vromen, Sascha Koppes, Afina W Lemstra, Yolande Pijnenburg, Pieter Jelle Visser, Betty N Tijms, Marta del Campo, The Alzheimer's Disease Neuroimaging Initiative, Sanne Abeln, Charlotte E Teunissen, Lisa Vermunt

Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques to identify cerebrospinal fluid (CSF) biomarkers that pre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6a64be64681054b573196d385232f2d3
https://doi.org/10.21203/rs.3.rs-2382373/v1

Nonadditivity in public and inhouse data: implications for drug design

Autor: Christian Margreitter, Eva Nittinger, Christian Tyrchan, Dea Gogishvili

Publikováno v: Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-18 (2021)
Journal of Cheminformatics, 13(1):47, 1-18. Chemistry Central
Journal of Cheminformatics
Gogishvili, D, Nittinger, E, Margreitter, C & Tyrchan, C 2021, ' Nonadditivity in public and inhouse data : implications for drug design ', Journal of Cheminformatics, vol. 13, no. 1, 47, pp. 1-18 . https://doi.org/10.1186/s13321-021-00525-z

Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4845b02556d332b7affe1ea8b86438f
https://doaj.org/article/17641e4b847843349772997d01307d1d