Publications by authors named "Cecilia Jensen"
Article Synopsis
- Proteomics is the study of proteins, and it's getting better at finding different versions of proteins called isoforms.
- A challenge in this area is figuring out how to accurately identify these isoforms because there's a lot of data to sort through.
- New tools using artificial intelligence can help scientists search for and identify proteins more effectively, leading to more discoveries about how proteins work in living things.
Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB.
View Article and Find Full Text PDFDiet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks.
View Article and Find Full Text PDFIntroduction: The Integrated Rehabilitation Programme for Chronic Conditions project (SIKS) implemented rehabilitation programmes for people with four chronic conditions in the local area within the Municipality of Copenhagen.
Objectives: The objective of this study was to evaluate the impact of rehabilitation on health-care utilisation in chronic obstructive pulmonary disease (COPD) patients as a subgroup of SIKS.
Methods: For the analyses, data from Danish National Registers' were obtained.