Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeed...
Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while eva...
Database : the journal of biological databases and curation
Feb 16, 2021
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders'...
The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: 'model organisms' chosen for their phylogenetic/aetiological similarities; multivariable...
The problem of the theoretical precariousness of psychology requires defining, at an epistemological level, its concepts and languages and the use of models for finding core concepts and building more or less 'hard' theories. After reviewing some mai...
Studies in health technology and informatics
Aug 21, 2019
A diagnostic model of general diseases could help general practitioners to decrease misdiagnoses and reduce workload. In this paper, we developed a neural network model that can classify potential diagnoses among 100 selected common diseases based on...
Studies in health technology and informatics
Aug 21, 2019
The evaluation of the validity of a knowledge-based system is of great importance during its development. It determines whether the system represents the experts' knowledge correctly. This is highly important, but also particularly difficult, if the ...
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