OBJECTIVES: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.
Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progression, but EHR data is challenging to use for diagnosis prediction due to its high dimensionality, relative scarcity, and substantial level of noise. We ...
BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting k...
Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurat...
OBJECTIVE: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-dista...
The aim of the present study is to develop effective and understandable classification models for sex estimation and to identify the most dimorphic linear measurements in adult crania by means of data mining techniques. Furthermore, machine learning ...
Clinical pharmacology and therapeutics
Aug 1, 2020
The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive a...
PURPOSE: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could he...
In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this informat...
BACKGROUND: The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to en...
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