AI Medical Compendium Topic:
Semantics

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Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain.

Computer methods and programs in biomedicine
OBJECTIVE AND BACKGROUND: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructu...

Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning.

IEEE transactions on visualization and computer graphics
We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable. Our approach is based on Markov Decision Process (MDP). In the camera viewfinder or live preview mode, given the current frame, our...

A convolutional route to abbreviation disambiguation in clinical text.

Journal of biomedical informatics
OBJECTIVE: Abbreviations sense disambiguation is a special case of word sense disambiguation. Machine learning methods based on neural networks showed promising results for word sense disambiguation (Festag and Spreckelsen, 2017) [1] and, here we ass...

Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes.

Scientific reports
There exists a plethora of measures to evaluate functional similarity (FS) between genes, which is a widely used in many bioinformatics applications including detecting molecular pathways, identifying co-expressed genes, predicting protein-protein in...

Automatic extraction of gene-disease associations from literature using joint ensemble learning.

PloS one
A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathwa...

Biomedical literature classification with a CNNs-based hybrid learning network.

PloS one
Deep learning techniques, e.g., Convolutional Neural Networks (CNNs), have been explosively applied to the research in the fields of information retrieval and natural language processing. However, few research efforts have addressed semantic indexing...

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.

BMC medical informatics and decision making
BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organi...

Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.

BMC medical informatics and decision making
BACKGROUND: In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretab...

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and ...