An introductory review of deep learning for prediction models with big data
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and
machine learning. Recent breakthrough results in image analysis and speech recognition …
machine learning. Recent breakthrough results in image analysis and speech recognition …
Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Abstract In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is
converted into a control signal through processing a specific pattern in brain signals …
converted into a control signal through processing a specific pattern in brain signals …
Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
Named entity recognition and relation detection for biomedical information extraction
The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
Inter-sentence relation extraction with document-level graph convolutional neural network
Inter-sentence relation extraction deals with a number of complex semantic relationships in
documents, which require local, non-local, syntactic and semantic dependencies. Existing …
documents, which require local, non-local, syntactic and semantic dependencies. Existing …
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
E Pérez Santín, R Rodríguez Solana… - Wiley …, 2021 - Wiley Online Library
The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Motivation Adverse events resulting from drug-drug interactions (DDI) pose a serious health
issue. The ability to automatically extract DDIs described in the biomedical literature could …
issue. The ability to automatically extract DDIs described in the biomedical literature could …