[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 …
Informatics and computational methods in natural product drug discovery: a review and perspectives
The discovery of new pharmaceutical drugs is one of the preeminent tasks—scientifically,
economically, and socially—in biomedical research. Advances in informatics and …
economically, and socially—in biomedical research. Advances in informatics and …
Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean
Recent substantial advances in high-throughput field phenoty** have provided plant
breeders with affordable and efficient tools for evaluating a large number of genotypes for …
breeders with affordable and efficient tools for evaluating a large number of genotypes for …
[PDF][PDF] Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013)
The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug
interactions that appear in biomedical literature. We propose two subtasks for the …
interactions that appear in biomedical literature. We propose two subtasks for the …
Drug‐drug interaction extraction via convolutional neural networks
Drug‐drug interaction (DDI) extraction as a typical relation extraction task in natural
language processing (NLP) has always attracted great attention. Most state‐of‐the‐art DDI …
language processing (NLP) has always attracted great attention. Most state‐of‐the‐art DDI …
[HTML][HTML] Drug-drug interaction extraction from biomedical texts using long short-term memory network
The simultaneous administration of multiple drugs increases the probability of interaction
among them, as one drug may affect the activities of others. This interaction among drugs …
among them, as one drug may affect the activities of others. This interaction among drugs …
Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network
Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network
Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health
safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has …
safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has …
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 …
Deep learning for drug–drug interaction extraction from the literature: a review
Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These
interactions may cause adverse drug effects that threaten public health and patient safety …
interactions may cause adverse drug effects that threaten public health and patient safety …