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Network representation learning: from preprocessing, feature extraction to node embedding
Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …
networks, knowledge graphs, and complex biomedical and physics information networks …
Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers
Computational drug repurposing aims to identify new indications for existing drugs by
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …
Subgraph neural networks
Deep learning methods for graphs achieve remarkable performance on many node-level
and graph-level prediction tasks. However, despite the proliferation of the methods and their …
and graph-level prediction tasks. However, despite the proliferation of the methods and their …
Knowledge-based biomedical data science
Knowledge-based biomedical data science involves the design and implementation of
computer systems that act as if they knew about biomedicine. Such systems depend on …
computer systems that act as if they knew about biomedicine. Such systems depend on …
A survey of the recent trends in deep learning for literature based discovery in the biomedical domain
Every day, enormous amounts of biomedical texts discussing various biomedical topics are
produced. Revealing strong semantic connections hidden in those unstructured data is …
produced. Revealing strong semantic connections hidden in those unstructured data is …
Large language model-based natural language encoding could be all you need for drug biomedical association prediction
Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of
drug discovery and development. While various artificial intelligence (AI)-based tools have …
drug discovery and development. While various artificial intelligence (AI)-based tools have …
IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier
R Zhu, Y Wang, JX Liu, LY Dai - BMC bioinformatics, 2021 - Springer
Background Identifying lncRNA-disease associations not only helps to better comprehend
the underlying mechanisms of various human diseases at the lncRNA level but also speeds …
the underlying mechanisms of various human diseases at the lncRNA level but also speeds …
[HTML][HTML] Understanding drug repurposing from the perspective of biomedical entities and their evolution: Bibliographic research using aspirin
Background Drug development is still a costly and time-consuming process with a low rate
of success. Drug repurposing (DR) has attracted significant attention because of its …
of success. Drug repurposing (DR) has attracted significant attention because of its …
DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification
The great number of online scientific publications on cancer research makes large scale
data mining possible. The hallmarks or characteristics of cancer can be used to distinguish …
data mining possible. The hallmarks or characteristics of cancer can be used to distinguish …
AGATHA: automatic graph mining and transformer based hypothesis generation approach
Medical research is risky and expensive. Drug discovery requires researchers to efficiently
winnow thousands of potential targets to a small candidate set. However, scientists spend …
winnow thousands of potential targets to a small candidate set. However, scientists spend …