ArticleFrontiers in bioengineering and biotechnology2019
STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- SeEn: Sequential enriched datasets for sequence-aware recommendations.Scientific data · 2022Article
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- Hybrid semantic recommender system for chemical compounds in large-scale datasets.Journal of cheminformatics · 2021Article
- RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites.Frontiers in bioengineering and biotechnology · 2020Article
- Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification.BioMed research international · 2020Article
- T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.Frontiers in microbiology · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
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Abstract
Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an
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Registered trials
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