ArticleOncotarget2016
Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Integrated LC-MS/MS profiling, network pharmacology, molecular docking, and enzyme inhibition assays reveal the antidiabetic potential ofToxicology reports · 2026Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we proposed an improved algorithm for identifying proteins relevant to cancer. The algorithm was named two-layer molecular similarity ensemble approach (TL-SEA). We applied TL-SEA to analyzing the correlation between anticancer compounds (against cell lines K562, MCF7 and A549) and active compounds against separate target proteins listed in BindingDB. Several associations between cancer types and related proteins were revealed using this chemoinformatics approach. An analysis of the literature showed that 26 of 35 predicted proteins were correlated with cancer cell proliferation, apoptosis or differentiation. Additionally, interactions between proteins in BindingDB and anticancer chemicals were also predicted. We discuss the roles of the most important predicted proteins in cancer biology and conclude that TL-SEA could be a useful tool for inferring novel proteins involved in cancer and revealing underlying molecular mechanisms.
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Registered trials
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