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Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

Overview of attention for article published in Scientific Reports, January 2018
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Title
Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways
Published in
Scientific Reports, January 2018
DOI 10.1038/s41598-017-17842-9
Pubmed ID
Authors

Renchu Guan, Xu Wang, Mary Qu Yang, Yu Zhang, Fengfeng Zhou, Chen Yang, Yanchun Liang

Abstract

The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making it accessible, or biocuration, is an important aspect of biomedical research and discovery. However, because maintaining sophisticated biocuration is highly resource dependent, it continues to lag behind the continually being generated biomedical data. Another critical aspect of cancer research, pathway analysis, has proven to be an efficient method for gaining insight into the underlying biology associated with cancer. We propose a deep-learning-based model, Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL), for facilitating gene multi-function discovery and pathway completion. SdaMLL can capture intermediate representations robust to partial corruption of the input pattern and generate low-dimensional codes superior to conditional dimension reduction tools. Experimental results indicate that SdaMLL outperforms existing classical multi-label algorithms. Moreover, we found some gene functions, such as Fused in Sarcoma (FUS, which may be part of transcriptional misregulation in cancer) and p27 (which we expect will become a member viral carcinogenesis), that can be used to complete the related pathways. We provide a visual tool ( https://www.keaml.cn/gpvisual ) to view the new gene functions in cancer pathways.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 16%
Researcher 11 15%
Student > Bachelor 10 13%
Student > Master 8 11%
Other 4 5%
Other 13 17%
Unknown 17 23%
Readers by discipline Count As %
Computer Science 20 27%
Biochemistry, Genetics and Molecular Biology 9 12%
Medicine and Dentistry 9 12%
Agricultural and Biological Sciences 7 9%
Engineering 4 5%
Other 9 12%
Unknown 17 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2018.
All research outputs
#18,604,390
of 23,045,021 outputs
Outputs from Scientific Reports
#94,256
of 124,509 outputs
Outputs of similar age
#331,626
of 443,333 outputs
Outputs of similar age from Scientific Reports
#3,058
of 4,091 outputs
Altmetric has tracked 23,045,021 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 124,509 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 443,333 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,091 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.