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Part 1: Document Description
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Citation |
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Title: |
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data |
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Identification Number: |
doi:10.21979/N9/VLZWCL |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2017-09-19 |
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Version: |
1 |
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Bibliographic Citation: |
Zheng, Jie, 2017, "Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data", https://doi.org/10.21979/N9/VLZWCL, DR-NTU (Data), V1 |
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Citation |
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Title: |
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data |
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Identification Number: |
doi:10.21979/N9/VLZWCL |
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Authoring Entity: |
Zheng, Jie (Nanyang Technological University) |
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Software used in Production: |
(To be provided) |
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Grant Number: |
AcRF Tier 2 grant ARC 39/13 (MOE2013-T2-1-079) |
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Grant Number: |
No. 61672113 and No. 61272380 |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Zheng, Jie |
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Depositor: |
Cheng, Wei Yeow |
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Date of Deposit: |
2017-08-26 |
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Holdings Information: |
https://doi.org/10.21979/N9/VLZWCL |
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Study Scope |
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Keywords: |
Computer and Information Science, Computer and Information Science, Signaling pathways, Cellular signaling networks, Hybrid modeling |
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Abstract: |
<h3>About Knowledge-guided fuzzy logic network model</h3> <p>Knowledge-guided fuzzy logic network model is a new hybrid method to integrate the prior knowledge and data-driven learning for signaling pathway inference. It is applied to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that the method can model temporally-ordered experimental observations. </p> <h3>Download</h3> <p style="color:#6f2a11 ;">Three datasets (Synthetic dataset, DREAM4 dataset and Cell fate prediction dataset) that are used to evaluate the model are available for downloading in this dataset record. The datasets can also be downloaded at: <a href="https://github.com/hliu2016/fuzzy">Github</a>.</p> <p style="color:#6f2a11 ;">Supplementary material: <a href="http://www.ntu.edu.sg/home/zhengjie/software/fuzzySigNet/Supplementary.pdf">"Supplementary File: Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data"</a>.</p> |
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Kind of Data: |
Source code |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1038/srep35652 |
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Bibliographic Citation: |
Liu, H., Zhang, F., Mishra, S. K., Zhou, S., & Zheng, J. (2016). Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data. Scientific Reports, 6, 35652. |
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Citation |
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Identification Number: |
10356/155401 |
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Bibliographic Citation: |
Liu, H., Zhang, F., Mishra, S. K., Zhou, S., & Zheng, J. (2016). Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data. Scientific Reports, 6, 35652. |
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cdtw.m |
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application/octet-stream |
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cell_fate.mat |
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application/octet-stream |
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DREAM4.m |
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application/octet-stream |
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DREAM.mat |
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application/octet-stream |
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dtw.m |
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text/plain; charset=US-ASCII |
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normalize.m |
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application/octet-stream |
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objfun.m |
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application/octet-stream |
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objfun_parallel.m |
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application/octet-stream |
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objfun_parallel_ts.m |
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application/octet-stream |
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objfun_parallel_yaffe_ts.m |
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application/octet-stream |
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README.txt |
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text/plain |
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toy.m |
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application/octet-stream |
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ts_synthetic.mat |
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application/octet-stream |
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Yaffe.m |
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application/octet-stream |