Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal\n Graphs
J Zhang, X Shi, J Xie, H Ma, I King, DY Yeung. Cited by 316
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How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach
H Hu, Y Luo, Y Wen, YS Ong, X Zhang. Cited by 6
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Additional file 6: of CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, PL ̨, C Angione, S Pucciarelli
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Additional file 5: of CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, PL ̨, C Angione, S Pucciarelli
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Additional file 4: of CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, PL ̨, C Angione, S Pucciarelli
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Additional file 7: of CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, PL ̨, C Angione, S Pucciarelli
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Additional file 3: of CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, PL ̨, C Angione, S Pucciarelli
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PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images
A Datta, S Ghosh, AK Ghosh. Cited by 51
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Traffic Prediction Based Power Saving in Cellular Networks
S Zhang, S Zhao, M Yuan, J Zeng, J Yao, MRT Lyu, I King. Cited by 43
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Multilabel Prediction via Cross-View Search
X Shen, W Liu, IWH Tsang, Q Sun, YS Ong. Cited by 72
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Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data
Y Chen, D Lu, L Luo, YN POKHREL, K Deb, J Huang, Y Ran. Cited by 135
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Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments
S Vijayakumar, C Angione