A New Machine Learning Approach for Protein Phosphorylation Site Prediction in Plants

2009 ◽  
pp. 18-29 ◽  
Author(s):  
Jianjiong Gao ◽  
Ganesh Kumar Agrawal ◽  
Jay J. Thelen ◽  
Zoran Obradovic ◽  
A. Keith Dunker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Phosphorylation ◽  
Phosphorylation Site ◽  
Learning Approach ◽  
Site Prediction ◽  
Machine Learning Approach ◽  
New Machine

Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach

2010 ◽  
Vol 50 (5) ◽  
pp. 716-731 ◽  
Author(s):  
Shivani Agarwal ◽  
Deepak Dugar ◽  
Shiladitya Sengupta
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Approach ◽  
Chemical Structures ◽  
Machine Learning Approach ◽  
New Machine

scholarly journals Supplementary material to "OPTiMAL: A new machine learning approach for GDGT-based palaeothermometry"

2019 ◽  
Author(s):  
Yvette L. Eley ◽  
William Thompson ◽  
Sarah E. Greene ◽  
Ilya Mandel ◽  
Kirsty Edgar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Supplementary Material ◽  
New Machine

scholarly journals Low Amplitude Burst Detection of Catecholamines

2021 ◽  
Author(s):  
Amnah Eltahir ◽  
Jason White ◽  
Terry Lohrenz ◽  
P. Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Abstract Machine learning advances in electrochemical detection have recently produced subsecond and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to subsecond, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

scholarly journals OPTIMAL: A NEW MACHINE LEARNING APPROACH FOR GDGT-BASED PALAEOTHERMOMETRY

2019 ◽  
Author(s):  
Yvette Eley ◽  
◽  
William Thomson ◽  
Sarah E. Greene ◽  
Ilya Mandel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
New Machine

scholarly journals Low amplitude burst detection of catecholamines

2021 ◽  
Author(s):  
Amnah M Eltahir ◽  
Jason White ◽  
Terry M Lohrenz ◽  
Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Machine learning advances in electrochemical detection have recently produced sub- second and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to sub- second, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

scholarly journals A New Machine‐Learning Approach for Classifying Hysteresis in Suspended‐Sediment Discharge Relationships Using High‐Frequency Monitoring Data

2018 ◽  
Vol 54 (6) ◽  
pp. 4040-4058 ◽  
Author(s):  
Scott D. Hamshaw ◽  
Mandar M. Dewoolkar ◽  
Andrew W. Schroth ◽  
Beverley C. Wemple ◽  
Donna M. Rizzo
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
High Frequency ◽  
Monitoring Data ◽  
Learning Approach ◽  
Sediment Discharge ◽  
Machine Learning Approach ◽  
Frequency Monitoring ◽  
New Machine
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