Building a new machine learning-based model to estimate county-level climatic yield variation for maize in Northeast China

2021 ◽  
Vol 191 ◽  
pp. 106557
Author(s):  
Manyao Li ◽  
Jin Zhao ◽  
Xiaoguang Yang
Keyword(s):  
Machine Learning ◽  
Northeast China ◽  
County Level ◽  
Yield Variation ◽  
New Machine

Quantifying the impacts of pre-occurred ENSO signals on wheat yield variation using machine learning in Australia

2020 ◽  
Vol 291 ◽  
pp. 108043 ◽  
Author(s):  
Bin Wang ◽  
Puyu Feng ◽  
Cathy Waters ◽  
James Cleverly ◽  
De Li Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wheat Yield ◽  
Yield Variation

First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

2021 ◽  
Author(s):  
Mohammad Zafari ◽  
Arun S. Nissimagoudar ◽  
Muhammad Umer ◽  
Geunsik Lee ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
First Principles ◽  
Conjugated Polymer ◽  
2D Materials ◽  
Single Atom ◽  
Vacancy Defects ◽  
Catalytic Activities ◽  
Polymer Supported ◽  
New Machine

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

2021 ◽  
Author(s):  
Yashas Samaga B L ◽  
Shampa Raghunathan ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Protein Stability ◽  
Data Augmentation ◽  
Stability Prediction ◽  
Structural Environment ◽  
Self Consistent ◽  
The Difference ◽  
Protein Sequence Space ◽  
New Machine

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

scholarly journals A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation

2018 ◽  
Vol 9 ◽  
Author(s):  
Adam McDermaid ◽  
Xin Chen ◽  
Yiran Zhang ◽  
Cankun Wang ◽  
Shaopeng Gu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Uncertainty Analysis ◽  
Rna Seq ◽  
Read Alignment ◽  
New Machine

scholarly journals A New Machine Learning Framework for Air Combat Intelligent Virtual Opponent

2018 ◽  
Vol 1069 ◽  
pp. 012031 ◽  
Author(s):  
Xiang Lei ◽  
Anxiang Huang ◽  
Tao Zhao ◽  
Yuqiang Su ◽  
Chuan Ren
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Air Combat ◽  
New Machine

scholarly journals Mycofier: a new machine learning-based classifier for fungal ITS sequences

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Luisa Delgado-Serrano ◽  
Silvia Restrepo ◽  
Jose Ricardo Bustos ◽  
Maria Mercedes Zambrano ◽  
Juan Manuel Anzola
Keyword(s):  
Machine Learning ◽  
Its Sequences ◽  
Fungal Its ◽  
New Machine
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