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Natural Science
Submission
Natural Science
ISSN Print:
2150-4091
ISSN Online:
2150-4105
www.scirp.org/journal/ns
E-mail:
ns@scirp.org
Google-based Impact Factor:
0.74
Citations
h5
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"
Other Mountain Stones Can Attack Jade: The 5-Steps Rule
"
written by
Kuo-Chen Chou
,
published by
Natural Science
,
Vol.12 No.3, 2020
has been cited by the following article(s):
Google Scholar
CrossRef
[1]
DNA methylation prediction using reduced features obtained via Gappy Pair Kernel and Partial Least Square
IEEE …
,
2022
[2]
Lasso regression under stochastic restrictions in linear regression: An application to genomic data
Communications in Statistics-Theory and …
,
2022
[3]
The significant and profound impacts of Gordon life science institute
2021
[4]
The Remarkable impacts of Gordon life science institute
2021
[5]
iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features
2021
[6]
pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning
2020
[7]
Use Chou's 5-steps rule to predict remote homology proteins by merging grey incidence analysis and domain similarity analysis
2020
[8]
Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting
2020
[9]
pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins
2020
[10]
Using Chou's 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods
2020
[11]
pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning
2020
[12]
Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model
2020
[13]
pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning
2020
[14]
pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning
2020
[15]
iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning
2020
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