Journal ArticleDOI
RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features.
TLDR
Based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the n2-methylguanosine modification sites for three species.About:
This article is published in Methods.The article was published on 2021-05-24. It has received 18 citations till now. The article focuses on the topics: Random forest & Computer science.read more
Citations
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Journal ArticleDOI
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique
TL;DR: In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii.
Journal ArticleDOI
Biological Sequence Classification: A Review on Data and General Methods
TL;DR: There are many branches of biological sequence classification research as mentioned in this paper , including function and modification classification of biological sequences based on machine learning, which is the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides.
Journal ArticleDOI
Bitter-RF: A random forest machine model for recognizing bitter peptides
TL;DR: In this paper , a Random Forest (RF)-based model, called Bitter-RF, was developed for identifying bitter peptides. But, the model was not used to build a prediction model for the peptide.
Journal ArticleDOI
Analysis and modeling of myopia-related factors based on questionnaire survey
Jianqiang Xiao,Mujiexin Liu,Qinlai Huang,Zijie Sun,Lin Ning,Junguo Duan,Siquan Q. Zhu,Jian-Zhong Huang,Hao Lin,Hui Yang +9 more
TL;DR: Wang et al. as discussed by the authors investigated the relationship between four main factors (environment, habits, parental vision, and demographic) and myopia status by analyzing the questionnaire data, and found that the 4 most influential features with XGBoost could achieve a competitive AUC of 0.764.
Journal ArticleDOI
DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins
TL;DR: In this article, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins, which combines the three features of monoDiKGap (k=2), cross-covariance, and grouped amino acid composition.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal ArticleDOI
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.