Journal ArticleDOI
The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions
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TLDR
The study shows that the LPLNP model based on the known lncRNA–protein interactions can produce high-accuracy performances, and can be validated, indicating that the method is a useful tool for lnc RNA–protein interaction prediction.About:
This article is published in Neurocomputing.The article was published on 2018-01-17. It has received 114 citations till now.read more
Citations
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Predicting drug-disease associations by using similarity constrained matrix factorization
TL;DR: A user-friendly web server is developed by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD, which makes use of known drug-disease associations, drug features and disease semantic information.
Journal ArticleDOI
M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
TL;DR: A novel machine learning-based predictor called M6APred-EL, expected to be a practical and effective tool for the investigation of m6A functional mechanisms, is developed and compared with other state-of-the-art methods of benchmarking datasets.
Journal ArticleDOI
SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions
TL;DR: The sequence-based feature projection ensemble learning method, “SFPEL-LPI”, is proposed, which accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods.
Journal ArticleDOI
SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions
TL;DR: A sparse feature learning ensemble method with linear neighborhood regularization, abbreviated as SFLLN, to predict drug–drug interactions that can find novel interactions, which are validated by public evidence and produces high accuracy and outperforms benchmark methods.
Journal ArticleDOI
PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method.
TL;DR: A stacked ensemble model PredT4SE-Stack was developed to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system.
References
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Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Proceedings ArticleDOI
The relationship between Precision-Recall and ROC curves
Jesse Davis,Mark Goadrich +1 more
TL;DR: It is shown that a deep connection exists between ROC space and PR space, such that a curve dominates in R OC space if and only if it dominates in PR space.
Book
Support Vector Machines
TL;DR: This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications and provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature.