A survey of link prediction in complex networks
Networks have become increasingly important to model complex systems composed of
interacting elements. Network data mining has a large number of applications in many …
interacting elements. Network data mining has a large number of applications in many …
Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries
The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Develo** new drug molecules to overcome …
development as intractable and hot research. Develo** new drug molecules to overcome …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Labeling trick: A theory of using graph neural networks for multi-node representation learning
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node
representation learning (where we are interested in learning a representation for a set of …
representation learning (where we are interested in learning a representation for a set of …
An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and …
Previous major earthquake events have revealed that soils susceptible to liquefaction are
one of the factors causing significant damages to the structures. Therefore, accurate …
one of the factors causing significant damages to the structures. Therefore, accurate …
[PDF][PDF] On dyadic fairness: Exploring and mitigating bias in graph connections
Disparate impact has raised serious concerns in machine learning applications and its
societal impacts. In response to the need of mitigating discrimination, fairness has been …
societal impacts. In response to the need of mitigating discrimination, fairness has been …
LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Protein-protein interactions (PPIs) play an important role in cell life activities such as
transcriptional regulation, signal transduction and drug signal transduction. The study of …
transcriptional regulation, signal transduction and drug signal transduction. The study of …
Random forest for bioinformatics
Y Qi - Ensemble machine learning: Methods and applications, 2012 - Springer
Modern biology has experienced an increased use of machine learning techniques for large
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
C Strobl, J Malley, G Tutz - Psychological methods, 2009 - psycnet.apa.org
Recursive partitioning methods have become popular and widely used tools for
nonparametric regression and classification in many scientific fields. Especially random …
nonparametric regression and classification in many scientific fields. Especially random …
Bias in random forest variable importance measures: Illustrations, sources and a solution
Background Variable importance measures for random forests have been receiving
increased attention as a means of variable selection in many classification tasks in …
increased attention as a means of variable selection in many classification tasks in …