GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction.

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems. Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic patterns of traffic flow with a single temporal learning approach, which relies heavily on constructing graphs and has low training efficiency. Different from existing studies, this paper proposes a spatio-temporal ensemble network that aims to leverage the strengths of different sequential capturing approaches to obtain the intrinsic dependencies of traffic flow.

A Post-Hoc Interpretable Ensemble Model to Feature Effect Analysis in Warfarin Dose Prediction for Chinese Patients.

To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set.

DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction.

Background: Vitamin K antagonist (warfarin) is the most classical and widely used oral anticoagulant with assuring anticoagulant effect, wide clinical indications and low price. Warfarin dosage requirements of different patients vary largely. For warfarin daily dosage prediction, the data imbalance in dataset leads to inaccurate prediction on the patients of rare genotype, who usually have large stable dosage requirement.

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients.

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and to build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing.

Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese.

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and the genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared base models through a technique called "bagging.

Rat induced pluripotent stem cells protect H9C2 cells from cellular senescence via a paracrine mechanism.

Objectives: Cellular senescence may play an important role in the pathology of heart aging. We aimed to explore whether induced pluripotent stem cells (iPSCs) could inhibit cardiac cellular senescence via a paracrine mechanism.

Methods: We collected iPSC culture supernatant, with or without oxidative stress, as conditioned medium (CM) for the rat cardiomyocyte-derived cell line H9C2.