Yi Xiong

TL;DR: The experimental results demonstrate that the proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods, such as DDR.

TL;DR: GOLabeler is proposed, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a paradigm of machine learning, especially powerful for multilabel classification.

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.

TL;DR: A new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine.

TL;DR: A new model is proposed, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus, which is more accurate and stable than the previously published models.