no code implementations • 30 Sep 2022 • Xinxing Wu, Chong Peng, Richard Charnigo, Qiang Cheng
Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients.
no code implementations • 25 Aug 2022 • Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng
Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior.
1 code implementation • 25 Aug 2022 • Xinxing Wu, Chong Peng, Gregory Jicha, Donna Wilcock, Qiang Cheng
Then, we apply it to study oscillation patterns in untimed genome-wide gene expression from 19 human brain regions of controls and AD patients.
no code implementations • 1 Dec 2021 • Xinxing Wu, Chaoyue Tan, Gul Deniz Cayli, Peide Liu
In this study, we prove that the space of all IVIFNs with the relation in the method for comparing any two IVIFNs based on a score function and three types of entropy functions is a complete chain and obtain that this relation is an admissible order.
no code implementations • 17 Nov 2021 • Xinxing Wu, Tao Wang, Qian Liu, Peide Liu, Guanrong Chen, Xu Zhang
By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.
no code implementations • 4 Jun 2021 • Xinxing Wu, Qiang Cheng
Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of domain problems.
1 code implementation • 21 Mar 2021 • Xinxing Wu, Qiang Cheng
Graph neural networks have been used for a variety of learning tasks, such as link prediction, node classification, and node clustering.
1 code implementation • 19 Oct 2020 • Xinxing Wu, Qiang Cheng
In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE).
1 code implementation • NeurIPS 2021 • Xinxing Wu, Qiang Cheng
Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes.
no code implementations • 19 Jul 2016 • Xinxing Wu, Junping Zhang
Concentration inequalities are indispensable tools for studying the generalization capacity of learning models.