16 papers · ranked by Valyu relevance
Xiangzheng Fu, Li Peng, Haowen Chen, Mingqiang Rong + 4 more
T-cell receptor (TCR)-epitope binding prediction is critical for immunotherapies but remains challenged by sparse interaction networks and severe class imbalance in training data. Current graph neural network (GNN) approaches for predicting TCR-epitope binding (TEB) fail to address two key limitations: over-smoothing…
Zhuliu Li, Tianci Song, Jeongsik Yong, Rui Kuang + 1 more
High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the…
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao
Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of…
Huimin Luo, Hui Yang, Ge Zhang, Jianlin Wang + 2 more
'Chaokun Yan'] Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep…
Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio + 5 more
'Kristina Yim' 'Rebecca Muhle' 'James Noonan' 'Guy Wolf' 'Smita Krishnaswamy'] While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making…
Chengxin Xie, Xiumei Wen, Hui Pang, Bo Zhang + 1 more
Social networking has become a hot topic, in which recommendation algorithms are the most important. Recently, the combination of deep learning and recommendation algorithms has attracted considerable attention. The integration of autoencoders and graph convolutional neural networks, while providing an effective…
Jiao Liu, Mingbo Zhao, Weijian Kong
Dimensionality reduction has always been a major problem for handling huge dimensionality datasets. Due to the utilization of labeled data, supervised dimensionality reduction methods such as Linear Discriminant Analysis tend achieve better classification performance compared with unsupervised methods. However…
Roméo Tayewo, François Septier, Ido Nevat, Gareth W. Peters + 1 more
'Donald J. Jacobs'] We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general…
Ding Li, Scott Dick
Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is…
Tomokaze Shiratori, Yuichi Takano, Jianchao Bai
Sparse estimation of a Gaussian graphical model (GGM) is an important technique for making relationships between observed variables more interpretable. Various methods have been proposed for sparse GGM estimation, including the graphical lasso that uses the ℓ1 norm regularization term, and other methods that use…
Markku Kuismin, Mikko J Sillanpää, Pier Luigi Martelli
The graphical lasso (Glasso) (; ) is one of the most popular tools for Gaussian graphical model (GGM) selection: the papers of and describing its use have been cited over 1821 and 544 times, respectively (Web of Science database, May 22, 2020). This is due to the following beneficial properties of L1 regularization…
Edward Antonian, Gareth W. Peters, Michael Chantler, Longxiu Huang
In this paper, we study a class of non-parametric regression models for predicting graph signals { 𝐲 t } as a function of explanatory variables { 𝐱 t }. Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. The goal of this paper is to…
Nicole Krämer, Juliane Schäfer, Anne-Laure Boulesteix
Background Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the…
Hai-Hui Huang, Yong Liang, Xiao-Ying Liu
Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased…
Yanbo Wang, Quan Liu, Bo Yuan
Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convex ℓ1 plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these…
Peixin Tian, Yiqian Hu, Zhonghua Liu, Yan Dora Zhang
Motivation Variable selection is a common statistical approach to identifying genes associated with clinical outcomes of scientific interest. There are thousands of genes in genomic studies, while only a limited number of individual samples are available. Therefore, it is important to develop a method to identify genes…