A5100342324- Prostate Cancer Treatment and Research
- Functional Brain Connectivity Studies
- Bioinformatics and Genomic Networks
- Data Stream Mining Techniques
- Gene expression and cancer classification
- Prostate Cancer Diagnosis and Treatment
- Text and Document Classification Technologies
- Machine Learning and Data Classification
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Anomaly Detection Techniques and Applications
- Time Series Analysis and Forecasting
- Cancer-related molecular mechanisms research
- Cancer, Lipids, and Metabolism
- Domain Adaptation and Few-Shot Learning
- Advanced Bandit Algorithms Research
- Molecular Biology Techniques and Applications
- Cancer Genomics and Diagnostics
- Neural dynamics and brain function
- Bayesian Modeling and Causal Inference
- EEG and Brain-Computer Interfaces
- Ferroptosis and cancer prognosis
- Cerebrovascular and Carotid Artery Diseases
- Acute Ischemic Stroke Management
- RNA modifications and cancer
Mohamed bin Zayed University of Artificial Intelligence
2025
Xavier University of Louisiana
2015-2024
Second Xiangya Hospital of Central South University
2021-2024
Central South University
2021-2024
Kunming Medical University
2022-2024
Ocean University of China
2024
Henan Normal University
2022-2024
Peking University
2024
Peking University Cancer Hospital
2024
Henan Provincial People's Hospital
2018-2024
In multi-label learning, each training example is associated with a set of labels and the task to predict proper label for unseen example. Due tremendous (exponential) number possible sets, learning from examples rather challenging. Therefore, key successful how effectively exploit correlations between different facilitate process. this paper, we propose use Bayesian network structure efficiently encode conditional dependencies as well feature set, common parent all labels. To make it...
Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation with instantaneous effects. Estimation Gaussian, linear poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how combine the model This effectively what called a vector autoregression (SVAR) model, and thus our work contributes long-standing problem estimate SVAR's. We that such identifiable...
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain improve accuracy in the target domain. However, assumption made by existing approaches, that marginal conditional probabilities directly related between domains, has applicability either original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach maps distribution of target-domain source-domain data into common space, utilize...
Anomaly detection in streaming data is of high interest numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies data. Underlying the fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant estimate each RS-tree defined on tree node into which an instance falls. Each incoming stream scored estimates averaged over all forest. Two strategies,...
Alu elements are the most abundant retrotransposable comprising ~11% of human genome. Many studies have highlighted role that in genetic instability and how their contribution to assortment mutagenic events can lead cancer. As yet, little has been done quantitatively assess association between distribution genes causally implicated oncogenesis. We investigated effect various densities on mutation type based classifications cancer genes. In order establish direct relationship s interest,...
We test the adequacies of several proposed and two new statistical methods for recovering causal structure systems with feedback from synthetic BOLD time series. compare an adaptation first correct method cyclic linear systems; Granger regression; a multivariate autoregressive model permutation test; Group Iterative Multiple Model Estimation (GIMME) algorithm; Ramsey et al. non-Gaussian methods; by Hyvärinen Smith; due to Patel al.; GlobalMIT algorithm. introduce also methods, Fast Adjacency...
Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable graph convolutional network (MGCN) model, incorporating the fMRI time series (FC) between each pair regions. Specifically, our model learns a embedding networks derived data. A manifold-based...
Abstract Understanding drug–drug interactions is an essential step to reduce the risk of adverse drug events before clinical co-prescription. Existing methods, commonly integrating heterogeneous data increase model performance, often suffer from a high complexity, As such, how elucidate molecular mechanisms underlying while preserving rational biological interpretability challenging task in computational modeling for discovery. In this study, we attempt investigate via associations between...
Several observational studies have found that idiopathic pulmonary fibrosis (IPF) is often accompanied by elevated circulating C-reactive protein (CRP) levels. However, the causal relationship between them remains to be determined. Therefore, our study aimed explore effect of CRP levels on IPF risk two-sample Mendelian randomization (MR) analysis.We analyzed data from two genome-wide association (GWAS) European ancestry, including (204,402 individuals) and (1028 cases 196,986 controls). We...
Abstract Background Guanosine is a purine nucleoside that widely used as raw material for food additives and pharmaceutical products. Microbial fermentation the main production method of guanosine. However, guanosine-producing strains possess multiple metabolic pathway interactions complex regulatory mechanisms. The lack with efficiently producing-guanosine greatly limited industrial application. Results We attempted to produce guanosine in Escherichia coli using systematic engineering....
We have previously shown that the Epstein-Barr virus (EBV) likely encodes hundreds of viral long noncoding RNAs (vlncRNAs) are expressed during reactivation. Here we show EBV latency origin replication (oriP) is transcribed bi-directionally reactivation and both leftward (oriPtLs) rightward (oriPtRs) transcripts largely localized in nucleus. While oriPtLs most noncoding, at least some oriPtRs contain BCRF1/vIL10 open reading frame. Nonetheless, oriPtR with 5' untranslated regions may...
Hepatocellular carcinoma (HCC) is the most common liver cancer and mechanisms of hepatocarcinogenesis remain elusive.This study aims to mine hub genes associated with HCC using multiple databases.Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed (DEGs) between control in each set identified by limma software. The GO term KEGG pathway enrichment DEGs aggregated datasets (aggregated DEGs) analyzed DAVID KOBAS 3.0 databases. Protein-protein...
Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order data nonlinearity have shown dramatic successes CTR prediction. However, these work poorly on cold-start ads with new IDs, whose embeddings are not well learned yet. In this paper, we propose Graph Meta Embedding (GME) can rapidly learn how to generate desirable initial for ad IDs based graph neural networks meta...
Canonical work handling distribution shifts typically necessitates an entire target that lands inside the training distribution. However, practical scenarios often involve only a handful of samples, potentially lying outside support, which requires capability extrapolation. In this work, we aim to provide theoretical understanding when extrapolation is possible and offer principled methods achieve it without requiring on-support To end, formulate problem with latent-variable model embodies...
Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data, providing insights into regulatory mechanisms. A significant yet often overlooked challenge is selection bias, a process where only cells meeting specific criteria, such as thresholds, survive or are observed, distorting the true joint distribution of and thus biasing GRNI results. Furthermore, influenced by latent confounders, non-coding RNAs, which add complexity GRNI. To...
ABSTRACT Comprehensive virome analysis of RNA sequence (RNA-seq) data sets from 118 non-Hodgkin's B-cell lymphomas revealed a small subset that is positive for Epstein-Barr virus (EBV) or human herpesvirus 6B (HHV-6B), with one coinfection. EBV transcriptome expression the latency genes RPMS1, LMP1, and LMP2, sample additionally showing high level early lytic another EBNA2 expression. HHV-6B majority were transcribed.
Interleukin-17 (IL-17) plays important roles in inflammation, autoimmune diseases, and some cancers. Obese people are a chronic inflammatory state with increased serum levels of IL-17, insulin, insulin-like growth factor 1 (IGF1). How these factors contribute to the status that promotes development aggressive prostate cancer obese men is largely unknown. We found that, mice, hyperinsulinemia enhanced IL-17-induced expression downstream proinflammatory genes IL-17 receptor A (IL-17RA),...
Abstract Previous studies have documented that decompression led to endothelial dysfunction with controversial results. This study aimed clarify the relationship between dysfunction, bubble formation and rate. Rats were subjected simulated air dives one of four rates: slow three rapid. Bubble was detected ultrasonically following for two hours, before measurement related indices. Bubbles found in only rapid-decompressed rats amount correlated rate significant variability. Serum levels ET-1,...
Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents MDD and healthy controls, particularly microstates differences. The aim current study was to characterize microstate activity controls (HCs). Methods: A total 35 HCs were recruited this study. symptoms assessed by Hamilton Depression Scale (HAMD) Children's Inventory (CDI), anxiety...