A5032517198- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Computational Drug Discovery Methods
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- Circular RNAs in diseases
- Advanced biosensing and bioanalysis techniques
- Biomedical Text Mining and Ontologies
- Protein Structure and Dynamics
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- Network Packet Processing and Optimization
- Chromosomal and Genetic Variations
- RNA Interference and Gene Delivery
- Microscopic Colitis
- Network Security and Intrusion Detection
- Graph theory and applications
- Inflammatory Bowel Disease
- Caching and Content Delivery
- Complexity and Algorithms in Graphs
- Advanced Multi-Objective Optimization Algorithms
- Advanced Graph Theory Research
- Genomics and Phylogenetic Studies
- Rough Sets and Fuzzy Logic
- Alzheimer's disease research and treatments
Huazhong University of Science and Technology
2011-2025
Tongji Hospital
2011-2025
Zhejiang University
2025
Central South University
2020-2024
Jiangsu Province Hospital
2024
Fuzhou University
2022-2024
Massachusetts Institute of Technology
2022-2024
Shanxi Agricultural University
2024
University of California, Irvine
2018-2023
Quanta Computer (China)
2023
Abstract Motivation Identifying drug–target interactions (DTIs) is a crucial step in drug repurposing and discovery. Accurately identifying DTIs silico can significantly shorten development time reduce costs. Recently, many sequence-based methods are proposed for DTI prediction improve performance by introducing the attention mechanism. However, these only model single non-covalent inter-molecular among drugs proteins ignore complex interaction between atoms amino acids. Results In this...
Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion all miRNA-disease pairs current datasets are experimentally validated. This prompts high-precision computational methods to predict real interaction pairs. In this paper, we propose new model Logistic Model Tree predicting miRNA-Disease Association...
Abstract Background The key to modern drug discovery is find, identify and prepare molecular targets. However, due the influence of throughput, precision cost, traditional experimental methods are difficult be widely used infer these potential Drug-Target Interactions (DTIs). Therefore, it urgent develop effective computational validate interaction between drugs target. Methods We developed a deep learning-based model for DTIs prediction. proteins evolutionary features extracted via Position...
Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying association between circRNAs diseases plays a crucial role exploring pathogenesis complex improving diagnosis treatment However, due to mechanisms diseases, it is expensive time-consuming discover new circRNA-disease associations by biological experiment. Therefore, there increasingly urgent need for utilizing computational methods predict novel...
Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional vitro experiments, more attention has been paid to development efficient feasible computational methods predict potential associations between miRNA disease. In this work, we present a machine learning-based model called MLMDA for predicting association miRNAs More specifically, first use k-mer sparse matrix...
<h3>Importance</h3> Patients increasingly demand transparency in and control of how their medical records biospecimens are shared for research. How much they willing to share what factors influence sharing preferences remain understudied real settings. <h3>Objectives</h3> To examine whether various presentations consent forms associated with differences electronic health record biospecimen rates these vary according user interface design, data recipients, items, patient characteristics....
Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore pathogenesis of complex promote disease-targeted therapy. Most methods, such as k-mer PSSM, based on analysis high-throughput expression data have tendency think functionally similar nucleic acid lack direct linear homology regardless positional information only quantify nonlinear...
Abstract Aberrant regulation of microRNAs (miRNAs) has been implicated in the pathogenesis Alzheimer’s disease (AD), but most abnormally expressed miRNAs found AD are not regulated by synaptic activity. Here we report that dysfunction miR-135a-5p/Rock2/Add1 results memory/synaptic disorder a mouse model AD. miR-135a-5p levels significantly reduced excitatory hippocampal neurons mice. This decrease is tau dependent and mediated Foxd3. Inhibition leads to memory impairments. Furthermore,...
piRNA and PIWI proteins have been confirmed for disease diagnosis treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough further clarify functions of cancer underlying mechanism. Therefore, how provide large-scale serious candidates biological has grown up be a pressing issue. In this study, computational model based on structural perturbation method proposed predict potential disease-associated piRNAs, called...
The accurate measurement of soil moisture content emerges as a critical parameter within the ambit agricultural irrigation management, wherein precise prediction this variable plays an instrumental role in enhancing efficiency and conservation water resources. This study introduces innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) Transformer technologies, meticulously crafted to amplify precision reliability forecasts. Leveraging meteorological...
MicroRNAs (miRNAs) play a critical role in human diseases. Determining the association between miRNAs and disease contributes to elucidating pathogenesis of liver diseases seeking effective treatment method. Despite great recent advances field associations diseases, implementing verification recognition efficiently at scale presents serious challenges biological experimental approaches. Thus, computational methods for predicting miRNA-disease have become research hotspot. In this paper, we...
Abstract Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract difficult to quantify. In this paper, we converted MeSH tree structure into a relationship network applied several graph embedding algorithms on it represent terms. Specifically, consisting nodes (MeSH headings) edges (relationships), which...
Identifying the frequencies of drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine usually time consuming expensive, most existing methods can only predict effect existence or associations, not their frequencies. Inspired by recent progress graph neural networks recommended system, we develop novel prediction model for frequencies, using attention network integrate three different types features,...
The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension biological processes but also is critical for identifying new drugs. However, due to the disadvantages expensive and high time-consuming traditional experiments, small section between drugs targets in database were verified experimentally. Therefore, it meaningful important develop computational methods with good performance DTIs prediction. At present, many existing utilize single type...
Obesity is variably associated with treatment response in biologic-treated patients inflammatory bowel diseases (IBD). We evaluated the association between obesity and risk of hospitalization, surgery, or serious infections IBD new users biologic agents a large, multicenter, electronic health record (EHR)-based cohort (CA-IBD).We created an EHR-based adult who were (tumor necrosis factor [TNF-α] antagonists, ustekinumab, vedolizumab) January 1, 2010, June 30, 2017, from 5 systems California....
Recent advances in cellular research demonstrate that scRNA-seq characterizes heterogeneity, while spatial transcriptomics reveals the distribution of gene expression. Cell representation is fundamental issue two fields. Here, we propose Topology-encoded Latent Hyperbolic Geometry (TopoLa), a computational framework enhancing cell representations by capturing fine-grained intercellular topological relationships. The introduces new metric, TopoLa distance (TLd), which quantifies geometric...
Abstract Dietary high salt intake is increasingly recognized as a risk factor for cognitive decline and dementia, including Alzheimer’s disease (AD). Recent studies have identified population of disease‐associated astrocytes (DAA)‐like closely linked to amyloid deposition tau pathology in an AD mouse model. However, the presence role these high‐salt diet (HSD) models remain unexplored. In this study, it demonstrated that HSD significantly induces enhanced reactivity DAA‐like hippocampal CA3...
Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes the target regions within remote sensing (RS) images captured under complex scenarios varied conditions. However, existing change detection (RSCD) approaches based on Mamba frequently struggle effectively perceive inherent locality as they direct flatten scan RS (i.e., features same region are not distributed continuously sequence but mixed from other throughout sequence). In...
Longest Prefix Matching (LPM), Policy Filtering (PF), and Content (CF) are three important tasks for Internet nowadays. It is both technologically economically to develop integrated solutions the effective execution of tasks. To this end, in paper, we propose a distributed Ternary Addressable Memory (TCAM) coprocessor architecture. The solution exploits complementary lookup load storage requirements balance among TCAMs. A prefix filtering-based CF algorithm designed reduce novel cache system...