About

Wei Wang is a Professor in the Computer Science Department at UCLA Samueli School of Engineering, where he also holds the Leonard Kleinrock Term Chair in Computer Science. His research interests encompass data mining, bioinformatics, database systems, machine learning, and natural language processing. Dr. Wang has made significant contributions to these fields, evidenced by his numerous awards and recognitions, including being named an IEEE Fellow in 2023 and an ACM Fellow in 2020. His work has been recognized through awards such as the IBM Invention Achievement Awards, NSF CAREER Award, and the ACM SIGKDD Service Award, among others. Dr. Wang's research focuses on advancing understanding and development in data-driven sciences, with particular emphasis on bioinformatics and health informatics, contributing to the integration of computational techniques in biological and medical research.

Research topics

• Computer Science
• Artificial Intelligence
• Information Retrieval
• Computational biology
• Genetics
• Data Mining
• Biology
• Mathematics
• Medicine
• World Wide Web
• Internal medicine
• Data science

Selected publications

• Semantic analysis-based recommender system using sequential clustering and convolutional neural network

  Engineering Applications of Artificial Intelligence · 2025-09-11 · 1 citations

  article
  PublisherDOI

• A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data

  Biology · 2025-11-19 · 3 citations

  articleOpen access

  . The high dimensionality of WGS data also increases computational demands, limiting its practical application. In this study, we introduce DAGP, a novel method that integrates deep autoencoder compression to reduce WGS data dimensionality by over 99% while preserving essential genetic information. This compression significantly improves computational efficiency, facilitating the effective use of high-dimensional genomic data. Our results demonstrated that DAGP, when combined with the genomic best linear unbiased prediction (GBLUP) method, maintained prediction accuracy comparable to WGS data, even at reduced marker densities of 50 K for sturgeon and 20 K for maize. Furthermore, integrating DAGP with Bayesian and machine learning models improved genomic prediction accuracy over traditional WGS-based GBLUP, with an average gain of 6.05% and 5.35%, respectively. DAGP provides an efficient and scalable solution for genomic prediction in species with large-scale genomic data, offering both computational feasibility and enhanced prediction performance.

  PublisherOA PDFDOI

• Technological strategies for building a network of sports facilities in urban environments

  Technology Analysis and Strategic Management · 2025-09-18

  article1st authorCorresponding
  PublisherDOI

• Integrating BERT and Graph Convolutional Networks for Medical Literature Mining: A Knowledge Graph Ap-proach to Pelvic Fracture Research Analysis (Preprint)

  2025-09-13

  preprintOpen accessSenior author

  <sec> <title>BACKGROUND</title> Pelvic fractures have consistently been a focal point in orthopedic research. This study aims to provide a comprehensive analysis of the literature on pel-vic fractures published between 1983 and 2023, revealing research trends, hotspots, and frontiers in this field. </sec> <sec> <title>OBJECTIVE</title> This study aimed to provide a comprehensive bibliometric and knowledge graph–based analysis of pelvic fracture literature published between 1983 and 2023, identifying research trends, hotspots, and emerging frontiers in this field. </sec> <sec> <title>METHODS</title> We searched the Web of Science database using a predefined strategy restricted to review articles and original research articles, excluding studies outside orthopedics and surgery. Medical entities and relationships were extracted to construct a comprehensive knowledge graph. Entity recognition, relationship extraction, and network topology analyses were performed to map research evolution and collaboration networks. </sec> <sec> <title>RESULTS</title> A total of 5248 articles were included for analysis. The results show a steady increase in the annual publication of pelvic fracture research, particularly after 2005. The United States, Germany, and China are the top three coun-tries in terms of the number of publications, with the University of Wash-ington, University of California, and University of San Francisco ranking the top three regions. Pohlmann T published the most significant number of ar-ticles, and Vaidya R was the strongest citation bursts author. Research on pelvic fractures has made significant progress over the past forty years, espe-cially in treatment techniques and methods. Bibliometric analysis reveals re-search hotspots in this field, such as hemostasis control, fracture fixation techniques, and osteoporotic fractures. </sec> <sec> <title>CONCLUSIONS</title> This study employs bibliometrics to quantify and delineate the contemporary research landscape and trends in pelvic fracture research, aspiring to provide scholars with a compass for navigating the realm of pelvic fracture-related research. </sec>

  PublisherDOI

• [Advances in the application of artificial intelligence-based predictive models for histopathological image analysis in pathological diagnosis].

  PubMed · 2025-11-08

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  PublisherDOI

• Integrative transcriptomics analysis reveals the metabolic regulatory functions of lncRNA in the livers of yak at different age stages

  PLoS ONE · 2025-10-08

  articleOpen accessCorresponding

  Exploring the regulatory role of long non-coding RNA (lncRNA) in plateau yak is crucial to understanding its metabolic network for adapting to extreme environments. By integrating transcriptomic sequencing and co-expression network analysis, the messenger RNA (mRNA) and lncRNA expression characteristics of yak liver at three growth and development stages were systematically analyzed. A total of 35,216 mRNAs and 10,073 lncRNAs were detected. Among the 288 differentially expressed lncRNAs, 88 lncRNAs related to metabolism were screened, and their potential functions in lipid metabolism, collagen remodeling, and protein transport were predicted. The age-dependent expression patterns of some lncRNAs were verified through qRT-PCR (quantitative real-time reverse transcription polymerase chain reaction) experiments, which initially revealed the status and role of lncRNAs in metabolic regulation in yak liver. This study provides new insights into the molecular mechanisms underlying metabolic adaptation in high-altitude species such as yak, and establishes a methodological framework for the screening and identification of functional lncRNAs in non-model organisms.

  PublisherDOI

• Smart Courts and Digital Analytics: Changing Basketball Teaching Methods with Data-Driven Insights

  International Journal of Human-Computer Interaction · 2025-11-28

  articleSenior authorCorresponding
  PublisherDOI

• Minute-scale single-cell transcriptomics enables dynamic modeling of cellular behavior

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-17

  preprintOpen accessSenior authorCorresponding

  Dynamic cellular processes such as signaling, fate decisions, and intercellular communication unfold on minute timescales, a regime inaccessible to conventional transcriptomic methods. This temporal gap has fundamentally limited the development of predictive, causal models of cell behavior. Here, we bridge this gap by introducing ChronoSeq, an automated single-cell RNA sequencing platform that achieves genome-wide profiling with a temporal resolution as brief as seven minutes. By integrating automated live-cell sampling with molecular time-barcoding, ChronoSeq captures rapid transcriptional dynamics with high fidelity. Applying ChronoSeq to TNF-α stimulated cells, we discovered a rapid, heterogeneity-driven bifurcation in the NF-κB response that was previously unobservable. We further demonstrate that the high-density temporal data generated by ChronoSeq enables a new class of computational models that dramatically outperform existing methods in inferring the directionality and targets of post-translationally regulated transcription factors. Finally, in a multicellular co-culture, ChronoSeq resolved a paracrine signaling cascade in real time, identifying both the timing and molecular identity of the intercellular relay. By providing a framework to measure dynamics, infer regulation, and model communication at the true pace of biology, ChronoSeq establishes a new foundation for dynamic systems biology.

  PublisherDOI

• Substance flow analysis combined with neural networks for predicting and reducing lead pollution in the secondary lead industry

  Waste Management & Research The Journal for a Sustainable Circular Economy · 2025-09-27

  articleSenior author

  The recycling of spent lead-acid batteries has become increasingly crucial for lead (Pb) supply, yet it generates significant secondary pollutants including lead dust, water-quenched slag (WQS) and wastewater that threaten soil and groundwater quality. The amount of discharged pollutants is influenced by several key processes, including crushing, separation, pre-desulphurization, crude lead smelting, refining and slag production. Accurate identification and predictive monitoring of primary pollutant-generating processes allow for targeted process optimization and enhanced environmental control. In this study, the substance flow method was employed to quantify Pb flows throughout the entire production processes and identified WQS as the primary pathway for Pb release into the environment. Then, a genetic algorithm (GA) was used to optimize an artificial neural network (ANN) model for the real-time estimation of pollutant generation from the key processes (slag production). The developed GA-ANN model exhibited a high level of prediction accuracy (mean square error = 0.0003), enabling enterprise to estimate the Pb content in WQS by analysing key input parameters. This facilitates data-driven adjustments to process parameters for pollution mitigation, offering actionable insights within actual production.

  PublisherDOI

• Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer

  Frontiers in Immunology · 2025-11-03 · 2 citations

  articleOpen access

  Introduction Gastric cancer (GC) is a highly heterogeneous malignancy with poor prognosis, underscoring the urgent need for reliable biomarkers to guide precise stratification and therapy. Transfer RNA-derived small RNAs (tsRNAs) have emerged as potential key regulators in cancer, yet their systematic role in defining GC subtypes remains unexplored. Methods We profiled tsRNA expression in GC using transcriptomic data from TCGA and GEO databases. Unsupervised consensus clustering identified tsRNA-based subtypes. A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays. Results Three distinct tsRNA-mediated subtypes (Stromal_H, Stromal_L, Stromal_M) were identified, exhibiting significant differences in stromal activity, tumor microenvironment, and clinical outcomes. The Stromal_H subtype demonstrated the poorest prognosis, characterized by an immunosuppressive microenvironment and dysregulated DNA repair pathways. A random survival forest (RSF)-based prognostic signature (GCtsRNAscore) effectively stratified patients into high- and low-risk groups, with high-risk patients showing increased sensitivity to targeted therapies (axitinib, bexarotene, dasatinib) and low-risk patients benefiting more from immunotherapy. Furthermore, tsRNA-Asp-3-0024 was significantly upregulated in GC tissues and cell lines, where it promoted proliferation and inhibited apoptosis. Discussion Our study establishes tsRNAs as powerful biomarkers for molecular subtyping and prognostic prediction in GC. The tsRNA-defined subtypes and GCtsRNAscore model provide a novel framework for personalized treatment strategies. The functional characterization of tsRNA-Asp-3-0024 highlights its potential as both a therapeutic target and a prognostic indicator, paving the way for tsRNA-based precision medicine in GC.

  PublisherOA PDFDOI

Recent grants

• RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic

  NSF · $114k · 2020–2023

• CAREER: Mining Salient Localized Patterns in Complex Data

  NSF · $437k · 2005–2012

• Designing neutralization antibodies against Sars-Cov-2

  NIH · $434k · 2020–2023

• NIH Grant R56AI125105

  NIH · $671k · 2019

• ABI Innovation: Next Generation Quantitative RNA Sequence Analysis

  NSF · $600k · 2016–2021

Frequent coauthors

• Yizhou Sun

  49 shared

• Xuemin Lin

  Shanghai Jiao Tong University

  48 shared

• Jyun‐Yu Jiang

  Search

  38 shared

• Jiong Yang

  36 shared

• Peipei Ping

  University of California, Los Angeles

  35 shared

• Wei Cheng

  33 shared

• Philip S. Yu

  University of Illinois Chicago

  27 shared

• Leonard McMillan

  University of North Carolina at Chapel Hill

  27 shared

Education

• Ph. D., Computer Science

  University of California Los Angeles

  1999

Awards & honors

• IEEE Fellow, 2023
• ACM Fellow, 2020
• IBM Invention Achievement Awards - 2000 and 2001
• UNC Junior Faculty Development Award - 2003
• NSF Faculty Early Career Development (CAREER) Award - 2005