VLDB 2026 Research / reviewers in the wild / expert
Ming-Yang Ho
dblp:294/5410
· DBLP profile ↗
4ranked-venue papers
3as first author
4since 2021 · last 2024
0000-0002-1767-7494ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 77% Medical and health informatics · 23% | |
| Artificial intelligence
2 papers |
Generative modeling · 100% | |
| Computer graphics and multimedia
1 paper |
Computational photography and imaging · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling › generative adversarial network
image-to-image translation |
1.3 | 2 | 2024 | Every Pixel Has Its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization · ECCV (45) 2024 Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization · ECCV (21) 2022 |
Bioinformatics and computational biology › drug discovery
drug repurposing |
0.5 | 1 | 2021 | PanGPCR: predictions for multiple targets, repurposing and side effects · Bioinform. 2021 |
Bioinformatics and computational biology › drug discovery › target identification
drug target prediction |
0.5 | 1 | 2021 | PanGPCR: predictions for multiple targets, repurposing and side effects · Bioinform. 2021 |
Computational photography and imaging
high-resolution imaging |
0.2 | 1 | 2024 | Every Pixel Has Its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization · ECCV (45) 2024 |
Medical and health informatics
computational pathology |
0.2 | 1 | 2022 | Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization · ECCV (21) 2022 |
Medical and health informatics › computational pathology
virtual staining |
0.2 | 1 | 2022 | Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization · ECCV (21) 2022 |
Bioinformatics and computational biology › drug discovery
drug side effect prediction |
0.1 | 1 | 2021 | PanGPCR: predictions for multiple targets, repurposing and side effects · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
dense normalization · 1.5kernelized instance normalization · 1.1molecular docking · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Every Pixel Has Its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization
Ming-Yang Ho, Che-Ming Wu, Min-Sheng Wu, Yufeng J. Tseng |
ECCV (45) | 1 |
| 2024 | Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGaitabstractWe present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw. Ming-Yang Ho, Ming-Che Kuo, Ciao-Sin Chen, Ruey-Meei Wu, Ching-Chi Chuang, Chi-Sheng Shih 0001, Yufeng J. Tseng |
IEEE J. Biomed. Health Informatics | 1 |
| 2022 | Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization
Ming-Yang Ho, Min-Sheng Wu, Che-Ming Wu |
ECCV (21) | 1 |
| 2021 | PanGPCR: predictions for multiple targets, repurposing and side effectsabstractSUMMARY: Drug discovery targeting G protein-coupled receptors (GPCRs), the largest known class of therapeutic targets, is challenging. To facilitate the rapid discovery and development of GPCR drugs, we built a system, PanGPCR, to predict multiple potential GPCR targets and their expression locations in the tissues, side effects and possible repurposing of GPCR drugs. With PanGPCR, the compound of interest is docked to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound's GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side-Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand. AVAILABILITY AND IMPLEMENTATION: PanGPCR is freely accessible at https://gpcrpanel.cmdm.tw/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Lu-Chi Liu, Ming-Yang Ho, Bo-Han Su, San-Yuan Wang, Ming-Tsung Hsu, Yufeng J. Tseng |
Bioinform. | 2 |