ArticleAnnual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing2022
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.
Article in Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source.The journal of physical chemistry. A · 2026Article
- Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films.Macromolecules · 2026Article
- A machine-learning-driven data labeling pipeline for scientific analysis inJournal of applied crystallography · 2025Article
- Optimizing inference of segmentation on high-resolution images in MLExchange.The Journal of supercomputing · 2025Article
- DLSIA: Deep Learning for Scientific Image Analysis.Journal of applied crystallography · 2024Article
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.IS&T International Symposium on Electronic Imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios-users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.