ArticleiScience2020
Model Reconstruction from Small-Angle X-Ray Scattering Data Using Deep Learning Methods.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- From Static Structures to Molecular Dynamics: Emerging Directions in X-Ray and Electron Materials Characterization.Materials (Basel, Switzerland) · 2026Review
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Structure-guided development of an electrochemical aptasensor for Salmonella Typhi HlyE antigen detection using in silico and experimental approaches.Scientific reports · 2026Article
- Article
- Analyzing aptamer structure and interactions: in silico modelling and instrumental methods.Biophysical reviews · 2024Review
- Direct experimental observation of blue-light-induced conformational change and intermolecular interactions of cryptochrome.Communications biology · 2022Article
- Parameter inversion of a polydisperse system in small-angle scattering.Journal of applied crystallography · 2022Article
- Recent advances in structural characterization of biomacromolecules in foodsFrontiers in nutrition · 2022Review
- Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data.Biophysical journal · 2021Article
- Latent representation learning in biology and translational medicine.Patterns (New York, N.Y.) · 2021Review
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Machine learning deciphers structural features of RNA duplexes measured with solution X-ray scattering.IUCrJ · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small-angle X-ray scattering (SAXS) method is widely used in investigating protein structures in solution, but high-quality 3D model reconstructions are challenging. We present a new algorithm based on a deep learning method for model reconstruction from SAXS data. An auto-encoder for protein 3D models was trained to compress 3D shape information into vectors of a 200-dimensional latent space, and the vectors are optimized using genetic algorithms to build 3D models that are consistent with the scattering data. The program has been tested with experimental SAXS data, demonstrating the capacity and robustness of accurate model reconstruction. Furthermore, the model size information can be optimized using this algorithm, enhancing the automation in model reconstruction directly from SAXS data. The program was implemented using Python with the TensorFlow framework, with source code and webserver available from http://liulab.csrc.ac.cn/decodeSAXS.
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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.