ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2020
Machine learning and big scientific data.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Nature's economy as blueprint: regional overlap & block-based algorithm for protein design.Frontiers in bioinformatics · 2026Article
- Advanced analysis of defect clusters in nuclear reactors using machine learning techniques.Scientific reports · 2025Article
- Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network.Sensors (Basel, Switzerland) · 2025Review
- Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior.Animals : an open access journal from MDPI · 2025Article
- Genetic variations as predictors of dispositional and dyadic empathy-a couple study.Scientific reports · 2024Article
- HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry.Oncology letters · 2023Article
- A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study.Frontiers in endocrinology · 2023Article
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Optimization and inference of bin widths for histogramming inelastic neutron scattering spectra.Journal of applied crystallography · 2022Article
- aEYE: A deep learning system for video nystagmus detection.Frontiers in neurology · 2022Article
- Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.Acta neurochirurgica. Supplement · 2022Review
- Artificial Intelligence-A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant.Journal of clinical medicine · 2021Article
- A framework for probabilistic weather forecast post-processing across models and lead times using machine learning.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.Frontiers in molecular biosciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such 'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our 'scientific machine learning' benchmark suite and of the research challenges these benchmarks will enable. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.
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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.