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QLattice
View on Wikipedia| QLattice | |
|---|---|
| Developer | Abzu |
| Initial release | March 4, 2020 |
| Written in | C, Python |
| Operating system | Linux, macOS, Windows |
| Type | Machine learning |
| License | CC BY-NC-ND 4.0 |
| Website | docs |
The QLattice is a software library which provides a framework for symbolic regression in Python. It works on Linux, Windows, and macOS. The QLattice algorithm is developed by the Danish/Spanish AI research company Abzu.[1] Since its creation, the QLattice has attracted significant attention, mainly for the inherent explainability of the models it produces.[2][3][4]
At the GECCO conference in Boston, MA in July 2022, the QLattice was announced as the winner of the synthetic track of the SRBench competition.[5]
Features
[edit]The QLattice works with data in categorical and numeric format. It allows the user to quickly generate, plot and inspect mathematical formulae that can potentially explain the generating process of the data. It is designed for easy interaction with the researcher, allowing the user to guide the search based on their preexisting knowledge.[2][6]
Scientific results
[edit]The QLattice mainly targets scientists, and integrates well with the scientific workflow.[2][6] It has been used in research into many different areas, such as energy consumption in buildings,[3] water potability,[7] heart failure,[8] pre-eclampsia,[4] Alzheimer's disease,[9] hepatocellular carcinoma,[9] and breast cancer.[9]
See also
[edit]References
[edit]- ^ Kevin René Broløs; Meera Vieira Machado; Chris Cave; Jaan Kasak; Valdemar Stentoft-Hansen; Victor Galindo Batanero; Tom Jelen; Casper Wilstrup (2021-04-12). "An Approach to Symbolic Regression Using Feyn". arXiv:2104.05417 [cs.LG].
- ^ a b c Abzu (2022-07-22). "What is a QLattice?".
- ^ a b Wenninger, Simon; Kaymakci, Can; Wiethe, Christian (2022). "Explainable long-term building energy consumption prediction using QLattice". Applied Energy. 308 118300. Elsevier BV. Bibcode:2022ApEn..30818300W. doi:10.1016/j.apenergy.2021.118300. ISSN 0306-2619. S2CID 245428233.
- ^ a b Wilstrup, Casper; Hedley, Paula L.; Rode, Line; Placing, Sophie; Wøjdemann, Karen R.; Shalmi, Anne-Cathrine; Sundberg, Karin; Christiansen, Michael (2022-06-30), Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia, Cold Spring Harbor Laboratory, doi:10.1101/2022.06.29.22277072, S2CID 250331945
- ^ Michael Kommenda; William La Cava; Maimuna Majumder; Fabricio Olivetti de França; Marco Virgolin (2022-07-22). "SRBench Competition 2022: Interpretable Symbolic Regression for Data Science".
- ^ a b Bharadi, Vinayak (2021-07-30). "QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning". Emerging Technologies for Healthcare. Wiley. pp. 69–92. doi:10.1002/9781119792345.ch3. ISBN 9781119792345. S2CID 238793347.
- ^ Riyantoko, Prismahardi Aji; Diyasa, I Gede Susrama Mas (2021-10-28). "F.Q.A.M" Feyn-QLattice Automation Modelling: Python Module of Machine Learning for Data Classification in Water Potability. IEEE. pp. 135–141. doi:10.1109/icimcis53775.2021.9699371. ISBN 978-1-6654-2733-3.
- ^ Wilstup, Casper; Cave, Chris (2021-01-15), Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths, Cold Spring Harbor Laboratory, doi:10.1101/2021.01.15.21249874, S2CID 231609904
- ^ a b c Christensen, Niels Johan; Demharter, Samuel; Machado, Meera; Pedersen, Lykke; Salvatore, Marco; Stentoft-Hansen, Valdemar; Iglesias, Miquel Triana (2022-06-22). "Identifying interactions in omics data for clinical biomarker discovery using symbolic regression". Bioinformatics. 38 (15). Oxford University Press (OUP): 3749–3758. doi:10.1093/bioinformatics/btac405. ISSN 1367-4803. PMC 9344843. PMID 35731214.
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