Home ›
Entities
› academia
› Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models
Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models
Research article (Chemometrics and Intelligent Laboratory Systems, 2016) · cited 13× · AI/ML
Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models
Summary
Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models is a scholarly article[1].
Key Facts
Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models. Retrieved May 24, 2026, from https://4ort.xyz/entity/predicting-the-biological-activities-of-triazole-derivatives-as-sglt2-inhibitors-using-multilayer-perceptron-neural-netw
MLA“Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/predicting-the-biological-activities-of-triazole-derivatives-as-sglt2-inhibitors-using-multilayer-perceptron-neural-netw.
BibTeX@misc{4ortxyz_predicting-the-biological-activities-of-triazole-derivatives-as-sglt2-inhibitors-using-multilayer-perceptron-neural-netw_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models}}, year = {2026}, url = {https://4ort.xyz/entity/predicting-the-biological-activities-of-triazole-derivatives-as-sglt2-inhibitors-using-multilayer-perceptron-neural-netw}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models — https://4ort.xyz/entity/predicting-the-biological-activities-of-triazole-derivatives-as-sglt2-inhibitors-using-multilayer-perceptron-neural-netw (retrieved 2026-05-24)
