Home › Entities › academia › Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Research article (Molecules, 2021) · cited 11× · AI/ML
academia openalex:W3205709618
Press Enter · cited answer in seconds

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Summary

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils is a scholarly article[1].

Key Facts

  • Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

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.

APA 4ort.xyz Knowledge Graph. (2026). Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils. Retrieved May 24, 2026, from https://4ort.xyz/entity/machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur
MLA “Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur.
BibTeX @misc{4ortxyz_machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils}}, year = {2026}, url = {https://4ort.xyz/entity/machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils — https://4ort.xyz/entity/machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/machine-learning-data-augmentation-as-a-tool-to-enhance-quantitative-compositionactivity-relationships-of-complex-mixtur · Last refreshed: