Machine learning-assisted pathway enrichment analysis
Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patterns. The approach moves beyond ranking pathways by p-value alone, using ML models to weight gene contributions, distinguish signal from noise across many samples, and prioritize biologically meaningful pathways in complex datasets.
Allikakirje
Tsiteeringud kopeeritud meetodi allikakirjest sõna-sõnalt. Nendest ei saa järeldada väidete tasemel kinnitust.
- Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma'ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128. · URL
- Way, G. P., & Greene, C. S. (2018). Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Pacific Symposium on Biocomputing, 23, 80–91. · URL
Kureeritud väited
Väited on salvestatud tõendite registrisse, igal oma hinnanguga.
See vaade ei loo väite hinnangut, kui registris seda pole.
Seotud meetodid
Genereeritud meetodigraafist ja kuvatud masina soovitatud seostena – väiteid ei järeldata.