most citedUncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach

12 citations · 12 across the 1 of their papers we have counts for

collaborators

1 paper

q-bio.QM2014★ 12 cited

Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach

Julián Candia, Srujana Cherukuri, Yin Guo +4

Complex phenotypic differences among different acute leukemias cannot be fully captured by analyzing the expression levels of one single molecule, such as a miR, at a time, but req…