Unraveling genotype-phenotype associations and predictive modeling of outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases, with a 5-year survival rate of only 20-30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed, and quantitatively subtyped.

Challenges in translational machine learning.

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models.

Analyzing high-dimensional cytometry data using FlowSOM.

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology.