2 papers
q-bio.QM2020
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
Lingjing Jiang, Niina Haiminen, Anna-Paola Carrieri +7
Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and com…
q-bio.GN2019
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development
Laura-Jayne Gardiner, Anna Paola Carrieri, Jenny Wilshaw +3
Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noi…