Big multi-omics data in bioinformatics often consists of a huge number of features and relatively small numbers of samples. In addition, features from multi-omics data have their own specific ...

Abstract: Permutation tests are widely used for significance testing in fMRI MVPA (multivariate pattern analysis) studies, but the precise way in which the tests are carried out varies, and test ...

Abstract: Permutation tests are widely used for significance testing in classification-based fMRI analyses, but the precise manner of relabeling varies, and is generally non-trivial for MVPA because ...

1. A `binary features matrix` also known as `Feature Set` (such as somatic mutations, copy number alterations, chromosomal translocations, etc.) The 1/0 row vectors indicate the presence/absence of ...

Permutation methods provide flexible, distribution-free approaches to statistical inference by rearranging data labels to generate an empirical null distribution for a test statistic. Historically ...

The PerQoDA software is designed to test the quality of a dataset for classification tasks using permutation testing. The method requires a labeled dataset. The intuition is that in a good quality ...

Permutation tests are a type of nonparametric test that can be used to compare two or more groups of data. Unlike parametric tests, such as t-tests or ANOVA, permutation tests do not require any ...

Semi-supervised learning is a machine learning technique that uses both labeled and unlabeled data to train a model. It can be useful when you have a lot of data but not enough labels, or when you ...