gliozzoj.r-universe.dev

[anacletolab] mosclust 1.0.3

jessica.gliozzo@gmail.com (Jessica Gliozzo) — Thu, 10 Sep 2026 08:32:25 GMT

Stability based methods for model order selection in clustering problems (Valentini, G (2007), ). Using multiple perturbations of the data the stability of clustering solutions is assessed. Different perturbations may be used: resampling techniques, random projections and noise injection. Stability measures for the estimate of clustering solutions and statistical tests to assess their significance are provided.

[gliozzoj] mosclust 1.0.3

jessica.gliozzo@gmail.com (Jessica Gliozzo) — Thu, 10 Sep 2026 08:32:25 GMT

[anacletolab] clusterv 1.1.2

jessica.gliozzo@gmail.com (Jessica Gliozzo) — Wed, 09 Sep 2026 16:01:02 GMT

The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), .

[gliozzoj] clusterv 1.1.2

jessica.gliozzo@gmail.com (Jessica Gliozzo) — Wed, 09 Sep 2026 16:01:02 GMT

[gliozzoj] NetInt 1.0.2

jessica.gliozzo@gmail.com (Jessica Gliozzo) — Wed, 09 Sep 2026 14:59:41 GMT

Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the ''predictiveness strength'' of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction).