arXiv subjects

Giacomo Baruzzo

Publications and source records attributed to Giacomo Baruzzo.

3 recordsLinked to original sources

arXiv2026

muxvizpy: a Python library for the analysis of multilayer biological networks

Biological systems are inherently multilayered: the same entities---genes, cells, or bacterial species---participate simultaneously in qualitatively distinct types of interactions, each carrying complementary information that no single relational view can capture. Analysing such systems with single-layer tools, or by collapsing layers into a monoplex projection, systematically discards inter-layer dependencies and can yield misleading conclusions about centrality, community structure, and robustness. The multilayer network formalism addresses, and \texttt{muxViz} established one of the first comprehensive toolkits for its structural analysis, but its R-only interface and dense data structures limit applicability to large biological networks. We introduce \textit{muxvizpy}, a Python library that reimplements and extends the \texttt{muxViz} analytical catalogue with a sparse linear-algebra stack built on SciPy and PyTorch. Muxvizpy exposes seven categories through a unified, composable API and is numerically validated against \texttt{muxViz} on synthetic Erd\H{o}s--R\'enyi and Barab\'asi--Albert multiplex networks while substantially reducing peak memory and wall-clock time at scale. We illustrate its applicability on a virus--human protein-interaction multiplex in which computing some structural analysis was unfeasible. \\[2pt] muxvizpy is freely available under the MIT licence at https://github.com/CoMuNeLab/MuxVizPy. Mathematical definitions of all implemented metrics are provided in the Additional File.

q-bio.QM↗

arXiv2024

Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data

In tabular biomedical data analysis, tuning models to high accuracy is considered a prerequisite for discussing feature importance, as medical practitioners expect the validity of feature importance to correlate with performance. In this work, we challenge the prevailing belief, showing that low-performing models may also be used for feature importance. We propose experiments to observe changes in feature rank as performance degrades sequentially. Using three synthetic datasets and six real biomedical datasets, we compare the rank of features from full datasets to those with reduced sample sizes (data cutting) or fewer features (feature cutting). In synthetic datasets, feature cutting does not change feature rank, while data cutting shows higher discrepancies with lower performance. In real datasets, feature cutting shows similar or smaller changes than data cutting, though some datasets exhibit the opposite. When feature interactions are controlled by removing correlations, feature cutting consistently shows better stability. By analyzing the distribution of feature importance values and theoretically examining the probability that the model cannot distinguish feature importance between features, we reveal that models can still distinguish feature importance despite performance degradation through feature cutting, but not through data cutting. We conclude that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. This paper demonstrates the potential for utilizing feature importance analysis alongside statistical analysis to compare features relatively, even when classifier performance is not satisfactory.

stat.ML↗

arXiv2024

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.

q-bio.NC↗