Davide Chicco
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View article: Data-driven probabilistic mapping of the spatial and molecular landscape of glioma
Data-driven probabilistic mapping of the spatial and molecular landscape of glioma Open
Understanding the spatial distribution of gliomas in the brain and their molecular subtypes can aid in diagnosis and development of targeted therapies. This study aims to create probabilistic radiologic maps of glioma locations using large…
View article: Nine quick tips for trustworthy machine learning in the biomedical sciences
Nine quick tips for trustworthy machine learning in the biomedical sciences Open
As machine learning (ML) becomes increasingly central to biomedical research, the need for trustworthy models is more pressing than ever. In this paper, we present nine concise and actionable tips to help researchers build ML systems that …
View article: Designing Vector-Symbolic Architectures for Biomedical Applications: Ten Tips and Common Pitfalls
Designing Vector-Symbolic Architectures for Biomedical Applications: Ten Tips and Common Pitfalls Open
Vector-Symbolic Architectures (VSAs) provide a powerful, brain-inspired framework for representing and manipulating complex data across the biomedical sciences. By mapping heterogeneous information, from genomic sequences and molecular str…
View article: Predicting the toxicity of chemical compounds via Hyperdimensional Computing
Predicting the toxicity of chemical compounds via Hyperdimensional Computing Open
Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predomina…
View article: A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification
A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification Open
Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolv…
View article: The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters
The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters Open
Clustering methods are unsupervised machine learning techniques that aggregate data points into specific groups, called clusters , according to specific criteria defined by the clustering algorithm employed. Since clustering methods are un…
View article: A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics
A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics Open
In an age when machine learning and artificial intelligence are broadly employed, traditional statistics can still provide insightful information and results quickly and at a low computational cost. Statistics, in fact, offers many useful …
View article: TaGra: an open Python package for easily generating graphs from data tables through manifold learning
TaGra: an open Python package for easily generating graphs from data tables through manifold learning Open
The challenge of analyzing high-dimensional data affects many scientific disciplines, from pharmacology to chemistry and biology. Traditional dimensionality reduction methods often oversimplify data, making it difficult to interpret indivi…
View article: Hyperdimensional computing in biomedical sciences: a brief review
Hyperdimensional computing in biomedical sciences: a brief review Open
Hyperdimensional computing (HDC, also known as vector-symbolic architectures—VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. It …
View article: A teaching proposal for a short course on biomedical data science
A teaching proposal for a short course on biomedical data science Open
As the availability of big biomedical data advances, there is a growing need of university students trained professionally on analyzing these data and correctly interpreting their results. We propose here a study plan for a master’s degree…
View article: Eight quick tips for biologically and medically informed machine learning
Eight quick tips for biologically and medically informed machine learning Open
Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine l…
View article: Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation
Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Open
Machine learning continues to grow in popularity in academia, in industry, and is increasingly used in other fields. However, most of the common metrics used to evaluate even simple binary classification models have shortcomings that are n…
View article: Gene signatures for cancer research: A 25-year retrospective and future avenues
Gene signatures for cancer research: A 25-year retrospective and future avenues Open
Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and incons…
View article: Ten quick tips for electrocardiogram (ECG) signal processing
Ten quick tips for electrocardiogram (ECG) signal processing Open
The electrocardiogram (ECG) is a powerful tool to measure the electrical activity of the heart, and the analysis of its data can be useful to assess the patient’s health. In particular, the computational analysis of electrocardiogram data,…
View article: Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records
Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records Open
Background Systemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully…
View article: Ensemble machine learning reveals key features for diabetes duration from electronic health records
Ensemble machine learning reveals key features for diabetes duration from electronic health records Open
Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as…
View article: Ten quick tips for fuzzy logic modeling of biomedical systems
Ten quick tips for fuzzy logic modeling of biomedical systems Open
Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness an…
View article: Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme
Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme Open
Glioblastoma multiforme (GM) is a malignant tumor of the central nervous system considered to be highly aggressive and often carrying a terrible survival prognosis. An accurate prognosis is therefore pivotal for deciding a good treatment p…
View article: Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment
Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment Open
Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that…
View article: Ten quick tips for avoiding pitfalls in multi-omics data integration analyses
Ten quick tips for avoiding pitfalls in multi-omics data integration analyses Open
Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatme…