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Machine Learning is a powerful tool for uncovering relationships and patterns within datasets. However, applying it to a large datasets can lead to biased outcomes and quality issues, due to confounder variables indirectly related to the outcome ...

Among the many proposed solutions in graph embedding, traditional random walk-based embedding methods have shown their promise in several fields. However, when the graph contains high-degree nodes, random walks often neglect low- or middle-degree ...

The COVID-19 pandemic highlighted the importance of RNA-based technologies for the development of new vaccines. Besides vaccines, a world of RNA-based drugs, including small non-coding RNA, could open new avenues for the development of novel ...

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Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high ...

Though a great deal of research work has been devoted to the development of dimensionality reduction algorithms, the problem is still open. The most recent and effective techniques, assuming datasets drawn from an underlying low dimensional ...

With the increasing spread and usage of Internet technologies, the challenge of ensuring Web accessibility for all, including anyone with a form of disability, has become an hot issue, pursued both by the World Wide Web Consortium (W3C) and by ...

The annotation and characterization of tissue-specific cis-regulatory elements (CREs) in non-coding DNA represents an open challenge in computational genomics. Several prior works show that machine learning methods, using epigenetic or spectral ...

Many interactions among bio-molecular entities, e.g. genes, proteins, metabolites, can be easily represented by means of property graphs, i.e. graphs that are annotated both on the vertices (e.g. entity identifier, Gene Ontology or Human Phenotype ...

At the present, several applications need to classify high dimensional points belonging to highly unbalanced classes. Unfortunately, when the training set cardinality is small compared to the data dimensionality (''small sample size'' problem) the ...

Recently, a great deal of research work has been devoted to the development of algorithms to estimate the intrinsic dimensionality ( id ) of a given dataset, that is the minimum number of parameters needed to represent the data without information ...

In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained considerable importance, since it is a relevant information for several real life applications. Unfortunately, although a great deal of research effort has ...

The high dimensionality of some real life signals makes the usage of the most common signal processing and pattern recognition methods unfeasible. For this reason, in literature a great deal of research work has been devoted to the development of ...

Most of the machine learning techniques suffer the "curse of dimensionality" effect when applied to high dimensional data. To face this limitation, a common preprocessing step consists in employing a dimensionality reduction technique. In literature, a ...

Most of the machine learning techniques suffer the "curse of dimensionality" effect when applied to high dimensional data. To face this limitation, a common preprocessing step consists in employing a dimensionality reduction technique. In literature, a ...

Objective: Computed tomography images are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies, and ...

This paper proposes a novel two-class classifier, called IPCAC, based on the Isotropic Principal Component Analysis technique; it allows to deal with training data drawn from Mixture of Gaussian distributions, by projecting the data on the Fisher ...

Magnetic resonance imaging (MRI) is becoming increasingly popular as a second-level technique, performed after ultrasonography (US) scanning, for detecting morphologic brain abnormalities. For this reason, several medical researchers in the past few ...

Objective: In the recent years liver segmentation from computed tomography scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver ...

Recently the tensor voting framework (<Literal>TVF</Literal> ), proposed by Medioni at al., has proved its effectiveness in perceptual grouping of arbitrary dimensional data. In the computer vision field, this algorithm has been applied to solve various ...

Computed tomography (CT) images are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D ...