Jessica Gliozzo

• Doctor of Philosophy
• Research Fellow at Università degli Studi di Milano

Motivation Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous and potentially high-dimensional data sources, such as multi-omics and clinical data. In literature several approa...

Recent advances in RNA technologies opened the avenue to the design of novel vaccines as witnessed by the success of the COVID-19 vaccine and also by new ongoing vaccines for cancer. New drugs based on non-coding RNA can also be developed at lower costs considering the relatively simple structure of these molecules with respect to classical recombi...

The “RNA world” represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient’s biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public reposito...

The advent of high-throughput sequencing technologies has revolutionized the field of multi-omics patient data analysis. While these techniques offer a wealth of information, they often generate datasets with dimensions far surpassing the number of available cases. This discrepancy in size gives rise to the challenging “small-sample-size” problem,...

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to the patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are continuously produced and available from public reposit...

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins” invite the application and adaptation of LLMs to protein modelling and design. Considering the...

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 therapies covering the full spectrum of the main human diseases. In the context of the “National Center for...

The integration of heterogeneous biological data into a common network representation is of paramount importance in different areas of biology and medicine. The size of the generated network in many cases prevents the possibility of its graphical visualization, inspection, and identification of characteristics. In this paper, we present the main fe...

Background Cis-regulatory regions (CRRs) are non-coding regions of the DNA that fine control the spatio-temporal pattern of transcription; they are involved in a wide range of pivotal processes such as the development of specific cell-lines/tissues and the dynamic cell response to physiological stimuli. Recent studies showed that genetic variants o...

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient o...

In the context of Genomic and Precision Medicine, prediction problems are often characterized by a high imbalance between classes and Big Data. This requires specialized tools, as traditional Machine Learning approaches may struggle with big datasets and often fail to predict the minority class with unbalanced classification problems.In this work w...

Motivation: Automated protein function prediction is a complex multi-class, multi-label, structured classification problem in which protein functions are organized in a controlled vocabulary, according to the Gene Ontology (GO). "Hierarchy-unaware" classifiers, also known as "flat" methods, predict GO terms without exploiting the inherent structur...

Wnt/Fzd signaling has been implicated in hematopoietic stem cell maintenance and in acute leukemia establishment. In our previous work we described a recurrent rearrangement involving the WNT10B locus (WNT10BR), characterized by the expression of WNT10BIVS1 transcript variant, in acute myeloid leukemia. To determine the occurrence of WNT10BR in T‐c...

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire n...

Background Histiocytoses are haematological disorders of bone marrow origin that share many biological and clinical features with haematological neoplasms. The association between histiocytoses of the cutaneous‐group and myeloid malignancies is a poorly investigated topic of high biological and clinical impact. Methods We performed a systematic re...

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 features directly extracted from DNA sequences, can predict active promoters and enhancers in specific ti...

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled...

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete’s behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it sugge...

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 Ontology terms) and on the edges (the strength of the relationship, the evidence of the source from wh...

DermoSprint is the first bilingual Italian-English, free of charge Dermatopathological Journal. It’s a friendly, easy to navigate, magazine for young dermatopathologists. Plenty of hyperlinks are available within the text. I believe it is the first PDF journal that offers its readers such rich, easy to access, content and HISTOPATHOLOGICAL DIGITAL...

Background: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nucl...

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and highly aggressive hematological malignancy with a poorly understood pathobiology and no effective therapeutic options. Despite a few recurrent genetic defects (e.g. single nucleotide changes, indels, large chromosomal aberrations) have been identified in BPDCN, none are disease‐spec...

Background: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, a...

Known pathogenic variants associated with genetic Mendelian diseases represent a tiny minority of the overall genetic variation that characterizes the human genome. In this context classical imbalance-aware machine learning methods are unable to distinguish pathogenic from benign variants, since they are severely biased toward the majority (benign)...

Cutaneous histiocytosis are bone-marrow derived disorders which may be considered to some extent as skin manifestation of dysmielopoiesis (i.e. result of an altered genetic background). Therefore, in some cases, histiocytosis patients are at increased risk of developing myeloid neoplasms such as myeloid leukemia and myeloproliferative/myelodysplast...

The high heterogeneity of Non-Langerhans Cell Histiocytosis shows how their differential diagnosis, prognostic stratification and therapeutic approach may be incredibly troublesome. Therefore, overlapping cases may benefit of a revision of nomenclature and classification, perhaps considering looser criteria (as already proposed for Langerhans-Cell...

Purpose: We investigate the frequency and features of the association between rare non-foamy, C-group, nonlangerhans- cell histiocytoses (NF-C-NLCH) and myeloid neoplasms (MN) from the scientific literature. Methods: We performed a literature search of published papers, with last query in May 2018, using four retrieval systems: PubMed, Scopus, Web...

Background Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalan...

In this thesis we present the novel semi-supervised network-based algorithm P-Net, which is able to rank and classify patients with respect to a specific phenotype or clinical outcome under study. The peculiar and innovative characteristic of this method is that it builds a network of samples/patients, where the nodes represent the samples and the...