A5008327028- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Cancer Genomics and Diagnostics
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
- Genomics and Phylogenetic Studies
- Biomedical Text Mining and Ontologies
- Cancer-related molecular mechanisms research
- Liver Disease Diagnosis and Treatment
- Machine Learning in Bioinformatics
- MicroRNA in disease regulation
- Hemoglobinopathies and Related Disorders
- Microbial Metabolic Engineering and Bioproduction
- Radiomics and Machine Learning in Medical Imaging
- Liver Disease and Transplantation
- Acute Myeloid Leukemia Research
- Gene Regulatory Network Analysis
- Receptor Mechanisms and Signaling
- Genomics and Rare Diseases
- Circular RNAs in diseases
- Cell death mechanisms and regulation
- Iron Metabolism and Disorders
- Genetics, Bioinformatics, and Biomedical Research
- Chronic Lymphocytic Leukemia Research
- Pancreatic function and diabetes
University of Turin
2020-2025
University of Padua
2009-2023
Azienda Ospedaliera Citta' della Salute e della Scienza di Torino
2022
Harvard University
2017-2019
Predicting the difference in thermodynamic stability between protein variants is crucial for design and understanding genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them trained or optimized on same 'all' available data, making a fair comparison unfeasible. Here, we introduce novel dataset, collected manually cleaned from latest version ThermoMutDB database, consisting 669 not included widely used training...
Objective Hyperferritinaemia is associated with liver fibrosis severity in patients metabolic dysfunction-associated steatotic disease (MASLD), but the longitudinal implications have not been thoroughly investigated. We assessed role of serum ferritin predicting long-term outcomes or death. Design evaluated relationship between baseline and events a multicentre cohort 1342 patients. Four survival models considering confounders non-invasive scoring systems were applied repeated five-fold...
Abstract The prediction of free energy changes upon protein residue variations is an important application in biophysics and biomedicine. Several methods have been developed to address this problem so far, including physical-based machine learning models. However, most the current computational tools, especially data-driven approaches, fail incorporate antisymmetric basic thermodynamic principle: a variation from wild-type mutated form structure ( <?CDATA $X_{W}\rightarrow X_{M}$?> <mml:math...
Hepatocellular carcinoma (HCC) is a highly lethal cancer and the second leading cause of cancer-related deaths worldwide. As demonstrated in other solid neoplasms HCC, infiltrating CD8
Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development effective classification and prognostication systems is crucial to improve the decision-making process drive innovative treatment strategies. We have created implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, personalized prognostic assessment in rare cancers. Clinical validation was performed on...
Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene changes and study the dynamics of their transcriptional activity in cell or response stimuli. Several methods for data selection, clustering functional analysis available; however, these steps usually performed independently, without exploiting integrating information derived from each step analysis.Here we present FunPat, an R package time series sequencing that...
Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools been developed predict free energy changes upon residue variations. Most these methods require both sequence structure information obtain reliable predictions. However, lower number structures available with respect their sequences, due experimental issues, drastically limits application tools. In addition, current methodologies ignore...
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy relatively low, it essential to promptly understand the course of better target patient's treatment. Predictive models for progression are thus great interest. One most extensive well-studied open-access data resources ALS Pooled Resource Open-Access Clinical Trials (PRO-ACT) repository. In 2015, DREAM-Phil Bowen Prediction Prize4Life Challenge...
An open challenge of computational and experimental biology is understanding the impact non-synonymous DNA variations on protein function and, subsequently, human health. The effects these variants stability can be measured as difference in free energy unfolding (ΔΔ G ) between mutated structure its wild-type form. Throughout years, bioinformaticians have developed a wide variety tools approaches to predict ΔΔ . Although performance highly variable, overall they are less accurate predicting...
Conventional/targeted chemotherapies and ionizing radiation (IR) are being used both as monotherapies in combination for the treatment of epithelial ovarian cancer (EOC). Several studies show that these therapies might favor oncogenic signaling impede anti-tumor responses. MiR-200c is considered a master regulator EOC-related oncogenes. In this study, we sought to investigate if chemotherapy IR could influence expression miR-200c-3p its target genes, like immune checkpoint PD-L1 other...
Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that “perturbation” of protein stability upon a missense variant (in terms absolute ΔΔG value, i.e., |ΔΔG|) significant, but not predictive, correlation with pathogenicity variant. However, here we show this becomes significantly amplified in haploinsufficient genes. Moreover, enrichment pathogenic increases at increasing perturbation value. These findings suggest might be considered...
Bacteria respond to nutrient starvation implementing the stringent response, a stress signaling system resulting in metabolic remodeling leading decreased growth rate and energy requirements. A well-characterized model of response Mycobacterium tuberculosis is one induced by low phosphate. The extracytoplasmic function (ECF) sigma factor SigE was previously suggested as having key role activation response. In this study, we challenge hypothesis analyzing temporal dynamics transcriptional...
Several computational pipelines for biomedical data have been proposed to stratify patients and predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering is an underexplored problem, current approaches limited applications, whose heterogeneous multimodal, with poor scalability high-dimensionality. We introduce VAE-Surv, a multimodal framework patients'...
Survival analysis, a foundational tool for modeling time-to-event data, has seen growing integration with machine learning (ML) approaches to handle the complexities of censored data and time-varying risks. Despite these advances, leveraging state-of-the-art survival models remains challenge due fragmented nature existing implementations, which lack standardized interfaces require extensive preprocessing. We introduce SurvHive, Python-based framework designed unify analysis methods within...
Abstract Predicting protein stability changes upon single‐point mutations is crucial in computational biology, with applications drug design, enzyme engineering, and understanding disease mechanisms. While deep‐learning approaches have emerged, many remain inaccessible for routine use. In contrast, potential‐like methods, including deep‐learning‐based ones, are faster, user‐friendly, effective estimating changes. However, most of them approximate Gibbs free‐energy differences without...
Abstract Epigenetic deregulation is a hallmark of cancer characterized by frequent acquisition new DNA methylation in CpG islands. To gain insight into the changes canine DLBCL, we investigated methylome primary DLBCLs comparison with control lymph nodes genome-wide microarray. We identified 1,194 target loci showing different levels tumors compared controls. The hypermethylated included promoter, 5′-UTRs, upstream and exonic regions. Interestingly, targets polycomb repressive complex stem...
Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches, especially when data is sensitive, such as medical applications. Federated a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival) an alternative approach that simplifies current state-of-the-art paradigm by allowing centres to generate local simulated instances from real then...
Motivation The identification of robust lists molecular biomarkers related to a disease is fundamental step for early diagnosis and treatment. However, methodologies the discovery using microarray data often provide results with limited overlap. These differences are imputable 1) dataset size (few subjects respect number features); 2) heterogeneity disease; 3) experimental protocols computational pipelines employed in analysis. In this paper, we focus on first two issues assess, both...