Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms
Abstract
Myeloid neoplasms (MN) present clinical and molecular heterogeneity and therefore a risk-adapted treatment strategy is mandatory. In MN, classification and prognostic tools based on clinical and morphologic criteria are being complemented by introducing genomic features. The clinical implementation of next-generation classifications and prognostic systems requires the availability of a robust methodological framework together with a solution to provide access to these technologies for clinicians.
Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization
Abstract
In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science.
Clinical relevance of clonal hematopoiesis in persons aged ≥80 years
The first official publication for GenoMed4All is out! The article has been published at the Blood journal from the American Society of Hematology, under the title Clinical relevance of clonal hematopoiesis in the oldest-old population. The focus is on the general elderly population (80 year-olds and above) and the paper aims to correlate genomic profiles to the risk of developing MDS (Myelodysplastic Syndromes) and other haematological malignancies.
Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. This phenomenon becomes common in persons aged ≥80 years, in whom the implications of CHIP are not well defined. We performed a mutational screening in 1794 persons aged ≥80 years and investigated the relationships between CHIP and associated pathologies.
Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes
Abstract
Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication.
Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes
Abstract
Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.
A Sex-Informed Approach to Improve Prognostication and Personalized Decision-Making Process in Myelodysplastic Syndromes
Abstract
Sex represents a major source of diversity among patients in terms of pathophysiology, clinical presentation, prognosis and response to therapy, and therefore sex (gender)-informed medicine is becoming a new paradigm to refine clinical decision making process in different human diseases. Myelodysplastic syndromes (MDS) are heterogeneous disease characterized by ineffective hematopoiesis and risk of leukemic evolution. We aimed to study clinical effect of sex in MDS as a basis to improve patient prognostication and personalized treatment.