Alessandro Verri
Overview
Explore the profile of Alessandro Verri including associated specialties, affiliations and a list of published articles.
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Articles
26
Citations
224
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Recent Articles
1.
Turrisi R, Verri A, Barla A
Front Comput Neurosci
. 2024 Oct;
18:1360095.
PMID: 39371524
Introduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best...
2.
Rando M, James M, Verri A, Rosasco L, Seminara A
ArXiv
. 2024 May;
PMID: 38711433
We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior information...
3.
Cilloni D, Petiti J, Campia V, Podesta M, Squillario M, Montserrat N, et al.
J Clin Med
. 2020 Jun;
9(6).
PMID: 32492887
During the phase of proliferation needed for hematopoietic reconstitution following transplantation, hematopoietic stem/progenitor cells (HSPC) must express genes involved in stem cell self-renewal. We investigated the expression of genes relevant...
4.
Brichetto G, Bragadin M, Fiorini S, Battaglia M, Konrad G, Ponzio M, et al.
Neurol Sci
. 2019 Oct;
41(2):459-462.
PMID: 31659583
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML...
5.
Fiorini S, Martini C, Malpassi D, Cordera R, Maggi D, Verri A, et al.
Annu Int Conf IEEE Eng Med Biol Soc
. 2017 Oct;
2017:1680-1683.
PMID: 29060208
Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online...
6.
Squillario M, Barbieri M, Verri A, Barla A
Microarrays (Basel)
. 2016 Sep;
5(2).
PMID: 27600081
Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses...
7.
Fiorini S, Verri A, Tacchino A, Ponzio M, Brichetto G, Barla A
Annu Int Conf IEEE Eng Med Biol Soc
. 2016 Jan;
2015:4443-6.
PMID: 26737281
In this work we present a machine learning pipeline for the detection of multiple sclerosis course from a collection of inexpensive and non-invasive measures such as clinical scales and patient-reported...
8.
Masecchia S, Coco S, Barla A, Verri A, Tonini G
BMC Med Genomics
. 2015 Sep;
8:57.
PMID: 26358114
Background: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4...
9.
Stagliano A, Noceti N, Verri A, Odone F
IEEE Trans Image Process
. 2015 Apr;
24(8):2415-28.
PMID: 25872209
In this paper, we propose a sparse coding approach to background modeling. The obtained model is based on dictionaries which we learn and keep up to date as new data...
10.
Tortolina L, Duffy D, Maffei M, Castagnino N, Carmody A, Kolch W, et al.
Oncotarget
. 2015 Feb;
6(7):5041-58.
PMID: 25671297
The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple...