Umberto Perron
Overview
Explore the profile of Umberto Perron including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
10
Citations
90
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Perron U, Grassi E, Chatzipli A, Viviani M, Karakoc E, Trastulla L, et al.
Nat Commun
. 2024 Nov;
15(1):9139.
PMID: 39528460
Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and...
2.
Najgebauer H, Perron U, Iorio F
Nat Comput Sci
. 2024 Jan;
1(1):22-23.
PMID: 38217149
No abstract available.
3.
Vinceti A, De Lucia R, Cremaschi P, Perron U, Karakoc E, Mauri L, et al.
Cell Rep Methods
. 2023 Feb;
3(1):100373.
PMID: 36814834
A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational...
4.
Vinceti A, Trastulla L, Perron U, Raiconi A, Iorio F
Bioinformatics
. 2023 Jan;
39(1).
PMID: 36669133
Motivation: Binary (or Boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are...
5.
Vinceti A, Perron U, Trastulla L, Iorio F
Cell Rep
. 2022 Jul;
40(4):111145.
PMID: 35905712
Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens-typically employing guide RNA libraries targeting...
6.
Vinceti A, Karakoc E, Pacini C, Perron U, De Lucia R, Garnett M, et al.
BMC Genomics
. 2021 Nov;
22(1):828.
PMID: 34789150
Background: CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from...
7.
Kalkauskas A, Perron U, Sun Y, Goldman N, Baele G, Guindon S, et al.
PLoS Comput Biol
. 2021 Jan;
17(1):e1008561.
PMID: 33406072
Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography-with location data provided in the...
8.
Weber C, Perron U, Casey D, Yang Z, Goldman N
Syst Biol
. 2020 May;
70(1):21-32.
PMID: 32353118
How can we best learn the history of a protein's evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints...
9.
Perron U, Kozlov A, Stamatakis A, Goldman N, Moal I
Mol Biol Evol
. 2019 May;
36(9):2086-2103.
PMID: 31114882
Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally aware empirical substitution model for amino...
10.
Perron U, Provero P, Molineris I
BMC Bioinformatics
. 2017 Apr;
18(Suppl 5):144.
PMID: 28361701
Background: In recent years long non coding RNAs (lncRNAs) have been the subject of increasing interest. Thanks to many recent functional studies, the existence of a large class of lncRNAs...