Demo Data from VLDB 2026 / reviewers in the wild / expert
Camila P. E. de Souza
dblp:284/4319
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-4465-5792ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › cancer genomics
mutational signature analysis |
0.6 | 1 | 2022 | SomaticSiMu: a mutational signature simulator · Bioinform. 2022 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | SomaticSiMu: a mutational signature simulatorabstractSUMMARY: SomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by (i) supervised machine learning classification of simulated sequences with different mutation types and burdens, and (ii) mutational signature extraction from simulated mutational catalogues. AVAILABILITY AND IMPLEMENTATION: SomaticSiMu is written in Python 3.8.3. The open-source code, documentation and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the CreativeCommonsAttribution4.0InternationalLicense. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Gurjit S. Randhawa, Maximillian P. M. Soltysiak, Camila P. E. de Souza, Lila Kari, Shiva M. Singh, Kathleen A. Hill |
Bioinform. | 4 |
| 2020 | Orthogonal Mixture of Hidden Markov Models
Negar Safinianaini, Camila P. E. de Souza, Henrik Boström, Jens Lagergren |
ECML/PKDD (1) | 2 |
| 2020 | Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation dataabstractWe present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal. Camila P. E. de Souza, Mirela Andronescu, Tehmina Masud, Farhia Kabeer, Justina Biele, Emma Laks, Daniel Lai, Patricia Ye, Jazmine Brimhall, Beixi Wang, Edmund Su, Tony Hui, Marcus Wong, Michelle Moksa, Richard A. Moore, Martin Hirst, Samuel Aparicio, Sohrab P. Shah |
PLoS Comput. Biol. | 1 |