Browsing by Author "Navarro, Gonzalo"

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    Fast and compact planar embeddings
    (2020) Ferres, Leo; Fuentes-Sepúlveda, José; Gagie, Travis; He, Meng; Navarro, Gonzalo
    There are many representations of planar graphs, but few are as elegant as Turán’s (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multiedges, and can store any specified embedding. Its main disadvantage has been that “it does not allow efficient searching” (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turán’s representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50% for large datasets.
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    Fast and Compact Planar Embeddings
    (2019) Ferres, Leo; Fuentes-Sepúlveda, José; Gagie, Travis; He, Meng; Navarro, Gonzalo
    There are many representations of planar graphs, but few are as elegant as Turán’s (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multi-edges, and can store any specified embedding. Its main disadvantage has been that “it does not allow efficient searching” (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turán’s representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50% for large datasets.
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    Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients
    (2022) Oróstica, Karen; Saez, Juan; De Santiago, Pamela; Rivas, Solange; Contreras, Sebastián; Navarro, Gonzalo; Asenjo, Juan; Olivera, Álvaro; Armisén, Ricardo
    Abstract Background: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohortsof cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), sum‑marises the mutational and clinical profles of diferent subtypes of Lung Cancer (LC). Mutational and clinical signa‑ tures have been used independently for tumour typifcation and prediction of metastasis in LC patients. Is it then possible to achieve better typifcations and predictions when combining both data streams? Methods: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for diferent classes of patients. Using the TML and diferent sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classifcation models that calculate the likelihood of developing metastasis. Results: We found that LC patients diferent in age, smoking status, and tumour type had signifcantly diferent mean TMLs. Although TML was an informative feature, its efect was secondary to the "tumour stage" feature. However, its contribution to the classifcation is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest perfor‑mance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). Conclusions: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evi‑ dence of the need for comprehensive diagnostic tools for metastasis.

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