Biology subjects

Giampieri, E.

Publications and source records attributed to Giampieri, E..

4 recordsLinked to original sources

bioRxiv2023

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in Onco-Hematology

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genetically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In particular, Dirichlet processes have become the preferred method to approach the fit of mixture models. Usually, the multinomial distribution is at the core of such models. However, despite their advanced statistical formalism, these processes are not to be considered black box techniques and a better understanding of their working mechanisms enables to improve their employment and explainability. Focused on genomic data in Acute Myeloid Leukemia, this work unfolds the driving factors and rationale of the Hierarchical Dirichlet Mixture Models of multinomials on binary data. In addition, we introduce a novel approach to perform accurate patients clustering via multinomials based on statistical considerations. The newly reported adoption of the Multivariate Fishers Non-Central Hypergeometric distributions reveals promising results and outperformed the multinomials in clustering both on simulated and real onco-hematological data. Author summaryExplainable models are particularly attractive nowadays since they have the advantage to convince clinicians and patients. In this work we show that a deeper understanding of the Hierarchical Dirichlet Mixture Model, a non-black box method, can lead to better data modelling. In onco-hematology Hierarchical Dirichlet Mixture Models typically help to cluster molecular alterations rather than patients. Here, an intuitive statistical approach is presented to tackle patient classification based on the Hierarchical Dirichlet Mixture Models outcome. Additionally, molecular alterations are usually modelled by Hierarchical Dirichlet Mixture Models as a mixture of multinomial distributions. This work highlights that the alternative Fishers Non-Central Hypergeometric distribution can provide even better results and can give a higher priority to rare molecular alterations for patient classification.

cancer biology↗

bioRxiv2019

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

2.A possible way to tackle the crisis of antimicrobial resistance development is a strict policy when prescribing antibiotics. Thus, it is important that prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes (ARG). We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of any qnrS gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 92% of the test samples MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work goes further than the typical predictions that use machine learning as a black box model concept. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement. 3. Impact statementWhole genome sequencing has become the standard approach to study molecular epidemiology of bacteria. However, the application of WGS in the clinical microbiology laboratory as part of individual patient diagnostics still requires significant steps forward, in particular with respect to prediction of antibiotic susceptibility based on DNA sequence. Whilst the majority of studies of prediction of susceptibility have used a binary outcome (susceptible/resistant), a quantitative prediction of susceptibility, such as the MIC, will allow for earlier detection of trends in increasing resistance as well as the flexibility to follow potential adjustments in definitions of susceptible (wild type) and resistant (non-wild type) categories (breakpoints/ epidemiological cut-off values). 4. Data summaryIn this study, 704 E. coli genomes combined with MIC measurement for ciprofloxacin were analysed (24). Paired-end sequencing was performed on all isolates and the results were stored in FASTQ format. The isolates originated from five countries, Denmark, Italy, USA, UK, and Vietnam. The MIC distribution for these isolates is depicted in Table 1. Out of 704, 266 E. coli genomes had no country metadata available and were used as an independent test set. All data were deposited in the AMR Data Hub (24) which consists of raw sequencing data, ciprofloxacin minimum inhibitory concentrations, and additional metadata such as the origin of the samples. O_TBL View this table: org.highwire.dtl.DTLVardef@5a31d2org.highwire.dtl.DTLVardef@17cfa2eorg.highwire.dtl.DTLVardef@ce3ccorg.highwire.dtl.DTLVardef@3f6721org.highwire.dtl.DTLVardef@8bb604_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTABLE 1C_FLOATNO O_TABLECAPTIONThe collected and used data in the analysis grouped by country and MIC values. C_TABLECAPTION C_TBL Publicly available sequencing data was used from projects PRJEB21131, PRJNA266657, PRJNA292901, PRJNA292904, PRJNA292902, PRJDB7087, PRJEB21880, PRJEB21997, PRJEB14086 and PRJEB16326. Download and analysis scripts are available at https://github.com/patbaa/AMR_ciprofloxacin. iTOL phylogenetic tree is available at https://itol.embl.de/tree/14511722611491391569485969. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗

bioRxiv2019

DNetPRO: A network approach for low-dimensional signatures from high-throughput data

The objective of many high-throughput \"omics\" studies is to obtain a relatively low-dimensional set of observables - signature - for sample classification purposes (diagnosis, prognosis, stratification). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised signature identification method based on a bottom-up combinatorial approach that exploits the discriminant power of all variable pairs. The algorithm is easily scalable allowing efficient computing even for high number of observables (104 - 105). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or compares to them but with a smaller number of selected variables. Moreover the linearity of DNetPRO allows a clearer interpretation of the obtained signatures in comparison to non linear classification models

bioinformatics↗

bioRxiv2019

Ecological modeling of Long Interspersed elements reveals footprints of evolution and a role of chromatin in shaping their genome landscape in mammals.

Transposons are genome components that account for the majority of genome size in many organisms, behaving as parasitic entities and interfering with the translation mechanism. Chromatin structure influences the activity of transposons, by coordinating genome accessibility for the expression and insertion of these sequences. As a case study, we show evidences of an evolutionary response of the chromatin structure to a variation in the activity of Long Interspersed Elements (LINEs) during mammals evolution, with focus on the murine radiation and primate evolution. LINEs activity was measured using a biophysical approach for modeling LINEs as an ecosystem, where different strains of transposons might reproduce, die and compete for access to the translational machinery of the host. The model, based on the discrete stochastic processes of amplification and deactivation of LINEs copies, has been adapted to the data using Bayesian statistics to estimate its main parameters: rate of growth of transposons copy number and rate of past competition between transposons variants. This approach allows to estimate the activity of ancient LINE strains still present in the genome as deactivated components, and the possible competition among different strains. We leverage these results to highlight how the change in the chromatin structure of the murine species seems to be following an increase of LINEs activity during the appearance of the murine specific strain Lx. On the contrary, a similar response is absent in primates evolution, which follows a decrease of LINEs activity during the amplication of primate specific LIMA/LPB strains.

biophysics↗