Luciana Ferrer

Bio: Luciana Ferrer is an academic researcher from National Scientific and Technical Research Council. The author has contributed to research in topics: Speaker recognition & Speaker diarisation. The author has an hindex of 31, co-authored 125 publications receiving 6235 citations. Previous affiliations of Luciana Ferrer include Torcuato di Tella University & University of Buenos Aires.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Abstract: The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.

A novel scheme for speaker recognition using a phonetically-aware deep neural network

Abstract: We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR) Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments The use of an ASR-DNN system in the speaker recognition pipeline is attractive as it integrates the information from speech content directly into the statistics, allowing the standard backends to remain unchanged Improvement from the proposed framework compared to a state-of-the-art system are of 30% relative at the equal error rate when evaluated on the telephone conditions from the 2012 NIST speaker recognition evaluation (SRE) The proposed framework is a successful way to efficiently leverage transcribed data for speaker recognition, thus opening up a wide spectrum of research directions

The Speakers in the Wild (SITW) Speaker Recognition Database.

Abstract: The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or “wild” conditions. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. Unlike existing databases for speaker recognition, this data was not collected under controlled conditions and thus contains real noise, reverberation, intraspeaker variability and compression artifacts. These factors are often convolved in the real world, as the SITW data shows, and they make SITW a challenging database for singleand multispeaker recognition

MLLR transforms as features in speaker recognition.

Abstract: We explore the use of adaptation transforms employed in speech recognition systems as features for speaker recognition. This approach is attractive because, unlike standard framebased cepstral speaker recognition models, it normalizes for the choice of spoken words in text-independent speaker verification. Affine transforms are computed for the Gaussian means of the acoustic models used in a recognizer, using maximum likelihood linear regression (MLLR). The high-dimensional vectors formed by the transform coefficients are then modeled as speaker features using support vector machines (SVMs). The resulting speaker verification system is competitive, and in some cases significantly more accurate, than state-of-the-art cepstral gaussian mixture and SVM systems. Further improvements are obtained by combining baseline and MLLR-based systems.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Reactome Pathway Knowledgebase.

Abstract: The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Abstract: Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients

Abstract: Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy (n = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples (n = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity (P < 0.01) and relative abundance of bacteria of the Ruminococcaceae family (P < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.