LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Back to previous search Search history
Advanced search

Your last searches

  1. AU="De Falco, Antonio"
  2. AU="Plenter, R J"
  3. AU="Malarz, Janusz"

Search results

Result 1 - 2 of total 2

Search options

  1. Article ; Online: A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data.

    De Falco, Antonio / Caruso, Francesca / Su, Xiao-Dong / Iavarone, Antonio / Ceccarelli, Michele

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 1074

    Abstract: Single-cell RNA sequencing is the reference technology to characterize the composition of the tumor microenvironment and to study tumor heterogeneity at high resolution. Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast ... ...

    Abstract Single-cell RNA sequencing is the reference technology to characterize the composition of the tumor microenvironment and to study tumor heterogeneity at high resolution. Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single-cell RNA-seq data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to datasets encompassing 106 samples and 93,322 cells from different tumor types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.
    MeSH term(s) Humans ; DNA Copy Number Variations/genetics ; Single-Cell Gene Expression Analysis ; Algorithms ; Brain Neoplasms ; Single-Cell Analysis/methods ; Sequence Analysis, RNA/methods ; Tumor Microenvironment/genetics
    Language English
    Publishing date 2023-02-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-36790-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets.

    de Falco, Antonio / Dezso, Zoltan / Ceccarelli, Francesco / Cerulo, Luigi / Ciaramella, Angelo / Ceccarelli, Michele

    Bioinformatics (Oxford, England)

    2020  Volume 37, Issue 10, Page(s) 1420–1427

    Abstract: Motivation: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most ... ...

    Abstract Motivation: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.
    Results: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates.
    Availability and implementation: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Drug Development ; Drug Discovery ; Pharmaceutical Preparations ; Proteins ; Software
    Chemical Substances Pharmaceutical Preparations ; Proteins
    Language English
    Publishing date 2020-10-03
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btaa968
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top