Big data biomédico: La integración masiva de datos para … · 2020. 7. 21. · Ferran Sanz – GRIB (IMIM-UPF) Ferran Sanz . Programa de Investigación en Informática Biomédica

Ferran Sanz – GRIB (IMIM-UPF) Ferran Sanz – GRIB (IMIM-UPF)

Ferran Sanz

Programa de Investigación en Informática Biomédica (GRIB) Institut Hospital del Mar d’Investigacions Mèdiques (IMIM)

Universitat Pompeu Fabra Barcelona

Big data biomédico: La integración masiva de datos

para investigación


Clinical Data

Biomedical imaging

‘omics & Systems Biology

Drugs & other chemicals

Biomedical literature

Biomedical Big Data

HEALTH CARE PRACTICE


Biomedical Big Data

Clinical Data

Biomedical imaging




Millions of EHRs that that can be reused for research


Biomedical Big Data

Clinical Data

Biomedical imaging




Worldwide estimated medical imaging in 2020: 35 ZB S. Sarcar. GE Healthcare. http://es.slideshare.net/sarcar/data-explosion-in-medical-imaging


Clinical Data

Biomedical imaging




Biomedical Big Data

BIOMEDICAL RESEARCH


Clinical Data

Biomedical imaging




Biomedical Big Data

In May 2015, the European Genotype-Phenotype Archive (EGA) stored 1.8 PB of human ‘omics data


Biomedical Big Data

Clinical Data

Biomedical imaging




ChEMBL: 11K targets; 1.5M compounds; 14M activities


Biomedical Big Data

Clinical Data

Biomedical imaging




20+ million scientific papers referenced in PubMed®, and 700,000+ are added every year


Clinical Data

Biomedical imaging




BIMEDICAL BIG DATA

Health information in social media (Web 2.0) should not be forgotten


Clinical Data

Biomedical imaging




BIMEDICAL BIG DATA

Health information in social media (Web 2.0) should not be forgotten

80+% digital information available is not structured and is in multiple languages


Clinical Data

Biomedical imaging




Integration of heterogeneous biomedical information to gain a more complete and powerful view on diseases and therapeutics

INTEGRATIVE BIOINFORMATICS


Exploitation of the Biomedical Big Data in pharmacovigilance

EHR Db iv EHR

Db iii EHR Db ii EHR

db i

Data extraction and integration

Signal detection

Signal substantiation

In silico pharmacology

Pharmacoepidemiological analysis

Text mining

Stardardization & terminology mapping

Bioinformatics

Ferran Sanz – GRIB (IMIM-UPF)


From Bauer-Mehren A, Bundschus M, Rautschka M, Mayer MA, Sanz F, Furlong LI. PLoS One 2011; 6(6): e20284

Knowledge discovery by information linkage


Workflows for chemo-bioinformatic signal substantiation

Drug used Clinical adverse event

Proteins interacted


Data Silos

Different Standards

Large Volume

Need for resources that gather, standardize and integrate information on the genetic basis of diseases

Information on genetic basis of diseases



• A comprehensive resource on gene-disease associations (GDAs)

• Integrates information from publicly available databases and from the literature by text mining

• DisGeNET v4.0 (April 2016) contains 429,036 GDAs involving 17,381 genes and more than 15,000 diseases and phenotypes

• Freely available at: http://www.disgenet.org

http://www.disgenet.org


GWAS Catalog

OrphaNet

UniProt

CTD

LHGDN

CTD

Curated Predicted Literature

RGD

BEFREE

GAD

ClinVar

MGD

DisGeNET version 4.0: Data sources

Bio-Entity Finder and Relation Extraction


DisGeNET version 4.0: Statistics

Source Genes Diseases Associations

Curated 7,362 7,607 32,834

Predicted 2,743 2,064 10,264

Literature 16,141 11,447 403,925

All 17,381 15,093 429,036


DisGeNET version 4.0: Tools

Network Analysis Web interface

Semantic Web

Programmatic Access

R package Federated queries


DisGeNET version 4.0: Top scoring genes for Wilson disease

Gene Number

of diseases

DisGeNET score DSI DPI Number of

PMIDs Number of

SNPs

ATP7B 57 0.819 0.596 0.592 234 99 ANXA5 129 0.2 0.505 0.741 1 0 PRNP 205 0.128 0.468 0.962 4 1 CP 114 0.126 0.532 0.704 26 0 LOX 141 0.123 0.498 0.778 2 0 LOXL2 48 0.123 0.610 0.481 1 0 APOE 729 0.122 0.333 1 2 0 TNF 1524 0.120 0.247 1 2 0 IL6 1260 0.120 0.268 1 2 0 NDUFB7 1 0.120 1 0.148 1 0


DisGeNET version 4.0: Top scoring genes for Major Depression

Gene Number

of diseases

DisGeNET score DSI DPI Number of

PMIDs Number of

SNPs

SLC6A4 374 0.236 0.411 0.852 157 5 TPH2 89 0.211 0.548 0.667 26 1 HTR2A 222 0.155 0.463 0.778 45 17 PCLO 20 0.130 0.696 0.333 12 5 CRHR1 118 0.127 0.531 0.778 11 11 CYP2D6 316 0.127 0.428 0.852 11 2 FKBP5 78 0.126 0.563 0.814 16 1 SP4 16 0.125 0.739 0.296 3 1 GRM7 32 0.123 0.666 0.444 5 1 GNAI3 7 0.122 0.812 0.296 2 1


Integrative Biomedical Informatics Group:

Acknowledgements

http://grib.upf.edu

• L.I. Furlong • A. Bauer-Mehren (Roche) • A. Bravo

• A. Gutiérrez • J. Piñero • N. Queralt

http://grib.epf.edu


Thanks for your attention!

Documents

Big data biomédico: La integración masiva de datos para … · 2020. 7. 21. · Ferran Sanz – GRIB (IMIM-UPF) Ferran Sanz . Programa de Investigación en Informática Biomédica