Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 183
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Mol Metab ; 30: 16-29, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31767167

RESUMO

OBJECTIVE: Translation of basic research from bench-to-bedside relies on a better understanding of similarities and differences between mouse and human cell biology, tissue formation, and organogenesis. Thus, establishing ex vivo modeling systems of mouse and human pancreas development will help not only to understand evolutionary conserved mechanisms of differentiation and morphogenesis but also to understand pathomechanisms of disease and design strategies for tissue engineering. METHODS: Here, we established a simple and reproducible Matrigel-based three-dimensional (3D) cyst culture model system of mouse and human pancreatic progenitors (PPs) to study pancreatic epithelialization and endocrinogenesis ex vivo. In addition, we reanalyzed previously reported single-cell RNA sequencing (scRNA-seq) of mouse and human pancreatic lineages to obtain a comprehensive picture of differential expression of key transcription factors (TFs), cell-cell adhesion molecules and cell polarity components in PPs during endocrinogenesis. RESULTS: We generated mouse and human polarized pancreatic epithelial cysts derived from PPs. This system allowed to monitor establishment of pancreatic epithelial polarity and lumen formation in cellular and sub-cellular resolution in a dynamic time-resolved fashion. Furthermore, both mouse and human pancreatic cysts were able to differentiate towards the endocrine fate. This differentiation system together with scRNA-seq analysis revealed how apical-basal polarity and tight and adherens junctions change during endocrine differentiation. CONCLUSIONS: We have established a simple 3D pancreatic cyst culture system that allows to tempo-spatial resolve cellular and subcellular processes on the mechanistical level, which is otherwise not possible in vivo.

2.
Genome Biol ; 20(1): 183, 2019 09 02.
Artigo em Inglês | MEDLINE | ID: mdl-31477158

RESUMO

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.

3.
Genome Biol ; 20(1): 155, 2019 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-31387612

RESUMO

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/ß-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.


Assuntos
Perfilação da Expressão Gênica/métodos , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Neoplasias da Mama/genética , Análise Custo-Benefício , Células-Tronco Embrionárias/metabolismo , Feminino , Perfilação da Expressão Gênica/economia , Genômica/economia , Sequenciamento de Nucleotídeos em Larga Escala/economia , Humanos , Células-Tronco Pluripotentes/metabolismo , Análise de Sequência de RNA/economia , Análise de Célula Única/economia , Análise de Célula Única/métodos , Via de Sinalização Wnt , Fluxo de Trabalho
4.
Nat Methods ; 16(8): 715-721, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31363220

RESUMO

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.


Assuntos
Algoritmos , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Aprendizado de Máquina , Fagócitos/metabolismo , Análise de Célula Única/métodos , Transcriptoma , Animais , Simulação por Computador , Perfilação da Expressão Gênica , Humanos , Leucócitos Mononucleares/citologia , Camundongos , Fagócitos/citologia , Especificidade da Espécie
5.
Cytometry A ; 95(9): 952-965, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31313519

RESUMO

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

6.
Front Immunol ; 10: 1515, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31354705

RESUMO

Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.

7.
Nature ; 571(7765): 419-423, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31292545

RESUMO

Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease1. However, current scRNA-seq approaches provide only a snapshot of gene expression and convey little information on the true temporal dynamics and stochastic nature of transcription. A further key limitation of scRNA-seq analysis is that the RNA profile of each individual cell can be analysed only once. Here we introduce single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing (scSLAM-seq), which integrates metabolic RNA labelling2, biochemical nucleoside conversion3 and scRNA-seq to record transcriptional activity directly by differentiating between new and old RNA for thousands of genes per single cell. We use scSLAM-seq to study the onset of infection with lytic cytomegalovirus in single mouse fibroblasts. The cell-cycle state and dose of infection deduced from old RNA enable dose-response analysis based on new RNA. scSLAM-seq thereby both visualizes and explains differences in transcriptional activity at the single-cell level. Furthermore, it depicts 'on-off' switches and transcriptional burst kinetics in host gene expression with extensive gene-specific differences that correlate with promoter-intrinsic features (TBP-TATA-box interactions and DNA methylation). Thus, gene-specific, and not cell-specific, features explain the heterogeneity in transcriptomes between individual cells and the transcriptional response to perturbations.


Assuntos
Regulação da Expressão Gênica/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única , Transcrição Genética/genética , Alquilação , Animais , Ciclo Celular , Citomegalovirus/fisiologia , Metilação de DNA , Fibroblastos/metabolismo , Fibroblastos/virologia , Cinética , Camundongos , Regiões Promotoras Genéticas/genética , RNA/análise , RNA/química , Compostos de Sulfidrila/química
8.
Development ; 146(12)2019 Jun 27.
Artigo em Inglês | MEDLINE | ID: mdl-31249007

RESUMO

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

9.
Mol Syst Biol ; 15(6): e8746, 2019 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-31217225

RESUMO

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

10.
Nat Commun ; 10(1): 2548, 2019 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-31186427

RESUMO

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike's information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.


Assuntos
Metilação de DNA/genética , DNA/sangue , Interação Gene-Ambiente , Estudos de Coortes , Epigênese Genética , Feminino , Sangue Fetal , Genótipo , Humanos , Recém-Nascido , Masculino , Gravidez , Fatores de Risco
11.
Development ; 146(12)2019 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-31160421

RESUMO

Deciphering mechanisms of endocrine cell induction, specification and lineage allocation in vivo will provide valuable insights into how the islets of Langerhans are generated. Currently, it is ill defined how endocrine progenitors segregate into different endocrine subtypes during development. Here, we generated a novel neurogenin 3 (Ngn3)-Venus fusion (NVF) reporter mouse line, that closely mirrors the transient endogenous Ngn3 protein expression. To define an in vivo roadmap of endocrinogenesis, we performed single cell RNA sequencing of 36,351 pancreatic epithelial and NVF+ cells during secondary transition. This allowed Ngn3 low endocrine progenitors, Ngn3 high endocrine precursors, Fev+ endocrine lineage and hormone+ endocrine subtypes to be distinguished and time-resolved, and molecular programs during the step-wise lineage restriction steps to be delineated. Strikingly, we identified 58 novel signature genes that show the same transient expression dynamics as Ngn3 in the 7260 profiled Ngn3-expressing cells. The differential expression of these genes in endocrine precursors associated with their cell-fate allocation towards distinct endocrine cell types. Thus, the generation of an accurately regulated NVF reporter allowed us to temporally resolve endocrine lineage development to provide a fine-grained single cell molecular profile of endocrinogenesis in vivo.

12.
Nat Med ; 25(7): 1153-1163, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31209336

RESUMO

Human lungs enable efficient gas exchange and form an interface with the environment, which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single-cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in healthy lungs, and lower airways in asthmatic lungs. We report location-dependent airway epithelial cell states and a novel subset of tissue-resident memory T cells. In the lower airways of patients with asthma, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report the presence of pathogenic effector type 2 helper T cells (TH2) in asthmatic lungs and find evidence for type 2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell-cell interactions identifies a shift from airway structural cell communication in healthy lungs to a TH2-dominated interactome in asthmatic lungs.


Assuntos
Asma/patologia , Pulmão/citologia , Adulto , Idoso , Linfócitos T CD4-Positivos/fisiologia , Comunicação Celular , Células Epiteliais/imunologia , Células Epiteliais/fisiologia , Feminino , Estudo de Associação Genômica Ampla , Células Caliciformes/metabolismo , Humanos , Pulmão/imunologia , Pulmão/patologia , Masculino , Metaplasia , Pessoa de Meia-Idade , Células Th2/fisiologia , Transcriptoma
13.
Nature ; 569(7756): 342-343, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31076731
14.
PLoS One ; 14(5): e0216110, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31120904

RESUMO

BACKGROUND: Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype. METHODS AND FINDINGS: We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates. CONCLUSIONS: Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.

15.
Am J Respir Cell Mol Biol ; 61(1): 31-41, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30995076

RESUMO

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.

16.
Nat Rev Genet ; 20(7): 389-403, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30971806

RESUMO

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.


Assuntos
Aprendizado Profundo , Genômica/métodos , Modelos Genéticos , Redes Neurais (Computação) , Sequência de Bases , Simulação por Computador , Humanos , Aprendizado de Máquina Supervisionado , Aprendizado de Máquina não Supervisionado
17.
Nat Biotechnol ; 37(4): 461-468, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30936567

RESUMO

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.


Assuntos
Diferenciação Celular/genética , Análise de Sequência de RNA/estatística & dados numéricos , Análise de Célula Única/estatística & dados numéricos , Animais , Apoptose/genética , Biotecnologia , Proliferação de Células/genética , Feminino , Células Secretoras de Insulina/citologia , Células Secretoras de Insulina/metabolismo , Funções Verossimilhança , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Modelos Biológicos , Células-Tronco Embrionárias Murinas/citologia , Células-Tronco Embrionárias Murinas/metabolismo , Linfócitos T/citologia , Linfócitos T/metabolismo , Fatores de Tempo
18.
Nat Commun ; 10(1): 963, 2019 02 27.
Artigo em Inglês | MEDLINE | ID: mdl-30814501

RESUMO

Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. Here, we use single cell transcriptomics and mass spectrometry-based proteomics to quantify changes in cellular activity states across 30 cell types and chart the lung proteome of young and old mice. We show that aging leads to increased transcriptional noise, indicating deregulated epigenetic control. We observe cell type-specific effects of aging, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts and altered relative frequency of airway epithelial cells as hallmarks of lung aging. Proteomic profiling reveals extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and collagen XIV. Computational integration of the aging proteome with the single cell transcriptomes predicts the cellular source of regulated proteins and creates an unbiased reference map of the aging lung.


Assuntos
Envelhecimento/genética , Envelhecimento/metabolismo , Pulmão/metabolismo , Envelhecimento/patologia , Animais , Colesterol/biossíntese , Colágeno/metabolismo , Células Epiteliais/metabolismo , Matriz Extracelular/metabolismo , Perfilação da Expressão Gênica , Pulmão/citologia , Camundongos , Camundongos Endogâmicos C57BL , Proteoma/metabolismo , Proteômica , Análise de Célula Única
19.
Genome Biol ; 20(1): 59, 2019 03 19.
Artigo em Inglês | MEDLINE | ID: mdl-30890159

RESUMO

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.


Assuntos
Biologia Computacional/métodos , Gráficos por Computador , Regulação da Expressão Gênica no Desenvolvimento , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Algoritmos , Animais , Embrião não Mamífero/citologia , Embrião não Mamífero/metabolismo , Células-Tronco Hematopoéticas/citologia , Células-Tronco Hematopoéticas/metabolismo , Humanos , Planárias/citologia , Planárias/genética , Padrões de Referência , Software , Peixe-Zebra/crescimento & desenvolvimento , Peixe-Zebra/metabolismo
20.
Allergy ; 74(7): 1364-1373, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30737985

RESUMO

BACKGROUND: Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high-dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them. METHODS: We assembled questionnaire, diagnostic, genotype, microarray, RT-qPCR, flow cytometry, and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild-to-moderate allergic asthmatics, and nonallergic asthmatics. Based on data from 260 German children aged 4-14 from our university outpatient clinic, we built a novel multilevel prediction approach for asthma outcome which could deal with a present complex missing data structure. RESULTS: The optimal learning method was boosting based on all data sets, achieving an area underneath the receiver operating characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%-confidence interval (CI): 0.65-0.94) using leave-one-out cross-validation. Besides improving the AUC, our integrative multilevel learning approach led to tighter CIs than using smaller complete predictor data sets (AUC = 0.82 [0.66-0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental). CONCLUSION: Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multilevel data-based risk prediction settings, which typically suffer from incomplete data.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA