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1.
Int J Neural Syst ; 32(9): 2250043, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-35912583

RESUMO

A practical problem in supervised deep learning for medical image segmentation is the lack of labeled data which is expensive and time-consuming to acquire. In contrast, there is a considerable amount of unlabeled data available in the clinic. To make better use of the unlabeled data and improve the generalization on limited labeled data, in this paper, a novel semi-supervised segmentation method via multi-task curriculum learning is presented. Here, curriculum learning means that when training the network, simpler knowledge is preferentially learned to assist the learning of more difficult knowledge. Concretely, our framework consists of a main segmentation task and two auxiliary tasks, i.e. the feature regression task and target detection task. The two auxiliary tasks predict some relatively simpler image-level attributes and bounding boxes as the pseudo labels for the main segmentation task, enforcing the pixel-level segmentation result to match the distribution of these pseudo labels. In addition, to solve the problem of class imbalance in the images, a bounding-box-based attention (BBA) module is embedded, enabling the segmentation network to concern more about the target region rather than the background. Furthermore, to alleviate the adverse effects caused by the possible deviation of pseudo labels, error tolerance mechanisms are also adopted in the auxiliary tasks, including inequality constraint and bounding-box amplification. Our method is validated on ACDC2017 and PROMISE12 datasets. Experimental results demonstrate that compared with the full supervision method and state-of-the-art semi-supervised methods, our method yields a much better segmentation performance on a small labeled dataset. Code is available at https://github.com/DeepMedLab/MTCL.


Assuntos
Currículo , Aprendizado de Máquina Supervisionado , Curadoria de Dados/métodos , Curadoria de Dados/normas , Conjuntos de Dados como Assunto/normas , Conjuntos de Dados como Assunto/provisão & distribuição , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina Supervisionado/classificação , Aprendizado de Máquina Supervisionado/estatística & dados numéricos , Aprendizado de Máquina Supervisionado/tendências
2.
Database (Oxford) ; 20222022 07 14.
Artigo em Inglês | MEDLINE | ID: mdl-35849014

RESUMO

In silico chromosome painting is a technique by which contributions of distinct genetic groups are represented along chromosomes of hybrid individuals. This type of analysis is used to study the mechanisms by which these individuals were formed. Such techniques are well adapted to identify genetic groups contributing to these individuals as well as hybridization events. It can also be used to follow chromosomal recombinations that occurred naturally or were generated by selective breeding. Here, we present GeMo, a novel interactive web-based and user-oriented interface to visualize in a linear-based fashion results of in silico chromosome painting. To facilitate data input generation, a script to execute analytical commands is provided and an interactive data curation mode is supported to ensure consistency of the automated procedure. GeMo contains preloaded datasets from published studies on crop domestication but can be applied to other purposes, such as breeding programs Although only applied so far on plants, GeMo can handle data from animals as well. Database URL: https://gemo.southgreen.fr/.


Assuntos
Curadoria de Dados , Interface Usuário-Computador , Animais , Cromossomos , Bases de Dados Factuais , Internet
3.
Methods Mol Biol ; 2505: 87-100, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35732939

RESUMO

In less than 10 years, molecular networking (MN) strategy has revolutionized the art of Natural Products (NP) isolation to enter a rational workflow greatly increasing the probabilities of isolating new chemical entities. To pinpoint and streamline the isolation of new Monoterpene Indole Alkaloids (MIAs) in producing plants, we rendered publicly available the MIA database (MIADB), comprising MS2 data for ca. 200 structurally diverse MIA, by uploading it to the Global Natural Products Social Molecular Networking (GNPS) platform. Here, we describe the key experimental aspects underlying data collection, data curation, and their subsequent upload to the GNPS libraries as a database. Practical tips are also provided at the end of this chapter to help optimizing the efficiency of the dereplication of MIA-containing plants against the MIADB-implemented GNPS library.


Assuntos
Produtos Biológicos , Espectrometria de Massas em Tandem , Produtos Biológicos/química , Curadoria de Dados , Alcaloides Indólicos/química , Monoterpenos , Plantas
4.
Metabolomics ; 18(6): 40, 2022 06 14.
Artigo em Inglês | MEDLINE | ID: mdl-35699774

RESUMO

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .


Assuntos
Metabolômica , Metadados , Curadoria de Dados/métodos , Espectrometria de Massas/métodos , Metaboloma , Metabolômica/métodos
5.
Gigascience ; 112022 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-35701374

RESUMO

The increasingly multidisciplinary nature of scientific research necessitates a need for Open Data repositories that can archive data in support of publications in scientific journals. Recognising this need, even before GigaScience launched in 2012, GigaDB was already in place and taking data for a year before (making it 11 this year). Since GigaDB launched, there has been a consistent growth in this resource in terms of data volume, data discoverability and data re-use. In this commentary, we provide a retrospective of key changes over the last decade, and the role of Data Curation in enhancing the user experience. Furthermore we explore a much needed emphasis on enabling researchers to interact with and explore datasets prior to data download.


Assuntos
Curadoria de Dados , Estudos Retrospectivos
6.
Sci Rep ; 12(1): 7686, 2022 05 10.
Artigo em Inglês | MEDLINE | ID: mdl-35538137

RESUMO

Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.


Assuntos
Eletroencefalografia , Sono , Curadoria de Dados , Eletroencefalografia/métodos , Humanos , Redes Neurais de Computação , Reprodutibilidade dos Testes , Sono/fisiologia , Fases do Sono/fisiologia
7.
Clin Imaging ; 87: 34-37, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35483162

RESUMO

Although natural language processing (NLP) can rapidly extract disease labels from radiology reports to create datasets for deep learning models, this may be less accurate than having radiologists manually review the images. In this study, we compared agreement between natural language processing (NLP) and radiologist-curated labels for possible tuberculosis (TB) on chest radiographs (CXR) and evaluated the performance of deep convolutional neural networks (DCNN) trained to identify TB using the preceding two sets of labels. We collected 10,951 CXRs from the NIH ChestX-ray14 dataset and labeled them as positive or negative for possible TB based on two methods: 1) NLP-derived disease labels and 2) radiologist-review of images. These images were used to train DCNNs on varying dataset sizes for possible TB and tested on an external dataset of 800 CXRs. Area under the ROC curve (AUC) was used to evaluate DCNNs. There was poor agreement between NLP and radiologist-curated labels for potential TB (Kappa coefficient 0.34). DCNNs trained using radiologist-curated labels had higher performance than the algorithm trained using the NLP-labels, regardless of the number of images used for training. The best-performing DCNN had an AUC of 0.88, which was trained on 10,951 images using the radiologist-annotated sets. DCNNs trained on CXRs labeled by a radiologist consistently outperformed those trained on the same CXRs labeled by NLP, highlighting the benefit of radiologists' determining groundtruth for machine learning dataset curation.


Assuntos
Aprendizado Profundo , Curadoria de Dados , Humanos , Processamento de Linguagem Natural , Radiografia Torácica/métodos , Radiologistas , Estudos Retrospectivos
8.
Med Image Anal ; 79: 102437, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-35427898

RESUMO

We propose a semi-supervised learning approach to annotate a dataset with reduced requirements for manual annotation and with controlled annotation error. The method is based on feature-space projection and label propagation using local quality metrics. First, an auto-encoder extracts the features of the samples in an unsupervised manner. Then, the extracted features are projected by a t-distributed stochastic neighbor embedding algorithm into a two-dimensional (2D) space. A selection of the best 2D projection is introduced based on the silhouette score. The expert annotator uses the obtained 2D representation to manually label samples. Finally, the labels of the labeled samples are propagated to the unlabeled samples using a K-nearest neighbor strategy and local quality metrics. We compare our method against semi-supervised optimum-path forest and K-nearest neighbor label propagation (without considering local quality metrics). Our method achieves state-of-the-art results on three different datasets by labeling more than 96% of the samples with an annotation error from 7% to 17%. Additionally, our method allows to control the trade-off between annotation error and number of labeled samples. Moreover, we combine our method with robust loss functions to compensate for the label noise introduced by automatic label propagation. Our method allows to achieve similar, and even better, classification performances compared to those obtained using a fully manually labeled dataset, with up to 6% in terms of classification accuracy.


Assuntos
Curadoria de Dados , Embolia Intracraniana , Algoritmos , Benchmarking , Humanos , Aprendizado de Máquina Supervisionado
9.
Sensors (Basel) ; 22(7)2022 Apr 04.
Artigo em Inglês | MEDLINE | ID: mdl-35408389

RESUMO

Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation approaches: their limited efficiency, training set bias and lack of novelty detection. MorphoCluster uses clustering and similarity search to enable efficient, computer-assisted image annotation. In this work, we provide a deeper analysis of this approach. We simulate the actions of a MorphoCluster user to avoid extensive manual annotation runs. This simulation is used to test supervised, unsupervised and transfer representation learning approaches. Furthermore, shrunken k-means and partially labeled k-means, two new clustering algorithms that are tailored specifically for the MorphoCluster approach, are compared to the previously used HDBSCAN*. We find that labeled training data improve the image representations, that unsupervised learning beats transfer learning and that all three clustering algorithms are viable options, depending on whether completeness, efficiency or runtime is the priority. The simulation results support our earlier finding that MorphoCluster is very efficient and precise. Within the simulation, more than five objects per simulated click are being annotated with 95% precision.


Assuntos
Benchmarking , Curadoria de Dados , Algoritmos , Análise por Conglomerados , Computadores , Processamento de Imagem Assistida por Computador/métodos
10.
Sensors (Basel) ; 22(8)2022 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-35458823

RESUMO

The performance of deep neural networks and the low costs of computational hardware has made computer vision a popular choice in many robotic systems. An attractive feature of deep-learned methods is their ability to cope with appearance changes caused by day-night cycles and seasonal variations. However, deep learning of neural networks typically relies on large numbers of hand-annotated images, which requires significant effort for data collection and annotation. We present a method that allows autonomous, self-supervised training of a neural network in visual teach-and-repeat (VT&R) tasks, where a mobile robot has to traverse a previously taught path repeatedly. Our method is based on a fusion of two image registration schemes: one based on a Siamese neural network and another on point-feature matching. As the robot traverses the taught paths, it uses the results of feature-based matching to train the neural network, which, in turn, provides coarse registration estimates to the feature matcher. We show that as the neural network gets trained, the accuracy and robustness of the navigation increases, making the robot capable of dealing with significant changes in the environment. This method can significantly reduce the data annotation efforts when designing new robotic systems or introducing robots into new environments. Moreover, the method provides annotated datasets that can be deployed in other navigation systems. To promote the reproducibility of the research presented herein, we provide our datasets, codes and trained models online.


Assuntos
Mãos , Redes Neurais de Computação , Curadoria de Dados , Reprodutibilidade dos Testes , Projetos de Pesquisa
11.
Nat Commun ; 13(1): 1161, 2022 03 04.
Artigo em Inglês | MEDLINE | ID: mdl-35246539

RESUMO

Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have a confounding effect on the assessment of model performance. Nevertheless, employing experts to remove label noise by fully re-annotating large datasets is infeasible in resource-constrained settings, such as healthcare. This work advocates for a data-driven approach to prioritising samples for re-annotation-which we term "active label cleaning". We propose to rank instances according to estimated label correctness and labelling difficulty of each sample, and introduce a simulation framework to evaluate relabelling efficacy. Our experiments on natural images and on a specifically-devised medical imaging benchmark show that cleaning noisy labels mitigates their negative impact on model training, evaluation, and selection. Crucially, the proposed approach enables correcting labels up to 4 × more effectively than typical random selection in realistic conditions, making better use of experts' valuable time for improving dataset quality.


Assuntos
Diagnóstico por Imagem , Aprendizado de Máquina , Benchmarking , Curadoria de Dados , Atenção à Saúde
12.
Adv Drug Deliv Rev ; 183: 114172, 2022 04.
Artigo em Inglês | MEDLINE | ID: mdl-35189266

RESUMO

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.


Assuntos
Curadoria de Dados , Nanomedicina , Algoritmos , Humanos , Aprendizado de Máquina , Nanotecnologia
13.
Sensors (Basel) ; 22(4)2022 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-35214497

RESUMO

Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidelines for annotating object instances in respective classes and statistics of gathered annotations. Given the challenging image conditions, where objects are present in large amounts of occlusion and clutter, the datasets appear appropriate for training models. However, we experience annotator inconsistency, which can hamper evaluation. Based on this we argue the importance of having an evaluation form independent of the manual annotation where we evaluate our models with physically based sieving metrics. Additionally, instead of the traditional time-consuming manual annotation approach, we evaluate Semi-Supervised Learning as an alternative, showing competitive results while requiring fewer annotations. Specifically, given a relatively large supervised set of around 1400 images we can improve the Average Precision by a number of percentage points. Additionally, we show a significantly large improvement when using an extremely small set of just over 100 images, with over 3× in Average Precision and up to 20 percentage points when estimating the quality.


Assuntos
Aprendizado Profundo , Curadoria de Dados , Silagem , Aprendizado de Máquina Supervisionado , Zea mays
14.
Database (Oxford) ; 20222022 02 02.
Artigo em Inglês | MEDLINE | ID: mdl-35106535

RESUMO

Critical to answering large-scale questions in biology is the integration of knowledge from different disciplines into a coherent, computable whole. Controlled vocabularies such as ontologies represent a clear path toward this goal. Using survey questionnaires, we examined the attitudes of biologists toward adopting controlled vocabularies in phenotype publications. Our questions cover current experience and overall attitude with controlled vocabularies, the awareness of the issues around ambiguity and inconsistency in phenotype descriptions and post-publication professional data curation, the preferred solutions and the effort and desired rewards for adopting a new authoring workflow. Results suggest that although the existence of controlled vocabularies is widespread, their use is not common. A majority of respondents (74%) are frustrated with ambiguity in phenotypic descriptions, and there is a strong agreement (mean agreement score 4.21 out of 5) that author curation would better reflect the original meaning of phenotype data. Moreover, the vast majority (85%) of researchers would try a new authoring workflow if resultant data were more consistent and less ambiguous. Even more respondents (93%) suggested that they would try and possibly adopt a new authoring workflow if it required 5% additional effort as compared to normal, but higher rates resulted in a steep decline in likely adoption rates. Among the four different types of rewards, two types of citations were the most desired incentives for authors to produce computable data. Overall, our results suggest the adoption of a new authoring workflow would be accelerated by a user-friendly and efficient software-authoring tool, an increased awareness of the challenges text ambiguity creates for external curators and an elevated appreciation of the benefits of controlled vocabularies.


Assuntos
Curadoria de Dados , Software , Atitude , Fenótipo , Fluxo de Trabalho
15.
PLoS One ; 17(2): e0263616, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35143560

RESUMO

Peste des petits ruminants (PPR) is a highly contagious and devastating viral disease infecting predominantly sheep and goats. Tracking outbreaks of disease and analysing the movement of the virus often involves sequencing part or all of the genome and comparing the sequence obtained with sequences from other outbreaks, obtained from the public databases. However, there are a very large number (>1800) of PPRV sequences in the databases, a large majority of them relatively short, and not always well-documented. There is also a strong bias in the composition of the dataset, with countries with good sequencing capabilities (e.g. China, India, Turkey) being overrepresented, and most sequences coming from isolates in the last 20 years. In order to facilitate future analyses, we have prepared sets of PPRV sequences, sets which have been filtered for sequencing errors and unnecessary duplicates, and for which date and location information has been obtained, either from the database entry or from other published sources. These sequence datasets are freely available for download, and include smaller datasets which maximise phylogenetic information from the minimum number of sequences, and which will be useful for simple lineage identification. Their utility is illustrated by uploading the data to the MicroReact platform to allow simultaneous viewing of lineage date and geographic information on all the viruses for which we have information. While preparing these datasets, we identified a significant number of public database entries which contain clear errors, and propose guidelines on checking new sequences and completing metadata before submission.


Assuntos
Métodos Epidemiológicos , Genoma Viral , Vírus da Peste dos Pequenos Ruminantes/genética , RNA Viral , Análise de Sequência de RNA , Curadoria de Dados , Humanos , Recombinação Genética , Sequenciamento Completo do Genoma
16.
J Biomed Inform ; 127: 104007, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-35124236

RESUMO

Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research.


Assuntos
Ontologias Biológicas , Pesquisa Biomédica , Animais , Curadoria de Dados , Metadados , Projetos de Pesquisa , Semântica
18.
Elife ; 112022 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-34989675

RESUMO

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software's performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.


Assuntos
Linhagem da Célula , Rastreamento de Células/métodos , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Curadoria de Dados , Humanos
19.
Drug Discov Today ; 27(1): 207-214, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34332096

RESUMO

Standardizing data is crucial for preserving and exchanging scientific information. In particular, recording the context in which data were created ensures that information remains findable, accessible, interoperable, and reusable. Here, we introduce the concept of self-reporting data assets (SRDAs), which preserve data and contextual information. SRDAs are an abstract concept, which requires a suitable data format for implementation. Four promising data formats or languages are popularly used to represent data in pharma: JCAMP-DX, JSON, AnIML, and, more recently, the Allotrope Data Format (ADF). Here, we evaluate these four options in common use cases within the pharmaceutical industry using multiple criteria. The evaluation shows that ADF is the most suitable format for the implementation of SRDAs.


Assuntos
Confiabilidade dos Dados , Curadoria de Dados , Indústria Farmacêutica , Disseminação de Informação/métodos , Projetos de Pesquisa/normas , Curadoria de Dados/métodos , Curadoria de Dados/normas , Difusão de Inovações , Indústria Farmacêutica/métodos , Indústria Farmacêutica/organização & administração , Humanos , Estudo de Prova de Conceito , Padrões de Referência , Tecnologia Farmacêutica/métodos
20.
Nucleic Acids Res ; 50(D1): D687-D692, 2022 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-34788843

RESUMO

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.


Assuntos
Antivirais/farmacologia , Bases de Conhecimento , Proteínas/metabolismo , COVID-19/metabolismo , Curadoria de Dados , Genoma Humano , Interações Hospedeiro-Patógeno , Humanos , Proteínas/genética , Transdução de Sinais , Software
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