Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 1.817
Filtrar
1.
Theranostics ; 10(12): 5641-5648, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32373237

RESUMEN

Rationale: Chest computed tomography (CT) has been used for the coronavirus disease 2019 (COVID-19) monitoring. However, the imaging risk factors for poor clinical outcomes remain unclear. In this study, we aimed to assess the imaging characteristics and risk factors associated with adverse composite endpoints in patients with COVID-19 pneumonia. Methods: This retrospective cohort study enrolled patients with laboratory-confirmed COVID-19 from 24 designated hospitals in Jiangsu province, China, between 10 January and 18 February 2020. Clinical and initial CT findings at admission were extracted from medical records. Patients aged < 18 years or without available clinical or CT records were excluded. The composite endpoints were admission to ICU, acute respiratory failure occurrence, or shock during hospitalization. The volume, density, and location of lesions, including ground-glass opacity (GGO) and consolidation, were quantitatively analyzed in each patient. Multivariable logistic regression models were used to identify the risk factors among age and CT parameters associated with the composite endpoints. Results: In this study, 625 laboratory-confirmed COVID-19 patients were enrolled; among them, 179 patients without an initial CT at admission and 25 patients aged < 18 years old were excluded and 421 patients were included in analysis. The median age was 48.0 years and the male proportion was 53% (224/421). During the follow-up period, 64 (15%) patients had a composite endpoint. There was an association of older age (odds ratio [OR], 1.04; 95% confidence interval [CI]: 1.01-1.06; P = 0.003), larger consolidation lesions in the upper lung (Right: OR, 1.13; 95%CI: 1.03-1.25, P =0.01; Left: OR,1.15; 95%CI: 1.01-1.32; P = 0.04) with increased odds of adverse endpoints. Conclusion: There was an association of older age and larger consolidation in upper lungs on admission with higher odds of poor outcomes in patients with COVID-19.


Asunto(s)
Infecciones por Coronavirus/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Pulmón/patología , Neumonía Viral/diagnóstico por imagen , Adulto , Factores de Edad , Anciano , Algoritmos , Betacoronavirus , China , Infecciones por Coronavirus/patología , Aprendizaje Profundo , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/patología , Pronóstico , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
2.
BMC Bioinformatics ; 21(Suppl 3): 63, 2020 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-32321437

RESUMEN

BACKGROUND: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to - 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. RESULTS: Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. CONCLUSION: Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.


Asunto(s)
Aprendizaje Profundo , Procesamiento Proteico-Postraduccional , Proteínas/metabolismo , Succinatos/metabolismo , Sitios de Unión , Ciclo del Ácido Cítrico , Lisina/metabolismo , Proteínas/química
3.
BMC Bioinformatics ; 21(1): 129, 2020 Apr 03.
Artículo en Inglés | MEDLINE | ID: mdl-32245392

RESUMEN

BACKGROUND: Imaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available. RESULTS: We developed an artificial intelligence approach to recognize off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. Next, we developed several machine and deep learning methods for recognizing off-sample ion images. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90). In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters. CONCLUSIONS: Overall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS.


Asunto(s)
Aprendizaje Automático , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Aprendizaje Profundo , Gentisatos/química
4.
N Engl J Med ; 382(18): 1687-1695, 2020 04 30.
Artículo en Inglés | MEDLINE | ID: mdl-32286748

RESUMEN

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).


Asunto(s)
Aprendizaje Profundo , Fondo de Ojo , Redes Neurales de la Computación , Oftalmoscopía/métodos , Papiledema/diagnóstico , Fotograbar , Retina/diagnóstico por imagen , Algoritmos , Área Bajo la Curva , Conjuntos de Datos como Asunto , Diagnóstico Diferencial , Humanos , Valor Predictivo de las Pruebas , Curva ROC , Retina/patología , Estudios Retrospectivos , Sensibilidad y Especificidad
5.
BMJ ; 369: m1326, 2020 04 03.
Artículo en Inglés | MEDLINE | ID: mdl-32245846
6.
JMIR Public Health Surveill ; 6(2): e18828, 2020 04 14.
Artículo en Inglés | MEDLINE | ID: mdl-32234709

RESUMEN

BACKGROUND: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources' data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE: This study aimed to predict the incidence of COVID-19 in Iran. METHODS: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. RESULTS: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Coronavirus , Minería de Datos , Aprendizaje Profundo , Neumonía Viral/epidemiología , Motor de Búsqueda/tendencias , Betacoronavirus , Brotes de Enfermedades , Femenino , Humanos , Incidencia , Irán/epidemiología , Masculino , Pandemias , Proyectos Piloto , Factores de Riesgo
7.
Chem Pharm Bull (Tokyo) ; 68(3): 227-233, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32115529

RESUMEN

The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of generating new molecules with desired properties. However, most of the properties do not have three-dimensional (3D) information, such as shape and pharmacophore. In drug discovery, pharmacophores are valuable clues in finding active compounds. In this study, we propose a computational strategy based on deep reinforcement learning for generating molecular structures with a desired pharmacophore. In addition, to extract selective molecules against a target protein, chemical genomics-based virtual screening (CGBVS) is used as post-processing method of deep reinforcement learning. As an example study, we have employed this strategy to generate molecular structures of selective TIE2 inhibitors. This strategy can be adopted into general use for generating selective molecules with a desired pharmacophore.


Asunto(s)
Aprendizaje Profundo , Diseño de Fármacos , Evaluación Preclínica de Medicamentos , Estructura Molecular , Unión Proteica
8.
Nat Rev Urol ; 17(4): 192-193, 2020 04.
Artículo en Inglés | MEDLINE | ID: mdl-32132704
9.
BMC Bioinformatics ; 21(1): 108, 2020 Mar 17.
Artículo en Inglés | MEDLINE | ID: mdl-32183722

RESUMEN

BACKGROUND: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. RESULTS: The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. CONCLUSION: The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.


Asunto(s)
Metilación de ADN , Aprendizaje Profundo , Interfaz Usuario-Computador , Envejecimiento/genética , Islas de CpG , Humanos , Neoplasias/genética , Neoplasias/patología
10.
Nat Med ; 26(3): 360-363, 2020 03.
Artículo en Inglés | MEDLINE | ID: mdl-32152582

RESUMEN

Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpretation strategies. Recently, deep neural networks have been used to automatically analyze ECG tracings and outperform physicians in detecting certain rhythm irregularities1. However, deep learning classifiers are susceptible to adversarial examples, which are created from raw data to fool the classifier such that it assigns the example to the wrong class, but which are undetectable to the human eye2,3. Adversarial examples have also been created for medical-related tasks4,5. However, traditional attack methods to create adversarial examples do not extend directly to ECG signals, as such methods introduce square-wave artefacts that are not physiologically plausible. Here we develop a method to construct smoothed adversarial examples for ECG tracings that are invisible to human expert evaluation and show that a deep learning model for arrhythmia detection from single-lead ECG6 is vulnerable to this type of attack. Moreover, we provide a general technique for collating and perturbing known adversarial examples to create multiple new ones. The susceptibility of deep learning ECG algorithms to adversarial misclassification implies that care should be taken when evaluating these models on ECGs that may have been altered, particularly when incentives for causing misclassification exist.


Asunto(s)
Aprendizaje Profundo , Electrocardiografía , Modelos Teóricos , Algoritmos , Humanos , Redes Neurales de la Computación
11.
Korean J Radiol ; 21(4): 505-508, 2020 04.
Artículo en Inglés | MEDLINE | ID: mdl-32174053

RESUMEN

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.


Asunto(s)
Betacoronavirus/genética , Infecciones por Coronavirus/virología , Neumonía Viral/virología , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Adulto , Betacoronavirus/aislamiento & purificación , Técnicas de Laboratorio Clínico , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/diagnóstico por imagen , Aprendizaje Profundo , Reacciones Falso Negativas , Humanos , Lactante , Masculino , Neumonía Viral/diagnóstico por imagen , Tomografía Computarizada por Rayos X
12.
BMC Bioinformatics ; 21(1): 119, 2020 Mar 20.
Artículo en Inglés | MEDLINE | ID: mdl-32197580

RESUMEN

BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.


Asunto(s)
Aprendizaje Profundo , Perfilación de la Expresión Génica , Aprendizaje Automático , Fenotipo , Enfermedad/genética , Humanos , Aprendizaje Automático Supervisado
13.
Nat Commun ; 11(1): 1162, 2020 03 06.
Artículo en Inglés | MEDLINE | ID: mdl-32139684

RESUMEN

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.


Asunto(s)
Citometría de Flujo/métodos , Ensayos Analíticos de Alto Rendimiento/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Microscopía Fluorescente/métodos , Aprendizaje Profundo , Euglena gracilis , Estudios de Factibilidad , Citometría de Flujo/instrumentación , Hematología/instrumentación , Hematología/métodos , Ensayos Analíticos de Alto Rendimiento/instrumentación , Humanos , Procesamiento de Imagen Asistido por Computador/instrumentación , Células Jurkat , Técnicas Microbiológicas/instrumentación , Microscopía Fluorescente/instrumentación , Sensibilidad y Especificidad
14.
BMJ ; 368: m689, 2020 03 25.
Artículo en Inglés | MEDLINE | ID: mdl-32213531

RESUMEN

OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. REVIEW METHODS: Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. RESULTS: Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. CONCLUSIONS: Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. STUDY REGISTRATION: PROSPERO CRD42019123605.


Asunto(s)
Aprendizaje Profundo , Diagnóstico por Imagen , Procesamiento de Imagen Asistido por Computador , Proyectos de Investigación , Algoritmos , Sesgo , Humanos , Médicos , Ensayos Clínicos Controlados Aleatorios como Asunto , Proyectos de Investigación/normas
17.
Sci Total Environ ; 711: 135160, 2020 Apr 01.
Artículo en Inglés | MEDLINE | ID: mdl-32000349

RESUMEN

Morphological species identification is often a difficult, expensive, and time-consuming process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An alternative approach is to automate the whole process, accelerating the identification process. Here, we demonstrate an automatic machine-based identification approach for non-biting midges (Diptera: Chironomidae) using Convolutional Neural Networks (CNNs) as a means of increasing taxonomic resolution of biomonitoring data at a minimal cost. Chironomidae were used to build the automatic identifier, as a family of insects that are abundant and ecologically important, yet difficult and time-consuming to accurately identify. The approach was tested with 10 morphologically very similar species from the same genus or subfamilies, comprising 1846 specimens from the South Morava river basin, Serbia. Three CNN models were built utilizing either species, genus, or subfamily data. After training the artificial neural network, images that the network had not seen during the training phase achieved an accuracy of 99.5% for species-level identification, while at the genus and subfamily level all images were correctly assigned (100% accuracy). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the mentum, ventromental plates, mandibles, submentum, and postoccipital margin to be morphologically important features for CNN classification. Thus, the CNN approach was a highly accurate solution for chironomid identification of aquatic macroinvertebrates opening a new avenue for implementation of artificial intelligence and deep learning methodology in the biomonitoring world. This approach also provides a means to overcome the gap in bioassessment for developing countries where widespread use techniques for routine monitoring are currently limited.


Asunto(s)
Aprendizaje Profundo , Animales , Ecosistema , Redes Neurales de la Computación , Serbia
18.
PLoS Comput Biol ; 16(2): e1007616, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-32012148

RESUMEN

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.


Asunto(s)
Aprendizaje Profundo , Estudios de Asociación Genética , Análisis Multivariante , Estudio de Asociación del Genoma Completo , Humanos , Polimorfismo de Nucleótido Simple , Sitios de Carácter Cuantitativo
19.
PLoS Comput Biol ; 16(2): e1007313, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-32023239

RESUMEN

We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machine-learning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney.


Asunto(s)
Órbita/anatomía & histología , Algoritmos , Aprendizaje Profundo , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Interfaz Usuario-Computador
20.
PLoS Comput Biol ; 16(2): e1007025, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-32069285

RESUMEN

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.


Asunto(s)
Antimaláricos/química , Antimaláricos/uso terapéutico , Aprendizaje Profundo , Descubrimiento de Drogas/métodos , Malaria/tratamiento farmacológico , Humanos , Relación Estructura-Actividad Cuantitativa , Reproducibilidad de los Resultados , Relación Estructura-Actividad
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA