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1.
BMC Bioinformatics ; 22(1): 204, 2021 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-33879050

RESUMO

BACKGROUND: Drug-target interaction (DTI) plays a vital role in drug discovery. Identifying drug-target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug-target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. RESULTS: This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. CONCLUSIONS: Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.


Assuntos
Desenvolvimento de Medicamentos , Aprendizado de Máquina não Supervisionado , Algoritmos , Sequência de Aminoácidos , Descoberta de Drogas
2.
Nat Commun ; 12(1): 2078, 2021 04 06.
Artigo em Inglês | MEDLINE | ID: mdl-33824310

RESUMO

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.


Assuntos
Imagem por Ressonância Magnética , Esclerose Múltipla/diagnóstico por imagem , Esclerose Múltipla/diagnóstico , Aprendizado de Máquina não Supervisionado , Adulto , Bases de Dados como Assunto , Progressão da Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Biológicos , Placebos , Ensaios Clínicos Controlados Aleatórios como Assunto , Recidiva , Reprodutibilidade dos Testes
3.
Medicina (Kaunas) ; 57(3)2021 Mar 03.
Artigo em Inglês | MEDLINE | ID: mdl-33802471

RESUMO

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country's measures, which were implemented to contain the virus' spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus' spread in these cities, states, and regions.


Assuntos
/epidemiologia , Redes Neurais de Computação , Aprendizado de Máquina não Supervisionado , Brasil/epidemiologia , /transmissão , Humanos , Análise Espaço-Temporal
4.
J Affect Disord ; 286: 309-319, 2021 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-33770539

RESUMO

BACKGROUND: Despite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles. METHODS: This multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters. RESULTS: Four groups were identified among the 1795 included patients: group 1 ("heterogeneous" n = 1099), group 2 ("lithium" n = 265), group 3 ("valproate" n = 268), and group 4 ("lamotrigine" n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales. LIMITATIONS: The one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder. CONCLUSIONS: Treatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.


Assuntos
Antimaníacos , Transtorno Bipolar , Antimaníacos/uso terapêutico , Transtorno Bipolar/tratamento farmacológico , Humanos , Estudos Longitudinais , Aprendizado de Máquina não Supervisionado , Ácido Valproico/uso terapêutico
5.
J Med Internet Res ; 23(2): e23957, 2021 02 23.
Artigo em Inglês | MEDLINE | ID: mdl-33544690

RESUMO

BACKGROUND: During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government's responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. OBJECTIVE: The aim of this study was to examine comments on Canadian Prime Minister Trudeau's COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. METHODS: We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau's COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. RESULTS: We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau's policies, essential work and frontline workers, individuals' financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China's relationship, vaccines, and reopening. CONCLUSIONS: This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau's daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.


Assuntos
Governo Federal , Processamento de Linguagem Natural , Saúde Pública , Opinião Pública , Mídias Sociais , Canadá , Emigração e Imigração , Financiamento Governamental , Governo , Humanos , Estudos Longitudinais , Pandemias , Equipamento de Proteção Individual , Política Pública , Quarentena , Aprendizado de Máquina não Supervisionado
6.
Nat Commun ; 12(1): 1029, 2021 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-33589635

RESUMO

A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts representative information of each cell. The scDHA pipeline consists of two core modules. The first module is a non-negative kernel autoencoder able to remove genes or components that have insignificant contributions to the part-based representation of the data. The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed). To diminish the tendency to overfit of neural networks, we repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, we demonstrate that scDHA outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.


Assuntos
Redes Neurais de Computação , Análise de Sequência de RNA/estatística & dados numéricos , Análise de Célula Única/estatística & dados numéricos , Aprendizado de Máquina não Supervisionado/estatística & dados numéricos , Animais , Teorema de Bayes , Benchmarking , Separação Celular/métodos , Cerebelo/química , Cerebelo/citologia , Embrião de Mamíferos , Humanos , Fígado/química , Fígado/citologia , Pulmão/química , Pulmão/citologia , Camundongos , Células-Tronco Embrionárias Murinas/química , Células-Tronco Embrionárias Murinas/citologia , Pâncreas/química , Pâncreas/citologia , Retina/química , Retina/citologia , Análise de Célula Única/métodos , Córtex Visual/química , Córtex Visual/citologia , Zigoto/química , Zigoto/citologia
9.
Nat Protoc ; 16(2): 754-774, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33424024

RESUMO

Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.


Assuntos
Forma Celular/fisiologia , Imageamento Tridimensional/métodos , Microscopia Confocal/métodos , Algoritmos , Núcleo Celular/fisiologia , Humanos , Software , Aprendizado de Máquina não Supervisionado/estatística & dados numéricos
10.
Sci Rep ; 11(1): 774, 2021 01 12.
Artigo em Inglês | MEDLINE | ID: mdl-33436946

RESUMO

Population-level data have suggested that bacille Calmette-Guerin (BCG) vaccination may lessen the severity of Coronavirus Disease-19 (COVID-19) prompting clinical trials in this area. Some reports have demonstrated conflicting results. We performed a robust, ecologic analysis comparing COVID-19 related mortality (CRM) between strictly selected countries based on BCG vaccination program status utilizing publicly available databases and machine learning methods to define the association between active BCG vaccination programs and CRM. Validation was performed using linear regression and country-specific modeling. CRM was lower for the majority of countries with a BCG vaccination policy for at least the preceding 15 years (BCG15). CRM increased significantly for each increase in the percent population over age 65. A higher total population of a country and BCG15 were significantly associated with improved CRM. There was a consistent association between countries with a BCG vaccination for the preceding 15 years, but not other vaccination programs, and CRM. BCG vaccination programs continued to be associated with decreased CRM even for populations < 40 years old where CRM events are less frequent.


Assuntos
Vacina BCG/uso terapêutico , Vacinação/estatística & dados numéricos , /epidemiologia , Europa (Continente) , Humanos , República da Coreia , Aprendizado de Máquina não Supervisionado
11.
Carbohydr Polym ; 254: 117271, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-33357852

RESUMO

Dispersion of cellulose nanocrystals (CNCs) is of utmost importance to guarantee their reliable application. Nevertheless, there is still no consensual method to characterize CNC aggregation. The hypothesis of this paper is that dispersion could be quantified through the classification of aggregates detected in transmission electron microscopy images. k-Means was used to classify image particulate elements of five CNC samples into groups according to their geometric features. Particles were classified into five groups according to their maximum Feret diameter, elongation, circularity and area. Two groups encompassed the most application-critical aggregates: one integrated aggregates of high complexity and low compactness while the other included elongated aggregates. In addition, the characterization of CNC dispersion after different levels of sonication was achieved by assessing the change in the number of elements belonging to each cluster after sonication. This approach could be used as a standard for the characterization of the aggregation state of CNCs.


Assuntos
Celulose/química , Nanopartículas/química , Nanopartículas/ultraestrutura , Materiais Biocompatíveis/química , Celulose/classificação , Fractais , Processamento de Imagem Assistida por Computador , Microscopia Eletrônica de Transmissão , Nanopartículas/classificação , Tamanho da Partícula , Sonicação , Aprendizado de Máquina não Supervisionado
12.
Neural Netw ; 133: 103-111, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33166911

RESUMO

In recent years transfer learning has attracted much attention due to its ability to adapt a well-trained model from one domain to another. Fine-tuning is one of the most widely-used methods which exploit a small set of labeled data in the target domain for adapting the network. Including a few methods using the labeled data in the source domain, most transfer learning methods require labeled datasets, and it restricts the use of transfer learning to new domains. In this paper, we propose a fully unsupervised self-tuning algorithm for learning visual features in different domains. The proposed method updates a pre-trained model by minimizing the triplet loss function using only unlabeled data in the target domain. First, we propose the relevance measure for unlabeled data by the bagged clustering method. Then triplets of the anchor, positive, and negative data points are sampled based on the ranking violations of the relevance scores and the Euclidean distances in the embedded feature space. This fully unsupervised self-tuning algorithm improves the performance of the network significantly. We extensively evaluate the proposed algorithm using various metrics, including classification accuracy, feature analysis, and clustering quality, on five benchmark datasets in different domains. Besides, we demonstrate that applying the self-tuning method on the fine-tuned network help achieve better results.


Assuntos
Algoritmos , Redes Neurais de Computação , Aprendizado de Máquina não Supervisionado , Humanos
13.
Neural Netw ; 133: 148-156, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33217683

RESUMO

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the discriminator contains non-informative adversarial noises, which hinders the generator from catching the pattern of real samples. Here, we develop adversarial symmetric GANs (AS-GANs) that incorporate adversarial training of the discriminator on real samples into vanilla GANs, making adversarial training symmetrical. The discriminator is therefore more robust and provides more informative gradient with less adversarial noise, thereby stabilizing training and accelerating convergence. The effectiveness of the AS-GANs is verified on image generation on CIFAR-10, CIFAR-100, CelebA, and LSUN with varied network architectures. Not only the training is more stabilized, but the FID scores of generated samples are consistently improved by a large margin compared to the baseline. Theoretical analysis is also conducted to explain why AS-GAN can improve training. The bridging of adversarial samples and adversarial networks provides a new approach to further develop adversarial networks.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Aprendizado de Máquina não Supervisionado , Humanos , Distribuição Normal
14.
Stud Health Technol Inform ; 275: 32-36, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33227735

RESUMO

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Registros Eletrônicos de Saúde , Pandemias , Pneumonia Viral , Aprendizado de Máquina não Supervisionado , Infecções por Coronavirus/epidemiologia , Humanos , Pneumonia Viral/epidemiologia
15.
Nat Commun ; 11(1): 5781, 2020 11 13.
Artigo em Inglês | MEDLINE | ID: mdl-33188183

RESUMO

The temporal molecular changes that lead to disease onset and progression in Alzheimer's disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage-or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P = 1.0 × 10-5), Aß (CERAD score, P = 1.8 × 10-5), and cognitive diagnosis (P = 3.5 × 10-7) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data.


Assuntos
Encéfalo/patologia , Degeneração Neural/patologia , Algoritmos , Doença de Alzheimer/patologia , Progressão da Doença , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Humanos , Degeneração Neural/genética , Córtex Pré-Frontal/patologia , Fatores de Tempo , Aprendizado de Máquina não Supervisionado
16.
Environ Monit Assess ; 192(12): 744, 2020 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-33141352

RESUMO

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.


Assuntos
Poluentes Químicos da Água , Qualidade da Água , Monitoramento Ambiental , Rios , Estações do Ano , Dióxido de Silício , Aprendizado de Máquina não Supervisionado , Poluentes Químicos da Água/análise
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2047-2050, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018407

RESUMO

Ultrasound images are potentially invaluable for imaging internal organs and diseases. However, due to noise, they are still difficult to interpret. We apply and compare supervised machine learning approaches to train a model of lesions using features with unsupervised machine learning approaches to segment and detect tumours in breasts. Two synthetic and one real datasets are used in our experiments. The best system performance is achieved by Frost Filter with Quick Shift.


Assuntos
Neoplasias da Mama , Mama , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Humanos , Aprendizado de Máquina Supervisionado , Ultrassonografia , Aprendizado de Máquina não Supervisionado
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3359-3362, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018724

RESUMO

Steady-State Visual Evoked Potentials (SSVEPs) have become one of the most used neural signals for brain- computer interfaces (BCIs) due to their stability and high signal- to-noise rate. However, the performance of SSVEP-based BCIs would degrade with a few training samples. This study was proposed to enhance the detection of SSVEP by combining the supervised learning information from training samples and the unsupervised learning information from the trial to be tested. A new method, i.e. cyclic shift trials (CST), was proposed to generate new calibration samples from the test data, which were furtherly used to create the templates and spatial filters of task- related component analysis (TRCA). The test-trial templates and spatial filters were combined with training-sample templates and spatial filters to recognize SSVEP. The proposed algorithm was tested on a benchmark dataset. As a result, it reached significantly higher classification accuracy than traditional TRCA when only two training samples were used. Speciflcally, the accuracy was improved by 9.5% for 0.7s data. Therefore, this study demonstrates CST is effective to improve the performance of SSVEP-BCI.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Potenciais Evocados Visuais , Aprendizado de Máquina não Supervisionado
19.
J Med Internet Res ; 22(11): e24361, 2020 11 06.
Artigo em Inglês | MEDLINE | ID: mdl-33108315

RESUMO

BACKGROUND: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. OBJECTIVE: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. METHODS: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. RESULTS: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence-related news (eg, Tara Reade, Melissa DeRosa). CONCLUSIONS: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.


Assuntos
Infecções por Coronavirus , Violência Doméstica/estatística & dados numéricos , Pandemias/estatística & dados numéricos , Pneumonia Viral , Mídias Sociais/estatística & dados numéricos , Aprendizado de Máquina não Supervisionado , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Surtos de Doenças/estatística & dados numéricos , Violência Doméstica/legislação & jurisprudência , Feminino , Humanos , Violência por Parceiro Íntimo/legislação & jurisprudência , Violência por Parceiro Íntimo/estatística & dados numéricos , Masculino , Pneumonia Viral/epidemiologia , Minorias Sexuais e de Gênero/estatística & dados numéricos
20.
Am J Hum Genet ; 107(4): 670-682, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-32910913

RESUMO

Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.


Assuntos
Diabetes Mellitus Tipo 2/genética , Predisposição Genética para Doença , Fator 1-alfa Nuclear de Hepatócito/genética , Mutação de Sentido Incorreto , Sistema de Registros , Aprendizado de Máquina não Supervisionado , Adolescente , Adulto , Alelos , Criança , Análise por Conglomerados , Conjuntos de Dados como Assunto , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/patologia , Feminino , Expressão Gênica , Humanos , Masculino , Noruega/epidemiologia , Fenótipo , Análise de Componente Principal , Reino Unido/epidemiologia , Sequenciamento Completo do Exoma , Adulto Jovem
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