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1.
Diagnostics (Basel) ; 11(11)2021 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-34829299

RESUMO

In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians. In this work, we propose a novel methodology to quantify the similarity between the decision rules used by a DL model and an ophthalmologist, based on the assumption that doctors are more likely to trust a prediction that was based on decision rules they can understand. Given an eye fundus image with OT, the proposed methodology compares the segmentation mask of OT lesions labeled by an ophthalmologist with the attribution matrix produced by interpretability methods. Furthermore, an open dataset that includes the eye fundus images and the segmentation masks is shared with the community. The proposal was tested on three different DL architectures. The results suggest that complex models tend to perform worse in terms of likelihood to be trusted while achieving better results in sensitivity and specificity.

2.
Stud Health Technol Inform ; 281: 173-177, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042728

RESUMO

Ocular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT. It could also help diagnose atypical cases, being particularly useful for ophthalmologists who have less experience. In this work, we propose the use of a deep learning model to perform automatic diagnosis of ocular toxoplasmosis from images of the eye fundus. A pretrained residual neural network is fine-tuned on a dataset of samples collected at the medical center of Hospital de Clínicas in Asunción, Paraguay. With sensitivity and specificity rates equal to 94% and 93%,respectively, the results show that the proposed model is highly promising. In order to replicate the results and advance further in this area of research, an open data set of images of the eye fundus labeled by ophthalmologists is made available.


Assuntos
Toxoplasmose Ocular , Fundo de Olho , Humanos , Redes Neurais de Computação , Paraguai , Sensibilidade e Especificidade , Toxoplasmose Ocular/diagnóstico por imagem
3.
PLoS Comput Biol ; 17(1): e1007814, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33465072

RESUMO

DNA topoisomerase II-ß (TOP2B) is fundamental to remove topological problems linked to DNA metabolism and 3D chromatin architecture, but its cut-and-reseal catalytic mechanism can accidentally cause DNA double-strand breaks (DSBs) that can seriously compromise genome integrity. Understanding the factors that determine the genome-wide distribution of TOP2B is therefore not only essential for a complete knowledge of genome dynamics and organization, but also for the implications of TOP2-induced DSBs in the origin of oncogenic translocations and other types of chromosomal rearrangements. Here, we conduct a machine-learning approach for the prediction of TOP2B binding using publicly available sequencing data. We achieve highly accurate predictions, with accessible chromatin and architectural factors being the most informative features. Strikingly, TOP2B is sufficiently explained by only three features: DNase I hypersensitivity, CTCF and cohesin binding, for which genome-wide data are widely available. Based on this, we develop a predictive model for TOP2B genome-wide binding that can be used across cell lines and species, and generate virtual probability tracks that accurately mirror experimental ChIP-seq data. Our results deepen our knowledge on how the accessibility and 3D organization of chromatin determine TOP2B function, and constitute a proof of principle regarding the in silico prediction of sequence-independent chromatin-binding factors.


Assuntos
Cromatina , DNA Topoisomerases Tipo II , Genoma/genética , Modelos Genéticos , Animais , Células Cultivadas , Cromatina/química , Cromatina/genética , Cromatina/metabolismo , DNA Topoisomerases Tipo II/química , DNA Topoisomerases Tipo II/genética , DNA Topoisomerases Tipo II/metabolismo , Genômica , Humanos , Células MCF-7 , Aprendizado de Máquina , Camundongos , Ligação Proteica , Timócitos
5.
Genes (Basel) ; 11(9)2020 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-32847102

RESUMO

The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available. Several methods have been based on predictive models trained on one-dimensional (1D) sequencing features, yielding promising results. However, different approaches vary both in the way they model chromatin interactions and in the machine learning-based strategy they rely on, making it challenging to carry out performance comparison of existing methods. In this study, we use publicly available 1D sequencing signals to model cohesin-mediated chromatin interactions in two human cell lines and evaluate the prediction performance of six popular machine learning algorithms: decision trees, random forests, gradient boosting, support vector machines, multi-layer perceptron and deep learning. Our approach accurately predicts long-range interactions and reveals that gradient boosting significantly outperforms the other five methods, yielding accuracies of about 95%. We show that chromatin features in close genomic proximity to the anchors cover most of the predictive information, as has been previously reported. Moreover, we demonstrate that gradient boosting models trained with different subsets of chromatin features, unlike the other methods tested, are able to produce accurate predictions. In this regard, and besides architectural proteins, transcription factors are shown to be highly informative. Our study provides a framework for the systematic prediction of long-range chromatin interactions, identifies gradient boosting as the best suited algorithm for this task and highlights cell-type specific binding of transcription factors at the anchors as important determinants of chromatin wiring mediated by cohesin.


Assuntos
Algoritmos , Cromatina/metabolismo , Simulação por Computador , Regulação Leucêmica da Expressão Gênica , Genoma Humano , Aprendizado de Máquina Supervisionado , Cromatina/genética , Humanos , Células K562 , Máquina de Vetores de Suporte
6.
Genes (Basel) ; 11(7)2020 07 21.
Artigo em Inglês | MEDLINE | ID: mdl-32708319

RESUMO

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.


Assuntos
Antígenos de Superfície/imunologia , Infecções por Coronavirus/genética , Infecções por Coronavirus/imunologia , Proteínas Ligadas por GPI/imunologia , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno/imunologia , Animais , Antígenos de Superfície/genética , Biologia Computacional/métodos , Proteínas Ligadas por GPI/genética , Regulação da Expressão Gênica , Interações Hospedeiro-Patógeno/genética , Camundongos Knockout , Vírus da Hepatite Murina
7.
Genes (Basel) ; 10(12)2019 11 22.
Artigo em Inglês | MEDLINE | ID: mdl-31766738

RESUMO

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.


Assuntos
Biomarcadores Tumorais/genética , Redes Reguladoras de Genes , Neoplasias Pulmonares/genética , Algoritmos , Biologia Computacional , Distrofina/genética , Expressão Gênica , Humanos , Pulmão/metabolismo , Proteínas de Membrana/genética , Mutação , Fumar/genética
8.
Microb Genom ; 5(11)2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31626589

RESUMO

Acinetobacter baumannii is an opportunistic bacterium that causes hospital-acquired infections with a high mortality and morbidity, since there are strains resistant to virtually any kind of antibiotic. The chase to find novel strategies to fight against this microbe can be favoured by knowledge of the complete catalogue of genes of the species, and their relationship with the specific characteristics of different isolates. In this work, we performed a genomics analysis of almost 2500 strains. Two different groups of genomes were found based on the number of shared genes. One of these groups rarely has plasmids, and bears clustered regularly interspaced short palindromic repeat (CRISPR) sequences, in addition to CRISPR-associated genes (cas genes) or restriction-modification system genes. This fact strongly supports the lack of plasmids. Furthermore, the scarce plasmids in this group also bear CRISPR sequences, and specifically contain genes involved in prokaryotic toxin-antitoxin systems that could either act as the still little known CRISPR type IV system or be the precursors of other novel CRISPR/Cas systems. In addition, a limited set of strains present a new cas9-like gene, which may complement the other cas genes in inhibiting the entrance of new plasmids into the bacteria. Finally, this group has exclusive genes involved in biofilm formation, which would connect CRISPR systems to the biogenesis of these bacterial resistance structures.


Assuntos
Acinetobacter baumannii/genética , Plasmídeos/genética , Bactérias/genética , Proteínas de Bactérias/genética , Biofilmes , Sistemas CRISPR-Cas , Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Genoma Bacteriano/genética , Genômica , Filogenia
10.
Bioinformatics ; 28(19): 2441-8, 2012 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-22833524

RESUMO

MOTIVATION: The prediction of a protein's contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation. RESULTS: The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions. AVAILABILITY: http://icos.cs.nott.ac.uk/servers/psp.html. CONTACT: natalio.krasnogor@nottingham.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Inteligência Artificial , Biologia Computacional/métodos , Proteínas/química , Algoritmos , Caspase 8/química , Caspase 9/química , Bases de Dados de Proteínas , Humanos , Domínios e Motivos de Interação entre Proteínas
11.
Comput Biol Med ; 42(2): 245-56, 2012 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-22196882

RESUMO

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR.


Assuntos
Análise por Conglomerados , Biologia Computacional/métodos , Bases de Dados Genéticas , Perfilação da Expressão Gênica/métodos , Algoritmos , Humanos
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