Computational biology: deep learning

Jones, William; Alasoo, Kaur; Fishman, Dmytro; Parts, Leopold

doi:10.1042/etls20160025

Cited by 69 publications

(53 citation statements)

References 99 publications

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“…Our findings confirm the utility of ensemble methods, and in particular boosting models, for predicting antibiotic resistance. While deep learning models are able to capture higher order interactions between features, and therefore often outperform simpler alternatives [37], they did not provide additional advantage here. Tree-based methods are often used as an intermediate between simple models that treat features independently, like logistic regression, and more complex, but poorly interpretable models.…”

Section: Discussionmentioning

confidence: 99%

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Moradigaravand

Palm

Farewell

et al. 2018

Preprint

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The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The diagnosis is typically made by measuring a few known determinants previously identified from whole genome sequencing, and thus is restricted to existing information on biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average F1 score of 0.88 on held-out data (range 0.66-0.96). While the best models most frequently employed all inputs, an average F1 score of 0.73 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and failed to improve prediction for ten out of 11 antibiotics. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data are informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.SummaryOne of the major health threats of 21st century is emergence of antibiotic resistance. To manage its economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.93 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

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Section: Discussionmentioning

confidence: 99%

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Moradigaravand

Palm

Farewell

et al. 2018

Preprint

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“…This is a complicated task even for humans, and as classical computational approaches have not been sufficiently accurate, fluorescent staining with its clean nuclear signal has been preferred. However, recent breakthroughs in deep learning have led to impressive performance on image analysis tasks in general (Angermueller et al 2016;Fan and Zhou 2016) , and segmentation from cell images in particular (Van Valen et al 2016;Falk et al 2019;Christiansen et al 2018;Jones et al 2017) . This motivates a re-evaluation of whether nuclear segmentation could be achieved without a DNA stain.…”

Section: Introductionmentioning

confidence: 99%

Segmenting nuclei in brightfield images with neural networks

Fishman

Salumaa

Majoral

et al. 2019

Preprint

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Identifying nuclei is a standard first step to analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we demonstrate that it is possible to accurately segment nuclei directly from brightfield images using deep learning. We confirmed that three convolutional neural network architectures can be adapted for this task, with U-Net achieving the best overall performance, Mask R-CNN providing an additional benefit of instance segmentation, and DeepCell proving too slow for practical application. We found that accurate segmentation is possible using as few as 16 training images and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work liberates a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

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“…In the latter, deep learning models are attractive to computational biologists, mainly due to the ability to learn a robust representation directly from raw input data, including bases of DNA sequences or pixel intensities of a microscopy image. On the other hand, traditional machine learning methods require extensive laboratory work for feature extraction in order to develop a reliable model [16].…”

Section: Introductionmentioning

confidence: 99%

Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

Costa

Santos

Cerri

2020

Preprint

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AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

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Computational biology: deep learning

Cited by 69 publications

References 99 publications

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Segmenting nuclei in brightfield images with neural networks

Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

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