Deep learning models in genomics; are we there yet?

Koumakis, Lefteris

doi:10.1016/j.csbj.2020.06.017

Cited by 118 publications

(74 citation statements)

References 57 publications

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“…DL models are still in its infancy for study of genomics and none has been validated in clinical practice. Multiple generic challenges exist, such as the lack of explainable AI, balanced datasets representing both disease and healthy states, and integration of heterogeneous data, which is akin to some of the challenges presented by multi-modal algorithms discussed above ( Koumakis, 2020 ).…”

Section: Future Research and Recommendation On Digital Innovationsmentioning

confidence: 99%

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Liu

Ting

et al. 2021

Progress in Retinal and Eye Research

362

273

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The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a “new normal”, the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

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Section: Future Research and Recommendation On Digital Innovationsmentioning

confidence: 99%

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Liu

Ting

et al. 2021

Progress in Retinal and Eye Research

362

273

View full text Add to dashboard Cite

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“…For each dataset, the number of clusters used by the k-means algorithm in the experiment was chosen within the range of length 2 around the number of cell types in the dataset. For the above three datasets of 10X PBMC, Mouse Bladder Cells and Worm Neuron Cells, the selection range of the number of clusters fall into [6,10], [14,18] and [8,12] respectively. The clustering performance on these three datasets is shown in Fig.…”

Section: Selections Of the Number Of Clustersmentioning

confidence: 99%

“…Recently, deep learning technology [17] has made significant breakthroughs in computer vision, natural language processing and other cross domains based on deep neural networks (DNN) [18]. DNN can process high-dimensional data and extract efficient and effective features, thus becomes the dominant option for big data analysis.…”

Section: Introductionmentioning

confidence: 99%

AAE-SC: A scRNA-Seq Clustering Framework Based on Adversarial Autoencoder

Guo

Xiao

et al. 2020

IEEE Access

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Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells, and it is expected to provide higher cellular differential resolution than traditional bulk RNA sequencing. In scRNA-seq analysis, clustering is crucial for identifying cell types, and can be potentially exploited to understand high-level biological processes. Recently, autoencoder has been successfully applied in scRNAseq clustering problem and achieved promising results. Most existing works focus on characterizing the sparsity of data, and directly utilize the bottleneck feature of the autoencoder for clustering might not be optimal. In this paper, a novel framework named Adversarial AutoEncoder ScRNA-seq Clustering (AAE-SC) is proposed to bring an additional constraint on the bottleneck feature. Specifically, AAE-SC adds an AAE module on top of the bottleneck layer, and constrains the bottleneck feature distribution to be aligned with a consistent distribution. Also, the AAE and the reconstructed modules are jointly optimized to generate a highly discriminative and consistent feature, which is further proceeded for clustering. We find that by using AAE-SC to impose certain constraints on the features of the hidden layer, the performance of clustering can be improved. Experimental results on three real-world datasets demonstrate that the proposed AAE-SC framework outperformed state-of-the-art methods by 2% at least and 5% at most. And AAE-SC shows more robustness than the baseline model for downsampled and unbalanced cluster size datasets. INDEX TERMS Single-cell RNA-seq data, Adversarial autoencoder, Clustering analysis, Unsupervised learning Author et al.: AAE-SC: A scRNA-seq Clustering Framework based on Adversarial Autoencoder Genes Cells Low High Cluster1 Cluster2 Cluster3 Blood Cells Bladder Cells Neurons Unsupervised Clustering Cell Label Identification FIGURE 1. Explanation of scRNA-seq clustering task. Because dropout causes the gene expression level of the original data to be very low, the data is highly sparse, and it brings difficulties to the subsequent clustering. Therefore, special clustering algorithms are required to process this type of data and correctly assign different cell samples to different And identify the cell type. The heat map on the left of the figure is a visual representation of raw scRNA-seq data, and the numbers in the heat map indicate the expression value of each gene in the cell sample. The color bars in the figure indicate the level of gene expression.

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“…Curiously, the projective and receptive fields may not be aligned. Numerous other publications have shown that they capture meaningful properties and structure of the data, reducing complexity to a level that lends itself to interpretation (Way et al, 2020;Koumakis, 2020). In one instance involving transcription factor micro-array data, a close one-to-one mapping could be obtained from the last hidden layer, in addition to the higher level layers that related to biological processes in a hierarchical fashion (Chen et al, 2016a).…”

Section: Interpretabilitymentioning

confidence: 99%

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

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After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it.Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging.The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above.Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics.We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

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Deep learning models in genomics; are we there yet?

Cited by 118 publications

References 57 publications

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

AAE-SC: A scRNA-Seq Clustering Framework Based on Adversarial Autoencoder

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

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