Modern deep learning in bioinformatics

Li, Haoyang; Tian, Shuye; Yu, Li; Fang, Qiming; Tan, Renbo; Pan, Yijie; Huang, Chao; Xu, Ying; Gao, Xin

doi:10.1093/jmcb/mjaa030

Cited by 74 publications

(35 citation statements)

References 20 publications

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“…This step is implemented to automatically use different scaling operations (up-sampling or down-sampling) and different scaling factors. We rely on the two unsupervised models (AutoCryoPicker [24] and SuperCryoEMPicker [22]) to estimate the dimensions of the original particle patches' coordinates that are detected in the training particles picking and selection stage. First, for each test micrograph, we calculate the average particle patches' coordinate form each dataset.…”

Section: Experiments On Fully Automated Single Particle Picking On DImentioning

confidence: 99%

“…Recently, Deep Learning has exponentially grown in the field of machine learning [12,13]. Many Deep Learning algorithms from the field of computer vision and bioinformatics such as [22,23] use convolutional techniques to extract features from big data via layers in neural networks [12]. Furthermore, deep learning appears to be a suitable approach for cryo-EM image processing as the size and number of the micrographs per data set are continually increasing while the SNR of micrographs remains low [4].…”

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confidence: 99%

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DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

et al. 2020

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Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

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Section: Experiments On Fully Automated Single Particle Picking On DImentioning

confidence: 99%

mentioning

confidence: 99%

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

et al. 2020

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“…This can cause issues when there are radiographs with severe periodontitis which have only few or abnormal shapes of teeth. To overcome this limitation, more data, especially the one with these special cases, need to be obtained to further improve the performance, for which few-shot learning might be a helpful way to deal with such special situations [9,10].…”

Section: Discussionmentioning

confidence: 99%

Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs

Zhou

et al. 2020

Lecture Notes in Computer Science

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Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20%-50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local community and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable machine learning models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable machine learning method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.

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“…On one hand, the proposed model requires 35.45 s when testing a group of EEG signals, which is not sustainable for hardware implementation. On the other hand, the accuracy of the emotion recognition obtained cannot meet the actual needs, so we will consider using an attention mechanism (Li et al, 2020), generative adversarial network (GAN) (Li et al, 2019a), or other advanced models for experiments.…”

Section: Comparison and Analysismentioning

confidence: 99%

Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

Zhong

Zhu

Cai

et al. 2020

Front. Hum. Neurosci.

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In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy (MSE) features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network (CNN) and hidden Markov models (HMMs). Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method.

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Modern deep learning in bioinformatics

Cited by 74 publications

References 20 publications

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs

Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

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