DeepHPV: a deep learning model to predict human papillomavirus integration sites

Abstract Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: [email protected], [email protected], [email protected]

Download Full-text

DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.

10.1101/2021.01.08.425855 ◽

2021 ◽

Author(s):

Canbiao Wu ◽

Xiaofang Guo ◽

Mengyuan Li ◽

Xiayu Fu ◽

Zeliang Hou ◽

...

Keyword(s):

Deep Learning ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Binding Sites ◽

Learning Model ◽

Validation Dataset ◽

B Virus ◽

Integration Sites ◽

Deep Learning Model ◽

Pan Cancer

Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer.

Download Full-text

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Journal of Personalized Medicine ◽

10.3390/jpm11040290 ◽

2021 ◽

Vol 11 (4) ◽

pp. 290

Author(s):

Luca Pasquini ◽

Antonio Napolitano ◽

Emanuela Tagliente ◽

Francesco Dellepiane ◽

Martina Lucignani ◽

...

Keyword(s):

Deep Learning ◽

Hypoxia Inducible Factor ◽

Model Performance ◽

High Grade Glioma ◽

Learning Model ◽

Dense Layer ◽

Idh Mutation ◽

Diagnostic Challenge ◽

Who Grade ◽

Deep Learning Model

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

Download Full-text

DeepCompare: Visual and Interactive Comparison of Deep Learning Model Performance

IEEE Computer Graphics and Applications ◽

10.1109/mcg.2019.2919033 ◽

2019 ◽

Vol 39 (5) ◽

pp. 47-59 ◽

Cited By ~ 5

Author(s):

Sugeerth Murugesan ◽

Sana Malik ◽

Fan Du ◽

Eunyee Koh ◽

Tuan Manh Lai

Keyword(s):

Deep Learning ◽

Model Performance ◽

Learning Model ◽

Deep Learning Model

Download Full-text

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists

BMJ Open ◽

10.1136/bmjopen-2019-036423 ◽

2020 ◽

Vol 10 (9) ◽

pp. e036423

Author(s):

Zhigang Song ◽

Chunkai Yu ◽

Shuangmei Zou ◽

Wenmiao Wang ◽

Yong Huang ◽

...

Keyword(s):

Deep Learning ◽

Colorectal Adenoma ◽

Detection System ◽

Model Performance ◽

Area Under The Curve ◽

Learning Model ◽

Feature Maps ◽

Adenoma Detection ◽

Microscopic Evaluation ◽

Deep Learning Model

ObjectivesThe microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.DesignThe deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals.ResultsThe deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists.ConclusionsThe deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists’ performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.

Download Full-text

https://jusst.org/developing-the-rural-economy-structure-in-agriculture-towards-the-betterment-of-indias-economy/

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst12508 ◽

2020 ◽

Vol 22 (12) ◽

Author(s):

Amit Doegar ◽

◽

Maitreyee Dutta ◽

Gaurav Kumar ◽

◽

...

Keyword(s):

Deep Learning ◽

Rural Economy ◽

Learning Algorithm ◽

Learning Model ◽

Image Features ◽

Forgery Detection ◽

Image Forgery ◽

Image Forgery Detection ◽

Testing Dataset ◽

Deep Learning Model

In the present scenario, one of the threats of trust on images for digital and online applications as well as on social media. Individual’s reputation can be turnish using misinformation or manipulation in the digital images. Image forgery detection is an approach for detection and localization of forged components in the image which is manipulated. For effective image forgery detection, an adequate number of features are required which can be accomplished by a deep learning model, which does not require manual feature engineering or handcraft feature approaches. In this paper we have implemented GoogleNet deep learning model to extract the image features and employ Random Forest machine learning algorithm to detect whether the image is forged or not. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the dataset into training and testing dataset and also compared with the state-of-the-art approaches.

Download Full-text

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Sensors ◽

10.3390/s21041064 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1064

Author(s):

I Nyoman Kusuma Wardana ◽

Julian W. Gardner ◽

Suhaib A. Fahmy

Keyword(s):

Deep Learning ◽

Air Quality ◽

Short Term Memory ◽

Model Performance ◽

Learning Model ◽

Sensor Data ◽

Raspberry Pi ◽

Learning Models ◽

Proposed Model ◽

Deep Learning Model

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

Download Full-text

P1-119: ENHANCING DEEP LEARNING MODEL PERFORMANCE FOR AD DIAGNOSIS USING ROI-BASED SELECTION

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.674 ◽

2019 ◽

Vol 15 ◽

pp. P280-P281

Author(s):

Shangran Qiu ◽

Megan S. Heydari ◽

Matthew I. Miller ◽

Prajakta S. Joshi ◽

Benjamin C. Wong ◽

...

Keyword(s):

Deep Learning ◽

Model Performance ◽

Learning Model ◽

Deep Learning Model

Download Full-text

Auxiliary Equipment Detection in Marine Engine Rooms Based on Deep Learning Model

Journal of Marine Science and Engineering ◽

10.3390/jmse9091006 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1006

Author(s):

Jiahao Qi ◽

Jundong Zhang ◽

Qingyan Meng

Keyword(s):

Deep Learning ◽

Single Channel ◽

Model Performance ◽

Learning Model ◽

Detection Accuracy ◽

Auxiliary Equipment ◽

Marine Engine ◽

Engine Room ◽

Pipeline Networks ◽

Deep Learning Model

In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.

Download Full-text

Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry

10.21203/rs.3.rs-962895/v1 ◽

2021 ◽

Author(s):

Anass El Houd ◽

Charbel El Hachem ◽

Loic Painvin

Keyword(s):

Deep Learning ◽

Automotive Industry ◽

Model Performance ◽

Learning Model ◽

Production Lines ◽

Research Focus ◽

Welding Seam ◽

Target Performance ◽

Deep Learning Model

Abstract The welding seams visual inspection is still manually operated by humans in different companies, so the result of the test is still highly subjective and expensive. At present, the integration of deep learning methods for welds classification is a research focus in engineering applications. This work intends to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability, two of the various metrics affecting the production lines and cost in the automotive industry. For this purpose, we implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model in order to make a more accurate classification of welding seam defects and improve both its performance and its reliability. The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%, which presents new perspectives to the developments of deep Learning explainability and interpretability.

Download Full-text

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites

BMC Ecology and Evolution ◽

10.1186/s12862-021-01869-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Canbiao Wu ◽

Xiaofang Guo ◽

Mengyuan Li ◽

Jingxian Shen ◽

Xiayu Fu ◽

...

Keyword(s):

Deep Learning ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Binding Sites ◽

Integration Site ◽

Learning Model ◽

Genomic Features ◽

B Virus ◽

Integration Sites ◽

Deep Learning Model

Abstract Background The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. Results An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. Conclusions DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.

Download Full-text