HetEnc: a deep learning predictive model for multi-type biological dataset

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

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Deep-Learning Based Analysis of Preoperative MRI Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma

10.21203/rs.3.rs-1120787/v1 ◽

2021 ◽

Author(s):

Bao-Ye sun ◽

Pei-Yi Gu ◽

Ruo-Yu Guan ◽

Cheng Zhou ◽

Jian-Wei Lu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Predictive Model ◽

Clinical Outcomes ◽

Area Under The Curve ◽

Microvascular Invasion ◽

Dce Mri ◽

Preoperative Prediction ◽

Model Combining ◽

Contrast Enhanced

Abstract Background & Aims: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and AFP were independently associated with MVI: DL-MVI (odds ratio [OR]=35.738; 95% confidence interval [CI]: 14.027-91.056; p<0.001), AFP (OR=4.634, 95% CI: 2.576-8.336; p<0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

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A predictive model of gene expression using a deep learning framework

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2016.7822599 ◽

2016 ◽

Cited By ~ 1

Author(s):

Rui Xie ◽

Andrew Quitadamo ◽

Jianlin Cheng ◽

Xinghua Shi

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Predictive Model ◽

Learning Framework

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Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos

Entropy ◽

10.3390/e22101134 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1134

Author(s):

Cai Li ◽

Jianguo Zhang ◽

Luxiao Sang ◽

Lishuang Gong ◽

Longsheng Wang ◽

...

Keyword(s):

Deep Learning ◽

Predictive Model ◽

Random Number ◽

Random Number Generator ◽

External Cavity ◽

Number Generator ◽

Output Data ◽

Final Output ◽

Linear Congruential

In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.

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US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients

Frontiers in Oncology ◽

10.3389/fonc.2021.672055 ◽

2021 ◽

Vol 11 ◽

Author(s):

Hang Zhou ◽

Tao Jiang ◽

Qunying Li ◽

Chao Zhang ◽

Cong Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Predictive Model ◽

Clinical Information ◽

Learning Model ◽

Lower Sensitivity ◽

Liver Imaging ◽

The Us ◽

Cirrhotic Patients ◽

Deep Learning Model

The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.

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A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp596-604 ◽

2022 ◽

Vol 12 (1) ◽

pp. 596

Author(s):

Salma Firdose ◽

Surendran Swapna Kumar ◽

Ravinda Gayan Narendra Meegama

Keyword(s):

Deep Learning ◽

Predictive Model ◽

Infected Individual ◽

Predictive Performance ◽

Dynamic Monitoring ◽

Duration Of Stay ◽

Social Distancing ◽

Basic Model

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.

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