scholarly journals Target identification among known drugs by deep learning from heterogeneous networks

2020 ◽  
Vol 11 (7) ◽  
pp. 1775-1797 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Siyi Zhu ◽  
Weiqiang Lu ◽  
Zehui Liu ◽  
Jin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Target Identification ◽  
Drug Repurposing ◽  
Disease Network ◽  
The Relationship

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

Target Identification Among Known Drugs by Deep Learning from Heterogeneous Networks

2019 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Siyi Zhu ◽  
Weiqiang Lu ◽  
Jin Huang ◽  
Zehui Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Target Identification

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 800
Author(s):  
Jongchan Park ◽  
Min-Hyun Kim ◽  
Dong-Geol Choi
Keyword(s):  
Deep Learning ◽  
Simple Technique ◽  
Detection Method ◽  
Fraud Detection ◽  
Complementary Information ◽  
Multiple Modalities ◽  
Vending Machines ◽  
Emotion Intensity ◽  
The Relationship ◽  
Extract Information

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.

Automotive Headlighting: Effect of Foreground Illumination

1983 ◽  
Vol 57 (3_suppl) ◽  
pp. 1036-1038 ◽  
Author(s):  
Paul L. Olson ◽  
Michael Sivak
Keyword(s):  
Target Identification ◽  
Eye Fixation ◽  
Fixation Pattern ◽  
Eye Fixations ◽  
The Relationship

Described are studies of the relationship between the level of foreground illumination provided by automotive headlamps and the driver's eye-fixation pattern and ability to identify objects ahead of the car. Analysis indicates that the driver's eye fixations tended to move further from the car at high levels of foreground illumination. There were no differences in distance of target identification as a function of level of foreground illumination.

Assessing the Relationship Between Binarization and OCR in the Context of Deep Learning-Based ID Document Analysis

2021 ◽  
pp. 134-144
Author(s):  
Rubén Sánchez-Rivero ◽  
Pavel Bezmaternykh ◽  
Annette Morales-González ◽  
Francisco José Silva-Mata ◽  
Konstantin Bulatov
Keyword(s):  
Deep Learning ◽  
Document Analysis ◽  
The Relationship

Deep Learning and Online Education as an Informal Learning Process

2020 ◽  
pp. 1323-1343
Author(s):  
Theresa Neimann ◽  
Victor C. X. Wang
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Informal Learning ◽  
Traditional Classroom ◽  
Student Centered ◽  
Digital Platforms ◽  
Brick And Mortar ◽  
Controversial Topic ◽  
Electronic Education ◽  
The Relationship

Informal learning is a universal current phenomenon of learning via participation, experience, or learning via student-centered knowledge creation. It stands in stark contrast with the traditional view of didactic teacher-centered learning. Online education can be regarded as a positive and self-directed form of informal learning. Whether or not deep learning takes place for the online learner is a controversial topic for many educators. This chapter will discuss the benefits and challenges of the relationship between informal online learning leading to deeper learning. But, what isn't controversial is that in this century more education has been delivered in digital platforms than in any other time in history. For most providers of education to remain highly competitive, they must engage in electronic education of some form by moving beyond the brick and mortar of the traditional classroom. Informal learning has become the impetus resulting in the extensive and intensive application of electronic education.

scholarly journals Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1325
Author(s):  
Yoonjung Choi ◽  
Bonggun Shin ◽  
Keunsoo Kang ◽  
Sungsoo Park ◽  
Bo Ram Beck
Keyword(s):  
Deep Learning ◽  
Angiotensin Converting Enzyme ◽  
Drug Target ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Target Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

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