pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework

BEHRT-HF: an interpretable transformer-based, deep learning model for prediction of incident heart failure

European Heart Journal ◽

10.1093/ehjci/ehaa946.3553 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

S Rao ◽

Y Li ◽

R Ramakrishnan ◽

A Hassaine ◽

D Canoy ◽

...

Keyword(s):

Heart Failure ◽

Deep Learning ◽

State Of The Art ◽

Failure Prediction ◽

Predictive Performance ◽

Learning Model ◽

Learning Framework ◽

Incident Heart Failure ◽

Ablation Study ◽

Deep Learning Model

Abstract Background/Introduction Predicting incident heart failure has been challenging. Deep learning models when applied to rich electronic health records (EHR) offer some theoretical advantages. However, empirical evidence for their superior performance is limited and they remain commonly uninterpretable, hampering their wider use in medical practice. Purpose We developed a deep learning framework for more accurate and yet interpretable prediction of incident heart failure. Methods We used longitudinally linked EHR from practices across England, involving 100,071 patients, 13% of whom had been diagnosed with incident heart failure during follow-up. We investigated the predictive performance of a novel transformer deep learning model, “Transformer for Heart Failure” (BEHRT-HF), and validated it using both an external held-out dataset and an internal five-fold cross-validation mechanism using area under receiver operating characteristic (AUROC) and area under the precision recall curve (AUPRC). Predictor groups included all outpatient and inpatient diagnoses within their temporal context, medications, age, and calendar year for each encounter. By treating diagnoses as anchors, we alternatively removed different modalities (ablation study) to understand the importance of individual modalities to the performance of incident heart failure prediction. Using perturbation-based techniques, we investigated the importance of associations between selected predictors and heart failure to improve model interpretability. Results BEHRT-HF achieved high accuracy with AUROC 0.932 and AUPRC 0.695 for external validation, and AUROC 0.933 (95% CI: 0.928, 0.938) and AUPRC 0.700 (95% CI: 0.682, 0.718) for internal validation. Compared to the state-of-the-art recurrent deep learning model, RETAIN-EX, BEHRT-HF outperformed it by 0.079 and 0.030 in terms of AUPRC and AUROC. Ablation study showed that medications were strong predictors, and calendar year was more important than age. Utilising perturbation, we identified and ranked the intensity of associations between diagnoses and heart failure. For instance, the method showed that established risk factors including myocardial infarction, atrial fibrillation and flutter, and hypertension all strongly associated with the heart failure prediction. Additionally, when population was stratified into different age groups, incident occurrence of a given disease had generally a higher contribution to heart failure prediction in younger ages than when diagnosed later in life. Conclusions Our state-of-the-art deep learning framework outperforms the predictive performance of existing models whilst enabling a data-driven way of exploring the relative contribution of a range of risk factors in the context of other temporal information. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): National Institute for Health Research, Oxford Martin School, Oxford Biomedical Research Centre

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Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

Frontiers in Public Health ◽

10.3389/fpubh.2020.599550 ◽

2020 ◽

Vol 8 ◽

Author(s):

Adil Khadidos ◽

Alaa O. Khadidos ◽

Srihari Kannan ◽

Yuvaraj Natarajan ◽

Sachi Nandan Mohanty ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Input Data ◽

Training Data ◽

Learning Framework ◽

Learning Classifiers ◽

Tomography Image ◽

Mining Model ◽

Deep Learning Model

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

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A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

International Journal of Molecular Sciences ◽

10.3390/ijms19092817 ◽

2018 ◽

Vol 19 (9) ◽

pp. 2817 ◽

Cited By ~ 9

Author(s):

Haixia Long ◽

Bo Liao ◽

Xingyu Xu ◽

Jialiang Yang

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

New Drugs ◽

Post Translational Modifications ◽

Novel Approach ◽

Benchmark Datasets ◽

Memory Network ◽

Scoring Matrix ◽

Deep Learning Model

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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A Deep Learning Model to predict the Industry Readiness of Engineering Students Community

ITM Web of Conferences ◽

10.1051/itmconf/20213701017 ◽

2021 ◽

Vol 37 ◽

pp. 01017

Author(s):

Ashok Murugesan ◽

Kumar Ramasamy ◽

Umadevi Ashok ◽

Revathy Pandian

Keyword(s):

Deep Learning ◽

21St Century ◽

21St Century Skills ◽

Engineering Students ◽

Learning Model ◽

Skill Assessment ◽

Technical Knowledge ◽

Learner Model ◽

Learning Framework ◽

Deep Learning Model

Industry readiness of Engineering students community is a big challenge in the recent campus recruitments. 21st century skills are completely mapped with the technical and non – technical knowledge background of the engineering graduates. In this paper the work narrated the process of identifying the parameters for skill assessment of the candidates and derived a learner model using deep learning framework. Further the model can be used to predict the employability readiness of candidates.

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A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i1.pp844-850 ◽

2021 ◽

Vol 11 (1) ◽

pp. 844

Author(s):

Mohammed Y. Kamil

Keyword(s):

Deep Learning ◽

High Performance ◽

Early Stage ◽

Diagnostic Technique ◽

Disease Diagnosis ◽

X Ray ◽

Learning Framework ◽

Chest X Ray ◽

Low Sensitivity ◽

Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

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A Deep Learning Framework to Predict Routability for FPGA Circuit Placement

ACM Transactions on Reconfigurable Technology and Systems ◽

10.1145/3465373 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1-28

Author(s):

Abeer Al-Hyari ◽

Hannah Szentimrey ◽

Ahmed Shamli ◽

Timothy Martin ◽

Gary Gréwal ◽

...

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Parameter Tuning ◽

Learning Framework ◽

Proposed Model ◽

Field Programmable ◽

Circuit Placement ◽

Detailed Placement ◽

Place And Route ◽

Deep Learning Model

The ability to accurately and efficiently estimate the routability of a circuit based on its placement is one of the most challenging and difficult tasks in the Field Programmable Gate Array (FPGA) flow. In this article, we present a novel, deep learning framework based on a Convolutional Neural Network (CNN) model for predicting the routability of a placement. Since the performance of the CNN model is strongly dependent on the hyper-parameters selected for the model, we perform an exhaustive parameter tuning that significantly improves the model’s performance and we also avoid overfitting the model. We also incorporate the deep learning model into a state-of-the-art placement tool and show how the model can be used to (1) avoid costly, but futile, place-and-route iterations, and (2) improve the placer’s ability to produce routable placements for hard-to-route circuits using feedback based on routability estimates generated by the proposed model. The model is trained and evaluated using over 26K placement images derived from 372 benchmarks supplied by Xilinx Inc. We also explore several opportunities to further improve the reliability of the predictions made by the proposed DLRoute technique by splitting the model into two separate deep learning models for (a) global and (b) detailed placement during the optimization process. Experimental results show that the proposed framework achieves a routability prediction accuracy of 97% while exhibiting runtimes of only a few milliseconds.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127.v1 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127.v2 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model

Applied Sciences ◽

10.3390/app10228008 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8008

Author(s):

Byunghyun Kim ◽

Soojin Cho

Keyword(s):

Deep Learning ◽

Damage Detection ◽

Developed Countries ◽

Learning Framework ◽

Environmental Objects ◽

Concrete Damage ◽

History Of ◽

General Object ◽

Deep Learning Model ◽

Instance Segmentation

In many developed countries with a long history of urbanization, there is an increasing need for automated computer vision (CV)-based inspection to replace conventional labor-intensive visual inspection. This paper proposes a technique for the automated detection of multiple concrete damage based on a state-of-the-art deep learning framework, Mask R-CNN, developed for instance segmentation. The structure of Mask R-CNN, which consists of three stages (region proposal, classification, and segmentation) is optimized for multiple concrete damage detection. The optimized Mask R-CNN is trained with 765 concrete images including cracks, efflorescence, rebar exposure, and spalling. The performance of the trained Mask R-CNN is evaluated with 25 actual test images containing damage as well as environmental objects. Two types of metrics are proposed to measure localization and segmentation performance. On average, 90.41% precision and 90.81% recall are achieved for localization and 87.24% precision and 87.58% recall for segmentation, which indicates the excellent field applicability of the trained Mask R-CNN. This paper also qualitatively discusses the test results by explaining that the architecture of Mask R-CNN that is optimized for general object detection purposes, can be modified to detect long and slender shapes of cracks, rebar exposure, and efflorescence in further research.

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