Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku)

Background Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. Methods Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. Results A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 ( P < .001), whereas the hybrid model had an AUC of .785 ( P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 ( P = .096). Conclusions A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.

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Forecasting stock price movement: new evidence from a novel hybrid deep learning model

Journal of Asian Business and Economic Studies ◽

10.1108/jabes-05-2021-0061 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yang Zhao ◽

Zhonglu Chen

Keyword(s):

Time Series ◽

Deep Learning ◽

Hybrid Model ◽

Stock Price ◽

Linear Time ◽

Price Movement ◽

Content Type ◽

Non Linear ◽

New Evidence ◽

Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

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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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Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2021.4.007 ◽

2022 ◽

Vol 3 (4) ◽

pp. 322-335

Author(s):

C. R. Nagarathna ◽

M. Kusuma

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Hybrid Model ◽

Magnetic Resonance Images ◽

Medical Field ◽

The Past ◽

Learning Techniques ◽

Learning Technique ◽

Deep Learning Model

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.

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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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Detecting Shilling Attacks Using Hybrid Deep Learning Models

Symmetry ◽

10.3390/sym12111805 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1805

Author(s):

Mahsa Ebrahimian ◽

Rasha Kashef

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Hybrid Model ◽

Network Architecture ◽

Information Overload ◽

Spatial Information ◽

Recommendation Systems ◽

Learning Methods ◽

Digital World ◽

Deep Learning Model

Recommendation systems play a significant role in alleviating information overload in the digital world. They provide suggestions to users based on past symmetric activities or behaviors. Being heavily dependent on users’ behavior, they tend to be vulnerable to shilling attacks. Therefore, protecting them from attacks’ effects is highly important. As shilling attacks have features of a large number of ratings and increasing complexity in attack models, deep learning methods become proper alternatives for more accurate attack detections. This paper proposes a hybrid model of two different neural networks, convolutional and recurrent neural networks, to detect shilling attacks efficiently. The proposed deep learning model utilizes the transformed network architecture for undertaking the attributes derived from user-rated profiles. This architecture enables modeling of the temporal and spatial information in the recommendation system’s ratings. The hybrid model overcomes the limitations of the existing shilling attack deep-learning methods to enhance the recommendation systems’ efficiency and robustness. Experimental results show that the hybrid model results in better predictions on the Movie-Lens 100 K and Netflix datasets by accurately detecting most of the obfuscated attacks compared to the state-of-art deep learning algorithms used for investigation.

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Tool Wear Prediction Based on Edge Data Processing and Deep Learning Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1820/1/012165 ◽

2021 ◽

Vol 1820 (1) ◽

pp. 012165

Author(s):

Dong Yan ◽

Xinyu Ding ◽

Shuaiyu Pan ◽

Haoyang Huang

Keyword(s):

Deep Learning ◽

Tool Wear ◽

Data Processing ◽

Learning Model ◽

Wear Prediction ◽

Tool Wear Prediction ◽

Deep Learning Model

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Detection of COVID-19 from X-rays using Hybrid Deep Learning Models

10.21203/rs.3.rs-468236/v1 ◽

2021 ◽

Author(s):

Ritika Nandi ◽

Manjunath Mulimani

Keyword(s):

Deep Learning ◽

Hybrid Model ◽

Deep Neural Networks ◽

Learning Model ◽

Personal Digital Assistants ◽

X Rays ◽

X Ray ◽

Hardware Resource ◽

Chest X Ray ◽

Deep Learning Model

Abstract In this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light Deep Neural Networks (DNNs) and can be used with low hardware resource-based Personal Digital Assistants (PDA) for quick detection of COVID-19 infection. The performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia and coronavirus infected chest X-rays. Results show that the proposed hybrid model more suitable for COVID-19 detection and achieve the highest recognition accuracy on both the datasets.

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A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction

Sustainability ◽

10.3390/su12229490 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9490

Author(s):

Hao Zhen ◽

Dongxiao Niu ◽

Min Yu ◽

Keke Wang ◽

Yi Liang ◽

...

Keyword(s):

Deep Learning ◽

Wind Power ◽

Hybrid Model ◽

Learning Model ◽

Short Term ◽

Wind Power Forecasting ◽

Spatial Feature ◽

Feature Based ◽

Deep Learning Model ◽

Temporal Feature

The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R2 of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.

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