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Yogesh Kumar, Apeksha Koul, Seema Mahajan (2022)
A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intentSoft Computing, 26
A. Zhavoronkov, Y. Ivanenkov, A. Aliper, M. Veselov, V. Aladinskiy, Anastasiya Aladinskaya, V. Terentiev, Daniil Polykovskiy, Maksim Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Shayakhmetov Rim, Alexander Zhebrak, L. Minaeva, B. Zagribelnyy, Lennart Lee, R. Soll, D. Madge, Li Xing, Tao Guo, Alán Aspuru-Guzik (2019)
Deep learning enables rapid identification of potent DDR1 kinase inhibitorsNature Biotechnology, 37
A. Basile, A. Yahi, N. Tatonetti (2019)
Artificial Intelligence for Drug Toxicity and Safety.Trends in pharmacological sciences
A. Reddy, Shuxing Zhang (2013)
Polypharmacology: drug discovery for the futureExpert Review of Clinical Pharmacology, 6
G. Urban, Kevin Bache, Duc Phan, A. Sobrino, Alexander Shmakov, Stephanie Hachey, C. Hughes, P. Baldi (2019)
Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization ImagesIEEE/ACM Transactions on Computational Biology and Bioinformatics, 16
G. Kanna, S. Kumar, P. Parthasarathi, Y. Kumar (2023)
A Review on Prediction and Prognosis of the Prostate Cancer and Gleason Grading of Prostatic Carcinoma Using Deep Transfer Learning Based ApproachesArchives of Computational Methods in Engineering, 30
B. Chandrasekaran, Sara Abed, O. Al-Attraqchi, Kaushik Kuche, R. Tekade (2018)
Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties
Priyavrat Bhardwaj, Gaurav Bhandari, Yogesh Kumar, Surbhi Gupta (2022)
An Investigational Approach for the Prediction of Gastric Cancer Using Artificial Intelligence Techniques: A Systematic ReviewArchives of Computational Methods in Engineering, 29
(2022)
Artificial Intelligence in Medical Image Processing for Airway Diseases
A. Bender, I. Cortés-Ciriano (2021)
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological dataDrug Discovery Today, 26
Inci Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil Jain, Jiayu Zhou (2017)
Patient Subtyping via Time-Aware LSTM NetworksProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ShanShan Hu, Peng Chen, Pengying Gu, Bing Wang (2020)
A Deep Learning-Based Chemical System for QSAR PredictionIEEE Journal of Biomedical and Health Informatics, 24
Hongming Chen, O. Engkvist, Yinhai Wang, Marcus Olivecrona, T. Blaschke (2018)
The rise of deep learning in drug discovery.Drug discovery today, 23 6
Chun Lee, Yi-Ping Chen (2021)
Descriptive prediction of drug side‐effects using a hybrid deep learning modelInternational Journal of Intelligent Systems, 36
Feifei Cui, Zilong Zhang, Q. Zou (2021)
Sequence representation approaches for sequence-based protein prediction tasks that use deep learning.Briefings in functional genomics
Min Zhang, Guohua Geng (2019)
Adverse Drug Event Detection Using a Weakly Supervised Convolutional Neural Network and Recurrent Neural Network ModelInf., 10
(2019)
Recurrent Models for Drug Generation (Doctoral
S. Kawano, K. Ito, Kenzo Yahata, Kazunobu Kira, Takanori Abe, T. Akagi, M. Asano, K. Iso, Yuki Sato, Fumiyoshi Matsuura, Isao Ohashi, Yasunobu Matsumoto, Minetaka Isomura, Takeo Sasaki, Takashi Fukuyama, Yusuke Miyashita, Yosuke Kaburagi, A. Yokoi, O. Asano, T. Owa, Y. Kishi (2019)
A landmark in drug discovery based on complex natural product synthesisScientific Reports, 9
Sukhpreet Kaur, Yogesh Kumar, Apeksha Koul, Sushil Kamboj (2022)
A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and ChallengesArchives of Computational Methods in Engineering, 30
Michael Stravs, Kai Dührkop, Sebastian Böcker, Nicola Zamboni (2021)
MSNovelist: de novo structure generation from mass spectraNature Methods, 19
I. Baskin, D. Winkler, I. Tetko (2016)
A renaissance of neural networks in drug discoveryExpert Opinion on Drug Discovery, 11
Kai Zhao, H. So (2019)
Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression DataIEEE Journal of Biomedical and Health Informatics, 23
J. Vamathevan, Dominic Clark, P. Czodrowski, I. Dunham, Edgardo Ferran, George Lee, Bin Li, A. Madabhushi, Parantu Shah, M. Spitzer, Shanrong Zhao (2019)
Applications of machine learning in drug discovery and developmentNature Reviews Drug Discovery, 18
Tianen Liu, Natalia Khuri (2021)
Classification of drug prescribing information using long short-term memory networksProceedings of the 36th Annual ACM Symposium on Applied Computing
Yoav Goldberg (2015)
A Primer on Neural Network Models for Natural Language ProcessingArXiv, abs/1510.00726
X. Tong, Xiaohong Liu, Xiaoqin Tan, Xutong Li, Jiaxin Jiang, Zhaoping Xiong, Tingyang Xu, Hualiang Jiang, Nan Qiao, Mingyue Zheng (2021)
Generative Models for De Novo Drug Design.Journal of medicinal chemistry
Qingyuan Feng, Evgenia Dueva, A. Cherkasov, M. Ester (2018)
PADME: A Deep Learning-based Framework for Drug-Target Interaction PredictionArXiv, abs/1807.09741
Zhichao Liu, Ruth Roberts, M. Lal-Nag, Xi Chen, Ruili Huang, W. Tong (2021)
AI-based language models powering drug discovery and developmentDrug Discovery Today, 26
Mohammad Basiri, Moloud Abdar, M. Cifci, Shahla Nemati, U. Acharya (2020)
A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniquesKnowl. Based Syst., 198
Khushboo Bansal, R. Bathla, Yogesh Kumar (2022)
Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancerSoft Computing, 26
Jitendra Tembhurne, Tausif Diwan (2020)
Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networksMultimedia Tools and Applications, 80
Haixia Long, Mi Wang, Haiyan Fu (2017)
Deep Convolutional Neural Networks for Predicting Hydroxyproline in ProteinsCurrent Bioinformatics, 12
Inderpreet Kaur, A. Sandhu, Yogesh Kumar (2022)
Artificial Intelligence Techniques for Predictive Modeling of Vector-Borne Diseases and its Pathogens: A Systematic ReviewArchives of Computational Methods in Engineering, 29
Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, J. Bi (2018)
Edge Attention-based Multi-Relational Graph Convolutional NetworksArXiv, abs/1802.04944
Jinglu Tao, Xiaolong Zhang, Xiaoli Lin (2022)
A Targeted Drug Design Method Based on GRU and TopP Sampling Strategies
Selçuk Korkmaz (2020)
Deep Learning-Based Imbalanced Data Classification for Drug DiscoveryJournal of chemical information and modeling
S. Yadav, Asif Ekbal, S. Saha, P. Bhattacharyya (2018)
Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System
(2018)
2018), October An exploration of dropout with rnns for natural language inference. In International conference on artificial neural networks (pp. 157–167)
Y. Kumar, Surbhi Gupta, Williamjeet Singh (2022)
A novel deep transfer learning models for recognition of birds sounds in different environmentSoft Computing, 26
Maria Habib, Mohammad Faris, R. Qaddoura, Alaa Alomari, Hossam Faris (2021)
A Predictive Text System for Medical Recommendations in Telemedicine: A Deep Learning Approach in the Arabic ContextIEEE Access, 9
Youzhong Liu, Thomas Vijlder, Wout Bittremieux, K. Laukens, Wouter Heyndrickx (2021)
Current and future deep learning algorithms for MS/MS-based small molecule structure elucidation.Rapid communications in mass spectrometry : RCM
X. Li, Chengzhong Xu, Kang Wang, Zhiqiang Liu, Guihai Li (2022)
Prediction of Outlet Pressure for the Sulfur Dioxide Blower Based on Conv1D-BiGRU Model and Genetic AlgorithmComputational Intelligence and Neuroscience, 2022
Yogesh Kumar, Surbhi Gupta (2022)
Deep Transfer Learning Approaches to Predict Glaucoma, Cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and Healthy Eyes: An Experimental ReviewArchives of Computational Methods in Engineering, 30
Xun Wang, Jiali Liu, Chaogang Zhang, Shudong Wang (2022)
SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep LearningInternational Journal of Molecular Sciences, 23
Talia Kimber, Yonghui Chen, Andrea Volkamer (2021)
Deep Learning in Virtual Screening: Recent Applications and DevelopmentsInternational Journal of Molecular Sciences, 22
F. Gräßer, S. Kallumadi, H. Malberg, S. Zaunseder (2018)
Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data LearningProceedings of the 2018 International Conference on Digital Health
Steve Gardner, Sayoni Das, K. Taylor (2020)
AI Enabled Precision Medicine: Patient Stratification, Drug Repurposing and Combination Therapies
Xinyu Liu, Yongjun Wang, Xishuo Wang, Hui Xu, Chao Li, X. Xin (2021)
Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system.Optics express, 29 4
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, Yoshua Bengio (2014)
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence ModelingArXiv, abs/1412.3555
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Artificial intelligence-based drug discovery has gained attention lately since it drastically cuts the time and money needed to produce new treatments. In recent years, a vast quantity of data in various formats has been made accessible in the medical field to analyse different health complications. Drug discovery aims to uncover possible novel medications using a multidisciplinary approach that includes biology, chemistry, and pharmacology. Traditional sentiment analysis methods count or repeat words in a text assigned sentiment ratings by an expert. Several outdated, ineffective old methodologies are utilized to forecast drug design and discovery. However, with the development of DL (deep learning), the traditional drug discovery method has been further simplified. In this work, we applied deep learning models, such as LSTM (Long short-term memory), GRU (Gated recurrent units), Bidirectional LSTM (BiLSTM), Bidirectional GRU(BiGRU), SimpleRNN, embedding + LSTM, embedding + GRU, embedding + GRU + dropout, embedding + conv1d + LSTM, and Embedding + Conv1d + GRU on a dataset of drug reviews. Furthermore, we used Adam and RMSprop, two optimizers, for each model, for increased optimization. This research focuses on categorizing medication reviews into positive and negative categories. The effectiveness of the different deep learning models was assessed using a wide range of performance measures. Experiments demonstrated that the GRU (Gated Recurrent Unit) generated exceptional validation dataset results. In addition, this study emphasizes the relevance of deep learning methods over traditional learning approaches in categorization.
Archives of Computational Methods in Engineering – Springer Journals
Published: Jul 1, 2023
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