BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

106 related articles for article (PubMed ID: 35966348)

  • 1. Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content.
    Amin S; Alharbi A; Uddin MI; Alyami H
    Soft comput; 2022; 26(20):11077-11089. PubMed ID: 35966348
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study.
    Baker W; Colditz JB; Dobbs PD; Mai H; Visweswaran S; Zhan J; Primack BA
    JMIR Med Inform; 2022 Jul; 10(7):e33678. PubMed ID: 35862172
    [TBL] [Abstract][Full Text] [Related]  

  • 3. CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter.
    Abdelminaam DS; Ismail FH; Taha M; Taha A; Houssein EH; Nabil A
    IEEE Access; 2021; 9():27840-27867. PubMed ID: 34786308
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.
    Visweswaran S; Colditz JB; O'Halloran P; Han NR; Taneja SB; Welling J; Chu KH; Sidani JE; Primack BA
    J Med Internet Res; 2020 Aug; 22(8):e17478. PubMed ID: 32784184
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.
    Mackey T; Purushothaman V; Li J; Shah N; Nali M; Bardier C; Liang B; Cai M; Cuomo R
    JMIR Public Health Surveill; 2020 Jun; 6(2):e19509. PubMed ID: 32490846
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach.
    Alshalan R; Al-Khalifa H; Alsaeed D; Al-Baity H; Alshalan S
    J Med Internet Res; 2020 Dec; 22(12):e22609. PubMed ID: 33207310
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets.
    Swapnarekha H; Nayak J; Behera HS; Dash PB; Pelusi D
    Math Biosci Eng; 2023 Jan; 20(2):2382-2407. PubMed ID: 36899539
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.
    Pobiruchin M; Zowalla R; Wiesner M
    J Med Internet Res; 2020 Aug; 22(8):e19629. PubMed ID: 32790641
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.
    Chen S; Zhou L; Song Y; Xu Q; Wang P; Wang K; Ge Y; Janies D
    J Med Internet Res; 2021 Jan; 23(1):e24889. PubMed ID: 33326408
    [TBL] [Abstract][Full Text] [Related]  

  • 10. COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset.
    Reshi AA; Rustam F; Aljedaani W; Shafi S; Alhossan A; Alrabiah Z; Ahmad A; Alsuwailem H; Almangour TA; Alshammari MA; Lee E; Ashraf I
    Healthcare (Basel); 2022 Feb; 10(3):. PubMed ID: 35326889
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic.
    Khurana S; Chopra R; Khurana B
    Emerg Radiol; 2021 Jun; 28(3):477-483. PubMed ID: 33459907
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Exploring Eating Disorder Topics on Twitter: Machine Learning Approach.
    Zhou S; Zhao Y; Bian J; Haynos AF; Zhang R
    JMIR Med Inform; 2020 Oct; 8(10):e18273. PubMed ID: 33124997
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.
    Ayoobi N; Sharifrazi D; Alizadehsani R; Shoeibi A; Gorriz JM; Moosaei H; Khosravi A; Nahavandi S; Gholamzadeh Chofreh A; Goni FA; Klemeš JJ; Mosavi A
    Results Phys; 2021 Aug; 27():104495. PubMed ID: 34221854
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Extracting psychiatric stressors for suicide from social media using deep learning.
    Du J; Zhang Y; Luo J; Jia Y; Wei Q; Tao C; Xu H
    BMC Med Inform Decis Mak; 2018 Jul; 18(Suppl 2):43. PubMed ID: 30066665
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells.
    ArunKumar KE; Kalaga DV; Kumar CMS; Kawaji M; Brenza TM
    Chaos Solitons Fractals; 2021 May; 146():110861. PubMed ID: 33746373
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?
    Kendra RL; Karki S; Eickholt JL; Gandy L
    J Med Internet Res; 2015 Jun; 17(6):e154. PubMed ID: 26091775
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.
    Rustam F; Khalid M; Aslam W; Rupapara V; Mehmood A; Choi GS
    PLoS One; 2021; 16(2):e0245909. PubMed ID: 33630869
    [TBL] [Abstract][Full Text] [Related]  

  • 18. AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.
    Kour H; Gupta MK
    Neural Process Lett; 2022 Dec; ():1-40. PubMed ID: 36575702
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.
    Fatima R; Samad Shaikh N; Riaz A; Ahmad S; El-Affendi MA; Alyamani KAZ; Nabeel M; Ali Khan J; Yasin A; Latif RMA
    Comput Intell Neurosci; 2022; 2022():6561622. PubMed ID: 36156967
    [No Abstract]   [Full Text] [Related]  

  • 20. Deepfake tweets classification using stacked Bi-LSTM and words embedding.
    Rupapara V; Rustam F; Amaar A; Washington PB; Lee E; Ashraf I
    PeerJ Comput Sci; 2021; 7():e745. PubMed ID: 34805502
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.