BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

134 related articles for article (PubMed ID: 38341049)

  • 1. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility.
    Hang NT; Long NT; Duy ND; Chien NN; Van Phuong N
    Int J Pharm; 2024 Mar; 653():123884. PubMed ID: 38341049
    [TBL] [Abstract][Full Text] [Related]  

  • 2. DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development-Validation through paracetamol and vanillin as a case study.
    Patel S; Patel M; Kulkarni M; Patel MS
    Int J Pharm; 2023 Apr; 637():122839. PubMed ID: 36931538
    [TBL] [Abstract][Full Text] [Related]  

  • 3. PharmDE: A new expert system for drug-excipient compatibility evaluation.
    Wang N; Sun H; Dong J; Ouyang D
    Int J Pharm; 2021 Sep; 607():120962. PubMed ID: 34339812
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.
    Qiu B; Shen Z; Yang D; Wang Q
    Front Oncol; 2023; 13():1183072. PubMed ID: 37293595
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques.
    Shi X; Cui Y; Wang S; Pan Y; Wang B; Lei M
    Spine J; 2024 Jan; 24(1):146-160. PubMed ID: 37704048
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Drug-excipient compatibility screening--role of thermoanalytical and spectroscopic techniques.
    Chadha R; Bhandari S
    J Pharm Biomed Anal; 2014 Jan; 87():82-97. PubMed ID: 23845418
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Decision Support for Excipient Risk Assessment in Pharmaceutical Manufacturing.
    Bejarano A; Hewa Nadungodage C; Wang F; Catlin AC; Hoag SW
    AAPS PharmSciTech; 2019 Jun; 20(6):223. PubMed ID: 31214878
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Drug-Excipient Compatibility Study Through a Novel Vial-in-Vial Experimental Setup: A Benchmark Study.
    Jain S; Shah RP
    AAPS PharmSciTech; 2023 May; 24(5):117. PubMed ID: 37160790
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Design and utilization of the drug-excipient chemical compatibility automated system.
    Thomas VH; Naath M
    Int J Pharm; 2008 Jul; 359(1-2):150-7. PubMed ID: 18486368
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Rapid 3-Day Excipient Screening Methodology and its Application in Identifying Chemical Stabilizers for Solid Formulations with Mixed Mechanisms of Degradation.
    Matharu AS; Dhareshwar SS; Cao YJ
    AAPS PharmSciTech; 2024 Jan; 25(1):12. PubMed ID: 38182862
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.
    Chandrashekar K; Setlur AS; Sabhapathi C A; Raiker SS; Singh S; Niranjan V
    Cancer Inform; 2023; 22():11769351221147244. PubMed ID: 36714384
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A new approach to accelerated drug-excipient compatibility testing.
    Sims JL; Carreira JA; Carrier DJ; Crabtree SR; Easton L; Hancock SA; Simcox CE
    Pharm Dev Technol; 2003; 8(2):119-26. PubMed ID: 12760562
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Improving the usability of open health service delivery simulation models using Python and web apps.
    Monks T; Harper A
    NIHR Open Res; 2023; 3():48. PubMed ID: 37881450
    [TBL] [Abstract][Full Text] [Related]  

  • 14. M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.
    Elbadawi M; Muñiz Castro B; Gavins FKH; Ong JJ; Gaisford S; Pérez G; Basit AW; Cabalar P; Goyanes A
    Int J Pharm; 2020 Nov; 590():119837. PubMed ID: 32961295
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.
    El-Behery H; Attia AF; El-Feshawy N; Torkey H
    Comput Biol Chem; 2021 Aug; 93():107536. PubMed ID: 34271420
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques.
    Zhao Q; Ye Z; Su Y; Ouyang D
    Acta Pharm Sin B; 2019 Nov; 9(6):1241-1252. PubMed ID: 31867169
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Selection of excipients for extended release formulations of glipizide through drug-excipient compatibility testing.
    Verma RK; Garg S
    J Pharm Biomed Anal; 2005 Jul; 38(4):633-44. PubMed ID: 15967291
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
    Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
    J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Understanding drug-excipient compatibility: oxidation of compound A in a solid dosage form.
    Wu Y; Dali M; Gupta A; Raghavan K
    Pharm Dev Technol; 2009; 14(5):556-64. PubMed ID: 19743950
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data.
    Rehman A; Alam T; Mujahid M; Alamri FS; Ghofaily BA; Saba T
    PeerJ Comput Sci; 2023; 9():e1684. PubMed ID: 38077612
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.