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Kirtan Dave, Shriya Joshi, Ruchita Shelat
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10.30574/wjarr.2024.21.1.0382 · 2024
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10.7763/ijcte.2015.v7.999 · 2015
Confidence 0%
Artificial intelligence in healthcare: transforming the practice of medicine
10.7861/fhj.2021-0095 · 2021
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10.3390/life12091430 · 2022
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10.3389/fchem.2017.00006 · 2017
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10.3390/app15126578 · 2025
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10.1016/j.csbj.2021.10.009 · 2021
Implementing machine learning in health care—addressing ethical challenges
10.1056/nejmp1714229 · 2018
Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: optimization, characterization, and in-vitro assessment for the anticancer drug delivery
10.1016/j.ijbiomac.2024.135123 · 2024
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2025
Machine learning methods for protein structure prediction
10.1109/rbme.2008.2008239 · 2008
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10.3389/fbioe.2023.1177151 · 2023
Proteomics technologies and challenges
10.1016/s1672-0229(07)60018-7 · 2007
Unresolved referenced work
Kept as external metadata until matched
Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug–drug links based on graph neural network
10.1093/bioinformatics/btab191 · 2021
Multifaceted applications of micro/nanorobots in pharmaceutical drug delivery systems: a comprehensive review
10.1186/s43094-023-00577-y · 2024
Integrating artificial intelligence with quality by design in the formulation of Lecithin/Chitosan nanoparticles of a poorly water-soluble drug
10.1208/s12249-023-02609-5 · 2023
Micromotor-enabled active drug delivery for in vivo treatment of stomach infection
10.1038/s41467-017-00309-w · 2017
Feature subset selection for splice site prediction
10.1093/bioinformatics/18.suppl_2.s75 · 2002
Progressing microbial genomics: Artificial intelligence and deep learning driven advances in genome analysis and therapeutics
10.1016/j.ibmed.2025.100251 · 2025
Artificial intelligence in clinical and genomic diagnostics
10.1186/s13073-019-0689-8 · 2019
The transport of non-surfactant based paclitaxel loaded magnetic nanoparticles across the blood brain barrier in a rat model
10.1016/j.biomaterials.2011.12.046 · 2012
10.1002/9780470593417
10.1002/9780470593417
Doxorubicin loaded magnetic gold nanoparticles for in vivo targeted drug delivery
10.1016/j.ijpharm.2015.05.032 · 2015
Optimization of pullulan production by Aureobasidium pullulansusing semi-solid-state fermentation and artificial neural networks: characterization and antibacterial activity of pullulan impregnated with Ag–TiO2nanocomposite
10.1016/j.ijbiomac.2024.132109 · 2024
Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types
10.1016/j.ejca.2025.115440 · 2025
A probabilistic view of gene function
10.1038/ng1370 · 2004
The future of drug delivery
10.1021/acs.chemmater.2c03003 · 2023
Artificial micromotors in the mouse’s stomach: a step toward in vivo use of synthetic motors
10.1021/nn507097k · 2015
Long walk to genomics: history and current approaches to genome sequencing and assembly
10.1016/j.csbj.2019.11.002 · 2020
Proteomics and Its Current Application in Biomedical Area: Concise Review
10.1155/2024/4454744 · 2024
A Review of Bias and Fairness in Artificial Intelligence
10.9781/ijimai.2023.11.001 · 2024
Magnetically Driven Micro and Nanorobots
10.1021/acs.chemrev.0c01234 · doi-reference
Artificial intelligence in drug design
10.1007/s11427-018-9342-2 · doi-reference
Applications of artificial intelligence to lipid nanoparticle delivery
10.1016/j.partic.2023.11.014 · doi-reference
Artificial intelligence in cancer target identification and drug discovery
10.1038/s41392-022-00994-0 · doi-reference
Artificial intelligence-assisted development of in situ forming nanoparticles for arthritis therapy via intra-articular delivery
10.1080/10717544.2022.2069882 · doi-reference
Artificial intelligence-assisted multiparameter size discrimination of silver nanoparticles through electrochemical collision
10.1021/acs.analchem.3c05115 · doi-reference
Artificial intelligence, physiological genomics, and precision medicine
10.1152/physiolgenomics.00119.2017 · doi-reference
Advances of artificial intelligence in anti-cancer drug design: a review of the past decade
10.3390/ph16020253 · doi-reference
Artificial intelligence in pharmaceutical technology and drug delivery design
10.3390/pharmaceutics15071916 · doi-reference
The evolution of commercial drug delivery technologies
10.1038/s41551-021-00698-w · doi-reference
The impact of AlphaFold protein structure database on the fields of life sciences
10.1002/pmic.202200128 · doi-reference
Role-based policy to maintain privacy of patient health records in cloud
10.1007/s11227-019-02887-6 · doi-reference
The artificial intelligence and design of experiment assisted in the development of progesterone-loaded solid-lipid nanoparticles for transdermal drug delivery
10.3897/pharmacia.71.e123549 · doi-reference
SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation
10.1093/bib/bbab534 · doi-reference
What are biomarkers?
10.1097/coh.0b013e32833ed177 · doi-reference
Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
10.1038/s41524-021-00614-5 · doi-reference
The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review
10.3390/s23239498 · doi-reference
Artificial intelligence and personalized medicine
10.1007/978-3-030-16391-4_11 · doi-reference
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
10.1039/d2sc05709c · doi-reference
A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models
10.1038/s41587-024-02143-0 · doi-reference
Machine learning model for prediction of development of cancer stem cell subpopulation in tumurs subjected to polystyrene nanoparticles
10.3390/toxics12050354 · doi-reference
AI-powered therapeutic target discovery
10.1016/j.tips.2023.06.010 · doi-reference
Clinically Relevant Genetic Variations in Drug Metabolizing Enzymes
10.2174/138920011795495321 · doi-reference
Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication
10.1097/ms9.0000000000001320 · doi-reference
Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities
10.3389/fphar.2024.1437167 · doi-reference
Biotechnology, big data and artificial intelligence
10.1002/biot.201800613 · doi-reference
10.1186/gb-2002-3-12-research0087
10.1186/gb-2002-3-12-research0087 · doi-reference
Predicting drug release from diazepam FDM printed tablets using deep learning approach: influence of process parameters and tablet surface/volume ratio
10.1016/j.ijpharm.2021.120507 · doi-reference
Pharmacogenetic interventions to improve outcomes in patients with multimorbidity or prescribed polypharmacy: a systematic review
10.1038/s41397-021-00260-6 · doi-reference
Using genetics and genomics strategies to personalize therapy for cancer: Focus on melanoma
10.1016/j.bcp.2010.04.017 · doi-reference
Drug delivery to the central nervous system
10.1038/s41578-021-00394-w · doi-reference
The Path to Routine Genomic Screening in Health Care
10.7326/m18-1722 · doi-reference
RNA-seq and ChIP-seq as complementary approaches for comprehension of plant transcriptional regulatory mechanism
10.3390/ijms21010167 · doi-reference
Solid lipid nanoparticles: a promising tool for insulin delivery
10.1080/17425247.2022.2138328 · doi-reference
Personalized medicine could transform healthcare
10.3892/br.2017.922 · doi-reference
Rational drug design
10.1016/j.ejphar.2009.06.065 · doi-reference
Impact of high-throughput screening in biomedical research
10.1038/nrd3368 · doi-reference
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
10.1093/bib/bbab457 · doi-reference
Application of deep learning in genomics
10.1007/s11427-020-1804-5 · doi-reference
Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches
10.2147/ijn.s344208 · doi-reference