Cancer treatment comes to age: from one-size-fits-all to next-generation sequencing (NGS) technologies

Sepideh Parvizpour ^1,2 * , Hanieh Beyrampour-Basmenj ², Jafar Razmara ³ * , Farhad Farhadi ⁴, Mohd Shahir Shamsir ⁵

Cited by CrossRef: 5

1- Li S, Ghorbian M, Ghobaei-Arani M. In-Depth Analysis of Meta-Learning in Cancer Disease: Key Challenges and Recommendations. Arch Computat Methods Eng. 2025; [Crossref]

2- Kadam H, Vora A, Sharma S. Companion diagnostic: An overview of regulatory framework, challenges, and future directions. Annales Pharmaceutiques Françaises. 2025; [Crossref]

3- Karimi S, Bakhshali R, Bolandi S, Zahed Z, Mojtaba Zadeh S, Kaveh Zenjanab M, Jahanban Esfahlan R. For and against tumor microenvironment: Nanoparticle-based strategies for active cancer therapy. Materials Today Bio. 2025;31:101626 [Crossref]

4- Hassan B, Elagamy S. Personalized medical recommendation system with machine learning. Neural Comput & Applic. 2025;37(9):6431 [Crossref]

5- Wang H, Huang J, Fang X, Liu M, Fan X, Li Y. Advances in next-generation sequencing (NGS) applications in drug discovery and development. Expert Opinion on Drug Discovery. 2025;20(4):537 [Crossref]

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