Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
Mousavi Milad; Manshadi Mahsa Dehghan; Soltani Madjid; Kashkooli Farshad M.; Rahmim Arman; Mosavi Amir; Kvasnica Michal; Atkinson Peter M.; Kovács Levente; Koltay Andras; Kis Norbert; Adeli Hojjat
MTMT : 32792285
Megjelenés dátuma : 2022
Folyóirat címe : Computers in Biology and Medicine
Évfolyam : 2022
Oldalszám : 1-35
Dokumentum típusa : folyóiratcikk
Kulcsszó : solid tumor, tumor growth, anti-angiogenic drugs, bevacizumab, ranibizumab, brolucizumab, artificial intelligence, cancer, Orvostudományok, Elméleti orvostudományok
Absztrakt :
Conclusion: We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
