文献
J-GLOBAL ID:202202217668230112
整理番号:22A0902724
抗血管新生薬研究のための新しい数値および人工知能に基づくアプローチ:エンドスタチン【JST・京大機械翻訳】
A novel numerical and artificial intelligence based approach to study anti-angiogenic drugs: Endostatin
著者 (8件):
Akbarpour Ghazani Mehran
(Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran)
,
Akbarpour Ghazani Mehran
(Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran)
,
Soltani M.
(Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran)
,
Soltani M.
(Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran)
,
Soltani M.
(Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada)
,
Soltani M.
(Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada)
,
Jalali Peyman
(Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran)
,
Hassannejad Reza
(Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran)
資料名:
Applied Mathematical Modelling
(Applied Mathematical Modelling)
巻:
105
ページ:
258-283
発行年:
2022年
JST資料番号:
H0624A
ISSN:
0307-904X
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)