Divergence Based Feature Selection for Pattern Recognizing of the Performance of Intrusion Detection in Mobile Communications Merged with the Computer Communication Networks

Autor: N. Chitra, Safinaz. S, K. Bhanu Rekha

Publikováno v: International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 75-88

The Feature Selection concept is the procedure where in the data is simplified removing the irrelevant features. Divergence method is another strategy of method where in the relation among the attributes and class is measured to understand their cont

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f378ca5ea5de3d4883a5daff39dde63a
https://doi.org/10.3991/ijim.v17i04.37733

Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer

Autor: Nosayba Al-Azzam, Ibrahem Shatnawi

Publikováno v: Annals of Medicine and Surgery

Background Breast cancer disease is the most common cancer in US women and the second cause of cancer death among women. Objectives To compare and evaluate the performance and accuracy of the key supervised and semi-supervised machine learning algori

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be49b4fe3f3424a9c1a0ff86cada706c
https://doi.org/10.1016/j.amsu.2020.12.043

Deep graph embedding for prioritizing synergistic anticancer drug combinations

Autor: Pingzhao Hu, Peiran Jiang, Ted M. Lakowski, Zhenyuan Fu, Shujun Huang, Zexuan Sun

Publikováno v: Computational and Structural Biotechnology Journal, Vol 18, Iss, Pp 427-438 (2020)
Computational and Structural Biotechnology Journal

Graphical abstract
Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7c0090da5ca0e0d96b490baf96f5081
http://www.sciencedirect.com/science/article/pii/S200103701930368X

Exhaustive non-synonymous variants functionality prediction enables high resolution characterization of the neurofibromin architecture

Autor: Shay Ben-Shachar, Deeann Wallis, Ofer Isakov, D. Gareth Evans

Publikováno v: Isakov, O, Wallis, D, Evans, D G & Ben-shachar, S 2018, ' Exhaustive non-synonymous variants functionality prediction enables high resolution characterization of the neurofibromin architecture ', EBioMedicine, vol. 36 . https://doi.org/10.1016/j.ebiom.2018.09.039
EBioMedicine

Background Neurofibromatosis type I (NF1) is caused by heterozygous loss-of-function variants in the NF1 gene encoding neurofibromin which serves as a tumor suppressor that inhibits RAS signaling and regulates cell proliferation and differentiation.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82854d153e0aebf0fdf4276732ba418c
https://doi.org/10.1016/j.ebiom.2018.09.039

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.