Prediction of Cancer Clinical Endpoints Using Deep Learning and RPPA Data

Zenbout, Imene; Bouramoul, Abdelkrim; Meshoul, Souham

doi:10.1007/978-3-030-96311-8_7

Cited by 1 publication

(1 citation statement)

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“…ML is considered an extension of classical statistics that may learn from data automatically to enhance their performance, Recently, ML has contributed successfully to the medical domain. Zenbout et al [63] proposed Deep learning architecture to model RPPA data for cancer clinical endpoints prediction, as well as, several studies have been pointed out by Abdelkrim et al [62] in their systematic literature review to prove the usefulness of ML techniques in drug discovery research, advancements of ML have extended to address addiction disorder issues. There are two sorts of addictions: substance use disorder (SUD) and non-substance disorder (NSUD).…”

Section: Fieldmentioning

confidence: 99%

A Review on Recent Machine Learning Applications for Addiction Disorders

BOUHADJA

Bouramoul

2022

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)

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Constant contributions of Machine Learning (ML) technology in health sciences has extended to solve addiction disorders problems, whether to detect symptoms or predict risks and treatment outcomes. This article presents an updated review related to the application of ML techniques for addiction disorders, the selected works covered substance addiction (N=18 studies) and non-substance addiction (N=3 studies), and were divided into three categories prognosis, diagnosis, and predicting treatment success. To provide strong evidence about the potential of ML methods to accelerate early prevention and intervention, ultimately aiming to pave the way for further applications of ML approaches in this field.

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Section: Fieldmentioning

confidence: 99%