Machine Learning techniques to identify potential drug targets for Anti-epileptic drugs

Kumar, Arvind; Janaki, Chintalapati; Hosur, Mahesh; Pal, Supriya N.

doi:10.1109/icmlant50963.2020.9355971

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…After performing the quality assessment, back-word snowballing to extract the relevant articles from the references of the selected articles. The articles by Kumar et al (2020) and Ilyas et al (2019) have been shortlisted after performing the inclusion exclusion criteria and quality assessment.…”

Section: Survey Methodologymentioning

confidence: 99%

“…(d) specificity (e) availability of developed tool (f) comparison on independent data and the type of glycosylation as mentioned in Table 12 . It is observed, most of the authors ( Kotidis & Kontoravdi, 2020 ; Sha et al, 2019 ; Park et al, 2019 ; Antonakoudis et al, 2021 ; Zhang et al, 2021b ; Wang et al, 2017 ; Kumar et al, 2020 ; Ilyas et al, 2019 ; Sugár et al, 2021 ; Bojar et al, 2021a ; Perpetuo et al, 2021 ; Huang & Li, 2018 ) did not provide the results or they did not follow provided performance metrics in their research. The authors ( Akmal, Rasool & Khan, 2017 ; Chien et al, 2020 ; Taherzadeh et al, 2019 ; Tran, Pham & Ou, 2021 ; Liu et al, 2019 ; Li et al, 2019 ; Pitti et al, 2019 ; Hwang et al, 2020 ; Le, Sandag & Ou, 2018 ; Dimeglio et al, 2020 ; Magaret et al, 2019 ; Ruiz-Blanco et al, 2017 ) mentioned most of the performance metrics specific to N-Linked sites identification and out of these, authors Chien et al (2020) and Hwang et al (2020) has not provide the data set on which experiments have performed.…”

Section: Assessment Of Q5mentioning

confidence: 99%

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An analytical study on the identification of N-linked glycosylation sites using machine learning model

Akmal

Hassan

Shoaib

et al. 2022

PeerJ Computer Science

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N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

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Section: Survey Methodologymentioning

confidence: 99%