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Article
Publication date: 3 May 2023

Rucha Wadapurkar, Sanket Bapat, Rupali Mahajan and Renu Vyas

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific…

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Abstract

Purpose

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

Design/methodology/approach

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

Findings

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

Originality/value

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

Details

Data Technologies and Applications, vol. 58 no. 1
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 2 September 2014

Gurjeet Kaur Sahi and Rupali Mahajan

The purpose of this paper is to empirically test an integrated model incorporating the constructs of organisational commitment (OC), behavioural intentions (BI), actual turnover…

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Abstract

Purpose

The purpose of this paper is to empirically test an integrated model incorporating the constructs of organisational commitment (OC), behavioural intentions (BI), actual turnover behaviour (ATB) and telecom work characteristics (WC) so as to examine the impact of commitment on employees’ BI, whereby they wish to dissolve their relationship with the employment provider.

Design/methodology/approach

Structural equation modelling technique has been used to test the data collected through questionnaire from a sample of 139 employees including managers and executives across the hierarchy of an Indian telecom organisation named Aircel Dishnet Ltd from the Jammu and Kashmir circle head office in India.

Findings

The theoretical constructs were validated before incorporating the hypothetical structural model. SEM results indicate a good fit to the empirical data. The findings confirmed that affective, continuance and normative commitments lay significant impact on employees’ OC. Also, commitment influence attitudes, subjective norms and perceived behavioural control, thereby affecting the BI of the employees. An indirect significant impact of OC on the ATB was also revealed. A partial mediation of WC was also found between BI and ATB.

Research limitations/implications

The generalisability of the study is limited as the sample concentrates to one organisation of a single industry in India.

Practical implications

The study provides insights for the policy makers to create and develop mechanisms and programmes leading to the enhancement of affective OC for employee retention.

Originality/value

The model clearly explains telecom employees’ OC and its impact on the ATB through their BI. Though the findings do not reveal any component of commitment to lay more impact on OC, it exhibits higher career commitment than OC among the telecom employees.

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