Computational characterization and identification of human polycystic ovary syndrome genes

Abstract

Human polycystic ovary syndrome (PCOS) is a highly heritable disease regulated by genetic and environmental factors. Identifying PCOS genes is time consuming and costly in wet-lab. Developing an algorithm to predict PCOS candidates will be helpful. In this study, for the first time, we systematically analyzed properties of human PCOS genes. Compared with genes not yet known to be involved in PCOS regulation, known PCOS genes display distinguishing characteristics: (i) they tend to be located at network center; (ii) they tend to interact with each other; (iii) they tend to enrich in certain biological processes. Based on these features, we developed a machine-learning algorithm to predict new PCOS genes. 233 PCOS candidates were predicted with a posterior probability >0.9. Evidence supporting 7 of the top 10 predictions has been found.

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

“…In recent years, the development of machine learning algorithms and the availability of gene expression data in public databases provide approaches to infer biomarkers for disease diagnosis or prognosis in a wide range of fields [30][31][32][33]. In the field of PCOS, some attempts have been made to explore a better way for PCOS diagnosis by using various machine learning algorithms [34][35][36][37][38], among which, suitable algorithms using some clinical data, such as survey data [35] or pelvic ultrasound data, were used [37]. An algorithm was ever constructed to predict new PCOS candidates using the data from Polycystic Ovary Syndrome Database (PCOSDB; http://www.pcosdb.net/) [39] and the KnowledgeBase on Polycystic Ovary Syndrome (PCOSKB; http://pcoskb.bicnirrh.res.in) [36,40].…”

“…In the field of PCOS, some attempts have been made to explore a better way for PCOS diagnosis by using various machine learning algorithms [34][35][36][37][38], among which, suitable algorithms using some clinical data, such as survey data [35] or pelvic ultrasound data, were used [37]. An algorithm was ever constructed to predict new PCOS candidates using the data from Polycystic Ovary Syndrome Database (PCOSDB; http://www.pcosdb.net/) [39] and the KnowledgeBase on Polycystic Ovary Syndrome (PCOSKB; http://pcoskb.bicnirrh.res.in) [36,40]. Another study converted the ovary microarray data of GEO database to the gene set regularity (GSR) indices, and the GSR indices were then computed by the modified differential rank conversion algorithm [38].…”

Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness

“…With the improvement of our understanding in terms of ovarian cancer subtypes’ composition, some histological specific therapeutic drugs can be used to achieve the effect of precision-targeted therapy. Aiming at the high-risk genes related to ovarian cancer is helpful for the diagnosed patients to carry out risk reduction assessment and preventive surgery ( Zhang et al, 2018 ).…”

PCOcare: PCOS Detection and Prediction using Machine Learning Algorithms

“…PCOS occurs as a result of hormonal imbalances. In this disorder, the ovaries develop small collections of fluids called follicles (cysts) and fail to release eggs, which is why women suffering from PCOS tend to have complications in conceiving [Zhang, 2018]. A lot of women have PCOS, but do not get diagnosed with it at an earlier stage.…”

“…Hyperprolactinemia, Cushing's syndrome and non-classic congenital adrenal hyperplasia are few examples. [Zhang, 2018] have used different machine learning algorithms like K-nearest neighbour (KNN), decision tree and SVM with different kernel functions to predict PCOS from the identification of new genes. [P. Mehrotra, 2011] have used machine learning algorithms like Bayes and Logistic Regression (LR) to develop an automated system that will act as an assisted tool for the doctor for saving considerable time in examining the patients and hence reducing the delay in diagnosing the risk of PCOS by using metabolic and clinical factors in a feature vector.…”