A Bibliometric Analysis of the Use of Machine Learning Methods in Variant Effect Prediction

Year 2025, Volume: 8 Issue: 2, 632 - 651, 12.03.2025

Gülbahar Merve Şilbir , Burçin Kurt

https://doi.org/10.47495/okufbed.1505771

Abstract

Studies using computational methods such as artificial intelligence, machine learning and deep learning to predict the effect of variants occurring in the human genome on the phenotype have increased recently. The aim of this study is to provide an overview of scientific research using machine learning methods in variant effect prediction using the bibliometric method. For this purpose, the Web of Science Core Collection (WoSCC) database was used to access the relevant literature in the study. Countries, institutions, authors, journals, quotations and keywords were analyzed using the "bibliometrix" library in the R-Studio program. As a result of the analysis, it has been seen that the popularity of scientific publications on the use of machine learning methods in variant effect prediction has increased in recent years, and the largest share of this increase is due to joint research by institutions in the United States with China, Germany, England and Australia. In the publication production in this field, it can be seen that the publications made by researchers Majid Masso and Yuedong Yang have spread over a long period of time, and when looking at the publications made in the last few years, researchers Yongguo Liu, Yun Zhang, Haicang Zhang and Jiajing Zhu come to the fore. It was observed that the most cited author was researcher Jian Zhou (1.116). Although there has been an increasing trend in publications in this field in recent years, it has been determined that older publications are still cited more. Therefore, it has become clear that there is still a need to conduct further research in this field, to strengthen international cooperation and communication, and to increase the quality of the literature by gaining experience.

Keywords

Bibliometric Analysis, Bibliometrix, Variant Effect Prediction, Machine Learning

Ethical Statement

This article was presented as an oral presentation at the 15th Medical Informatics Congress held in Trabzon on 30-31 May 2024.

Varyant Etki Tahmininde Makine Öğrenmesi Yöntemlerinin Kullanımına Yönelik Bibliyometrik Bir Analiz

Abstract

İnsan genomunda oluşan varyantların fenotip üzerindeki etkisinin tahmin edilmesinde yapay zeka, makine öğrenmesi ve derin öğrenme gibi hesaplamalı yöntemlerin kullanıldığı çalışmalar son zamanlarda giderek artmıştır. Bu çalışmanın amacı bibliyometrik yöntem kullanılarak varyant etki tahmininde makine öğrenmesi yöntemlerinin kullanıldığı bilimsel araştırmalara genel bir bakış sunmaktır. Bu amaç doğrultusunda çalışmada ilgili literatüre ulaşmak için Web of Science Core Collection (WoSCC) veritabanı kullanılmıştır. Ülkeler, kurumlar, yazarlar, dergiler, alıntılar ve anahtar kelimeler R-Studio programında “bibliometrix” kütüphanesi kullanılarak analiz edilmiştir. Yapılan analiz sonucunda göre varyant etki tahmininde makine öğrenmesi yöntemlerinin kullanımına ilişkin yapılan bilimsel yayınların son yıllarda popülerliğinin giderek arttığı ve bu artıştaki en büyük payın United States’te yer alan kurumların China, Germany, England ve Australia ile ortak araştırmalara bağlı olduğu görülmüştür. Bu alanda yayın üretimindeki araştırmacılara bakıldığında Majid Masso ve Yuedong Yang tarafından yapılan yayınların uzun bir zamana yayıldığı, son birkaç yıldır yapılan yayınlardaki araştırmacılara bakıldığında ise Yongguo Liu, Yun Zhang, Haicang Zhang ve Jiajing Zhu’nun ön plana çıktığı görülmektedir. En çok atıf alan yazarın ise Jian Zhou (1.116) olduğu görülmüştür. Son yıllarda bu alandaki yayınlarda artan bir eğilim olmasına rağmen yine de eski yayınlara daha çok atıf yapıldığı belirlenmiştir. Dolayısıyla bu alanda halen daha araştırma yapılmasına, uluslararası işbirliğinin ve iletişimin güçlendirilmesine ve deneyim kazanılarak literatür kalitesinin arttırılmasına ihtiyaç duyulduğu ortaya çıkmıştır.

Keywords

Bibliyometrik analiz, Bibliometrix, Varyant etki tahmini, Makine öğrenmesi

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