Prioritization of genes for translation: a computational approach

ABSTRACT

Introduction

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

Areas covered

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

Expert Opinion

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Graphical Abstract

KEYWORDS:

• Computational approach
• potential diagnostic predictor
• machine learning
• deep learning
• Alzheimer’s disease
• Parkinson’s disease
• glioblastoma

Article highlights

• Clinical informatics offers the potential to understand the intricate interplay of biological factors contributing to disease disparities.

• Genome sequencing of disease-associated genes has revealed valuable insights into the underlying mechanisms of various diseases.

• Advancements in clinical informatics and genomics provide a wealth of data that can be harnessed to develop more effective strategies for disease prevention, evidence-based disease management, and personalized medicine approaches.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Authors contributions

SC. da Silva Rosa, A Barzegar Behrooz, and S Guedes, wrote the draft. A Barzegar Behrooz prepared the biological section draft. SC. da Silva Rosa designed and generated the Graphical Abstract and Figures 1–4. R Vitorino and S Ghavami finalized the paper and supervised the project.

Additional information

Funding

This work was financed by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., European Union, Quadro de Referência Estratégico Nacional (QREN), Fundo Europeu de Desenvolvimento Regional (FEDER) and Programa Operacional de Competitividade (COMPETE), through Cardiovascular R&D Center [UIDB/00051/2020 and UIDP/00051/2020], RISE [LA/P/0053/2020], iBiMED [UIDB/04501/2020, POCI-01-0145-FEDER-007628] and LAQV-REQUIMTE [UIDB/50006/2020]. We thank the Canadian Institute of Health Research (CIHR) for funding Dr. Simone C. da Silva Rosa’s Postdoctoral Fellowship [FRN 176508].