A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Abstract

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

Radiomics in oncology: A mini-review of principles, applications and challenges

“…Robust feature selection, regularization techniques, and validation strategies are required to mitigate overfitting and improve the generalizability of radiomics models [15,22]. Recent advances in radiomics have focused on addressing its limitations and harnessing its full potential through innovative approaches and technologies such as multi-modal radiomics, deep learning approaches, and integration with genomics and proteomics [24,25]. However, the suboptimal quality in reporting current radiomics studies, as assessed based on the RQS by studies, suggests a generally low standard, with an overall average score of approximately 50% [15].…”

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

“…In Table 2 , a comprehensive analysis of the scope of review articles indicates that existing studies can be classified into three distinct groups. (I) Nine review papers primarily focus on the application of DL algorithms in survival prediction (Ahmed, 2005 ; Bakasa and Viriri, 2021 ; Kvamme and Borgan, 2021 ; Pobar et al, 2021 ; Kantidakis et al, 2022 ; Altuhaifa et al, 2023 ; Salerno and Li, 2023 ; Wekesa and Kimwele, 2023 ; Wiegrebe et al, 2023 ), (II) seven review papers summarize the application of ML algorithms in survival prediction (Gupta et al, 2018 ; Lee and Lim, 2019 ; Boshier et al, 2022 ; Guan et al, 2022 ; Mo et al, 2022 ; Wissel et al, 2022 ; Feldner-Busztin et al, 2023 ), andsix review papers summarize survival prediction methods from three different categories namely statistical, ML, and DL methods (Bashiri et al, 2017 ; Herrmann et al, 2021 ; Tewarie et al, 2021 ; Westerlund et al, 2021 ; Deepa and Gunavathi, 2022 ; Rahimi et al, 2023 ).…”

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

“…The advent of deep learning technologies has revolutionized the analysis of multi-omics data, particularly in complex disorders such as cancer and neurodegenerative diseases. Deep Neural Networks (DNNs) and Graph Neural Networks (GNNs), with their ability to capture complex patterns across multiple layers of information, have proven instrumental in identifying novel biomarkers, deciphering cancer progression, and predicting responses to therapies [5][6][7][8] . This has opened new avenues for understanding tumor biology and has been a catalyst in the movement toward personalized and precise cancer treatments [9][10][11] .…”