Multimodal data integration for oncology in the era of deep neural networks: a review
Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …
screening and diagnostic imaging to digitized histopathology slides to various types of …
Prot2text: Multimodal protein's function generation with gnns and transformers
In recent years, significant progress has been made in this field of protein function prediction
with the development of various machine-learning approaches. However, most existing …
with the development of various machine-learning approaches. However, most existing …
A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions
Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …
analysis and modeling of intricate relationships present in data structured as graphs. In …
Designing interpretable deep learning applications for functional genomics: a quantitative analysis
Deep learning applications have had a profound impact on many scientific fields, including
functional genomics. Deep learning models can learn complex interactions between and …
functional genomics. Deep learning models can learn complex interactions between and …
[HTML][HTML] Proximogram—A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling
The increasing availability of patient-derived multimodal biological data for various diseases
has opened up avenues for finding the optimal methods for jointly leveraging the information …
has opened up avenues for finding the optimal methods for jointly leveraging the information …
LASSO–MOGAT: a multi-omics graph attention framework for cancer classification
The application of machine learning (ML) methods to analyze changes in gene expression
patterns has recently emerged as a powerful approach in cancer research, enhancing our …
patterns has recently emerged as a powerful approach in cancer research, enhancing our …
Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities
H Zhang, C Lin, Y Chen, X Shen… - Journal of Cellular …, 2025 - Wiley Online Library
Cancer is a complex disease driven by mutations in the genes that play critical roles in
cellular processes. The identification of cancer driver genes is crucial for understanding …
cellular processes. The identification of cancer driver genes is crucial for understanding …
Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification
Multi-omics data is increasingly being utilized to advance computational methods for cancer
classification. However, multi-omics data integration poses significant challenges due to the …
classification. However, multi-omics data integration poses significant challenges due to the …
A controllability reinforcement learning method for pancreatic cancer biomarker identification
Y Wang, J Hong, Y Lu, N Sheng, Y Fu… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Pancreatic cancer is one of the most malignant cancers with rapid progression and poor
prognosis. The use of transcriptional data can be effective in finding new biomarkers for …
prognosis. The use of transcriptional data can be effective in finding new biomarkers for …
Multilayer network approaches to omics data integration in Digital Twins for cancer research
This review examines current and potential applications of DTs in healthcare, focusing on
the integration of multi-omics data using multilayer network approaches in cancer research …
the integration of multi-omics data using multilayer network approaches in cancer research …