## 2 Related Works
The study [20] considers the application of deep learning methods to navigate the challenging landscape of breast cancer subtyping, with its intrinsic heterogeneity and consequent varied prognostic outcomes, by introducing moBRCA-net, an interpretable deep learning-based classification framework that harnesses multi-omics datasets--specifically, by integrating gene expression, DNA methylation, and microRNA expression data while respecting the biological interrelations among them. By employing a self-attention module for each omics dataset to ascertain the relative importance of each feature and subsequently transforming these features into new representations based on learned importance, moBRCA-net aims to adeptly predict breast cancer subtypes, demonstrating notably enhanced performance and effective multi-omics integration in comparison with other methods, as substantiated by experimental results. Despite the promising advancements brought forth by moBRCA-net, the potential limitations of this approach, such as the need for comprehensive validation across varied demographic cohorts and the adaptability to accommodate evolving omics data types, warrant careful consideration and further exploration to ensure its applicability and reliability in real-world clinical settings. In [21], the authors proposed employing machine learning and deep learning methodologies, specifically utilizing an autoencoder neural network and various statistical models, to identify prognostic biomarkers predictive of time to development and survival stratification in oral cancer (OC) by analyzing the gene expression profiles of 86 patients from the GSE26549 dataset. The approaches used allowed for the extraction of 100 encoded features, of which 70 were found to be significantly related to time to OC development, and further analyses identified two survival risk groups and 21 top genes, demonstrating the overall random forest classifier accuracy of 0.916 over the test set, thus potentially illuminating transcriptional biomarkers pertinent to determining high-risk OC patients and offering promising therapeutic targets. However, despite the insightful findings, the study bears limitations, such as the restricted patient sample size and the need for thorough validation across diverse demographic cohorts and varied types of OC, ensuring the identified biomarkers' broader applicability and efficacy in prospective, retrospective, and real-world clinical settings. In [22], the authors considered the application of deep learning methods, such as convolutional neural network (CNN), deep neural network (DNN), and Long Short-Term Memory (LSTM) recurrent neural network (RNN), to address the critical need for efficient and rapid computational models for breast cancer prognosis, thereby proposing an ensemble model for breast cancer survivability prediction (EBCSP) that harnesses multi-modal data and stacks the outputs of various neural networks dedicated to distinct data modalities. By employing a CNN for clinical modalities, a DNN for copy number variations (CNV), and an LSTM-RNN for gene expression modalities and subsequently utilizing the individual models' outputs for binary classification based on survivability using the random forest method, the EBCSP model successfully outperforms models reliant on a single data modality for prediction, as well as existing benchmarks. Despite its promising results, it is imperative to acknowledge inherent limitations, such as the necessity for comprehensive validation across diverse demographic and clinical cohorts, as well as the evaluation of model robustness and accuracy in real-world practical medical settings, to ascertain its efficacy and applicability. The studies [23; 24; 25] delve into the meticulous application of recurrent neural networks (RNNs) for diverse approaches to understanding and classifying gene regulatory networks (GRNs) and cancer detection from gene expression data.