Multi-omics data integration by generative adversarial network.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ECG data correspond to normal and abnormal states. By addressing the challenges of limited biophysical data, including privacy concerns and restricted volunteer availability, our model generates realistic synthetic waveforms learned from real data. Combining real and synthetic datasets improved classification accuracy from 92% to 98.45%, highlighting the benefits of dataset augmentation for machine learning performance. The WGAN-GP model achieved 96.84% classification accuracy for synthetic EEG data representing relaxation states and optimal accuracy for concentration states when classified using a fusion of convolutional neural networks (CNNs). A 50% combination of synthetic and real EEG data yielded the highest accuracy of 98.48%. For EEG signals, the real dataset consisted of 60-s recordings across four channels (TP9, AF7, AF8, and TP10) from four individuals, providing approximately 15,000 data points per subject per state. For ECG signals, the dataset contained 1200 real samples, each comprising 140 data points, representing normal and abnormal states. WGAN-GP outperformed a basic generative adversarial network (GAN) in generating reliable synthetic data. For ECG data, a support vector machine (SVM) classifier achieved an accuracy of 98% with real data and 95.8% with synthetic data. Synthetic ECG data improved the random forest (RF) classifier’s accuracy from 97% with real data alone to 98.40% when combined with synthetic data. Statistical significance was assessed using the Wilcoxon signed-rank test, demonstrating the robustness of the WGAN-GP model. Techniques such as discrete wavelet transform, downsampling, and upsampling were employed to enhance data quality. This method shows significant potential in addressing biophysical data scarcity and advancing applications in assistive technologies, human-robot interaction, and mental health monitoring, among other medical applications.

<p>High data collection costs and complicated data access regulations increase the demand for synthetic data. Generative Adversarial Networks (GANs) are a novel generative framework with great potential for high quality synthetic data generation. GANs formulate the true distribution of data implicitly, and the success of GANs are often measured based on the similarity of generated data to this true distribution. GANs were originally designed to work with continuous data. However, many important real-world datasets such as medical images involve discontinuous distributions. GAN training for discontinuous distributions is relatively more challenging, as the training procedure often suffers from instability and mode collapse issues. This dissertation focuses on designing novel GAN architectures to generate representative synthetic image data, and proposes new structures to alleviate GANs' mode collapse issue. As part of this thesis, novel applications of image data generation with GANs have been also investigated for important problems arising in the telecommunication industry and medical domain. Specifically, we first explore various GAN structures to generate engineered electromagnetic surfaces. We consider the continuous approximation of the data and explore the capabilities of feed-forward and convolutional GANs for synthetic data generation. Next, we introduce a novel GAN architecture to address the problem of mode collapse in GAN training. The proposed structure incorporates a third network that penalizes the generator for generating low diversity samples. Lastly, we study the challenging problem of object generation in 3D space using GANs, and we propose extensions to existing 3D GAN structures to generate connected 3D volumes. Additionally, we explore a more challenging version of this 3D volume generation problem by generating connected volumes packed with spheres. This research has applications in radiosurgery treatment planning, and the proposed 3D GAN structure can help generate rare, unseen 3D tumor volumes and information on how to treat these tumors. Accordingly, our analysis contributes to overcoming data scarcity issues (e.g., due to privacy considerations) for an important practical problem in the medical domain.</p>

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

BackgroundThe exponential growth in patient data collection by healthcare providers, governments, and private industries is yielding large and diverse datasets that offer new insights into critical medical questions. Leveraging extensive computational resources, Machine Learning and Artificial Intelligence are increasingly utilized to address health-related issues, such as predicting outcomes from Electronic Health Records and detecting patterns in multi-omics data. Despite the proliferation of medical devices based on Artificial Intelligence, data accessibility for research is limited due to privacy concerns. Efforts to de-identify data have met challenges in maintaining effectiveness, particularly with large datasets. As an alternative, synthetic data, that replicate main statistical properties of real patient data, are proposed. However, the lack of standardized evaluation metrics complicates the selection of appropriate synthetic data generation methods. Effective evaluation of synthetic data must consider resemblance, utility and privacy, tailored to specific applications. Despite available metrics, benchmarking efforts remain limited, necessitating further research in this area.ResultsWe present SynthRO (Synthetic data Rank and Order), a user-friendly tool for benchmarking health synthetic tabular data across various contexts. SynthRO offers accessible quality evaluation metrics and automated benchmarking, helping users determine the most suitable synthetic data models for specific use cases by prioritizing metrics and providing consistent quantitative scores. Our dashboard is divided into three main sections: (1) Loading Data section, where users can locally upload real and synthetic datasets; (2) Evaluation section, in which several quality assessments are performed by computing different metrics and measures; (3) Benchmarking section, where users can globally compare synthetic datasets based on quality evaluation.ConclusionsSynthetic data mitigate concerns about privacy and data accessibility, yet lacks standardized evaluation metrics. SynthRO provides an accessible dashboard helping users select suitable synthetic data models, and it also supports various use cases in healthcare, enhancing prognostic scores and enabling federated learning. SynthRO’s accessible GUI and modular structure facilitate effective data evaluation, promoting reliability and fairness. Future developments will include temporal data evaluation, further broadening its applicability.

High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs’ potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients’ privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.

With the significant increase in the amount of solar energy sources, the variation in the production caused by the stochastic nature of these sources becomes a larger problem for the electrical grid. Having a large percentage of installed units, therefore, requires some type of both short- and long-term planning of energy production. Most planning and forecasting tools require a large amount of solar energy data for the location in question and oftentimes that data is difficult to obtain. In this paper, we propose a method for generating synthetic solar irradiance data and test its applicability on forecasting and long-term planning problems. Synthetic data are generated using various generative adversarial networks and masked autoencoder based models, which are all trained on a dataset of solar irradiance data measured on the territory of the city of Banja Luka in Bosnia and Herzegovina. The synthetic data are evaluated both on different types of short-term forecasting tools and a long-term solar plant simulation tool, and the obtained results are compared with the cases where real data is used. By comparing the results, we have concluded that by using generative models we are able to generate synthetic data which is not only realistic, but applicable for both of these uses.

Training data is an essential ingredient within supervised learning, but time consuming, expensive and for some applications impossible to acquire. A possible solution is to use synthetic training data. However, the domain shift of synthetic data makes it challenging to obtain good results when used as training data for deep learning models. It is therefore of interest to refine synthetic data, e.g. using image-to-image translation, to improve results. The aim of this work is to compare different methods to do image-to-image translation of synthetic training data of thermal IR-images using generative adversarial networks (GANs). Translation is done both using synthetic thermal IR-images alone, as well as including pixelwise depth and/or semantic information. To evaluate, we propose a new measure based on the Frechet Inception Distance, adapted to work for thermal IR-images. We show that by adapting a GAN model to also include corresponding pixelwise depth data to each synthetic IR-image, the performance is improved compared to using only IR-images.

Breast cancer (BC) is the second leading cause of cancer-related death among women in the United States. Previous studies have shown that African American (AA) women have disproportionately high mortality rate in BC compared to European American (EA) women in the United States. Existing studies have suggested that transfer learning could reduce health disparities in BC by transferring information from a majority group (e.g., EA women) to a minority group (e.g., AA women). However, their performance may be decreased due to limited training patient samples and using single-omics data only. We recently presented a transfer learning method by integrating two multi-omics data for reducing cancer disparities. However, the integration model was not optimized, and its performance in reducing disparities was not robust. To address these concerns, we propose a novel weighted multi-modal transfer learning model that can optimize the integration of multi-omics data to alleviate racial disparities in BC. Specifically, we first calculated the patient-patient similarity by using the Pearson Correlation Coefficient (PCC), based on which we performed weighted integration of multi-omics data. Then, we performed a nested grid search method to find the optimal weights for each type of omics data, which were subsequently fed into a transfer learning model. To demonstrate the effectiveness of our model, we performed the tasks of predicting Disease-Specific Survival (DSS) prognosis for the 1098 female BC patient samples from The Cancer Genome Atlas (TCGA) database. Results suggested that our model with optimized weighted integration of three multi-omics (including mRNA, miRNA and methylation) data significantly outperformed previous multi-omics transfer learning models in terms of AUC. In addition, our multi-modal transfer learning model performed remarkably better than single-omics and two-omics-integration transfer learning models, suggesting that the inclusion of more diverse omics data contributes to a better reduction of racial disparities in BC. Furthermore, the performance of our transfer learning model was also superior to that of conventional mixed models and independent models. In summary, our proposed weighted multi-modal transfer learning model achieves far better performance than existing state-of-the-art methods for reducing health disparities in BC. We expect that our proposed model will provide a new way to reduce health disparities in BC diagnosis, prognosis, and treatment, and it can be extensible to alleviate health disparities in other types of cancer. Citation Format: Min-jeong Baek, Lusheng Li, Jieqiong Wang, Vimla Band, Shibiao Wan. A weighted multi-modal transfer learning model for alleviating racial disparities in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3620.

For many complex heterogeneous diseases, the underlying mechanisms contributing to pathogenesis is still unknown. Although prediction models for complex heterogeneous diseases have been trained on clinical features and imaging data, they may not reveal mechanisms contributing to pathogenesis. The development of human diseases often involves a highly interactive and dynamic system of molecular layers. Networks are a useful framework for representing cross-talk relationships of molecules in biological and disease pathways. Understanding complex biological networks is a fundamental and challenging issue in computational and systems biology. Constructing disease-specific networks is still lacking in many diseases even with current advances. It necessitates the development of methods to integrate protein and genetic interaction information from knowledgebases with omics data to reconstruct networks and develop predictive models to effectively integrate different omics data and network information while being interpretable. Biological networks are often inferred through Gaussian Graphical Models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to incorporate PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we developed an augmented high-dimensional graphical lasso (AhGlasso) method to incorporate edge weights in known protein-protein interactions with omics data for global network learning. This new method outperforms weighted graphical lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. Using proteomic data from a study on chronic obstructive pulmonary disease (COPD), we demonstrated that AhGlasso improves protein network inference compared to other approaches that incorporate PPI information. Recently Deep Learning including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification by integrating a known PPI network from existing databases and a single omics data set. We extended the existing ConvGNN framework to incorporate PPI in addition to multi-omics data to develop a prediction model for COPD. This model achieved significantly better accuracy compared to several conventional classification methods. We also demonstrated that the updated COPD-associated network with AhGlasso further improves the prediction accuracy. Moreover, we applied explainable machine learning methods for interpretation and identified significant genes/proteins and important subnetworks in the ConvGNN model for COPD prediction. Our study suggests the benefits of using ConvGNN for the prediction of heterogeneous complex diseases by integrating multi-omics data and interaction networks.

BackgroundRecent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data.ObjectiveTo introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis.MethodsThe literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on.ConclusionThis study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.

Traditional datasets are often limited, which can affect the accuracy of analyses. Additionally, the use of students’ real data raises privacy concerns. Generative Adversarial Networks (GANs) offer a solution by generating synthetic data that closely mirrors real-world data without compromising sensitive information. The research explored the application of GANs to enhance student performance datasets by addressing challenges related to data scarcity and privacy in educational research. In the research, GANs were utilized to generate synthetic student performance data. The accuracy of the data was assessed using Mean Absolute Percentage Error (MAPE), with values ranging from 0.004% to 19.92% across various statistical measures and means. These results demonstrated the reliability of the synthetic data, making it suitable for further analysis. The synthetic datasets were then analyzed using the Apriori algorithm, a well-known method in data mining for discovering significant patterns and relationships. A lower bound minimum support of 0.1 (10%) and a minimum confidence threshold of 0.6 (60%) were applied, ensuring the identification of meaningful associations. The analysis reveals important patterns and relationships among student attributes and behaviors. The research highlights the potential of GANs to advance data-driven educational research. By generating high-quality synthetic data, GANs allow researchers to conduct comprehensive analyses while addressing privacy concerns. The research contributes to the methodological approach to data augmentation in education, offering new opportunities for ethical and robust research.

Cancer is one of the leading causes of mortality worldwide, and its inherent diversity and heterogeneity pose significant challenges in early diagnosis, drug development, and prognosis. Accurate identification of cancer subtypes has therefore become a critical aspect of personalized cancer treatment. With the advancement of omics technologies, multi-omics data offer a more comprehensive understanding of cancer's underlying mechanisms. However, effectively integrating such diverse datasets to identify cancer subtypes remains a significant challenge. In this study, we introduce a novel approach, termed HRAGNN, for cancer subtype identification through the integration of multi-omics data. HRAGNN first constructs an integrated graph and then leverages Relational Attention Mechanism (RAM) and Graph Neural Network (GNN) to capture complex features across these multi-omics layers. Subsequently, the Multi-view Fusion Network (MVFN) is employed to fuse the features derived from the different omics data. We evaluated the performance of HRAGNN on three datasets, comparing it with other existing methods. The experimental results demonstrate that HRAGNN outperforms other approaches in terms of several key evaluation metrics.

Ovarian aging occurs earlier than the aging of many other organs and has a lasting impact on women's overall health and well-being. However, effective interventions to slow ovarian aging remain limited, primarily due to an incomplete understanding of the underlying molecular mechanisms and drug targets. Recent advances in omics data resources, combined with innovative computational tools, are offering deeper insight into the molecular complexities of ovarian aging, paving the way for new opportunities in drug discovery and development. This review aims to synthesize the expanding multi-omics data, spanning genome, transcriptome, proteome, metabolome, and microbiome, related to ovarian aging, from both tissue-level and single-cell perspectives. We will specially explore how the analysis of these emerging omics datasets can be leveraged to identify novel drug targets and guide therapeutic strategies for slowing and reversing ovarian aging. We conducted a comprehensive literature search in the PubMed database using a range of relevant keywords: ovarian aging, age at natural menopause, premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), genomics, transcriptomics, epigenomics, DNA methylation, RNA modification, histone modification, proteomics, metabolomics, lipidomics, microbiome, single-cell, genome-wide association studies (GWAS), whole-exome sequencing, phenome-wide association studies (PheWAS), Mendelian randomization (MR), epigenetic target, drug target, machine learning, artificial intelligence (AI), deep learning, and multi-omics. The search was restricted to English-language articles published up to September 2024. Multi-omics studies have uncovered key mechanisms driving ovarian aging, including DNA damage and repair deficiencies, inflammatory and immune responses, mitochondrial dysfunction, and cell death. By integrating multi-omics data, researchers can identify critical regulatory factors and mechanisms across various biological levels, leading to the discovery of potential drug targets. Notable examples include genetic targets such as BRCA2 and TERT, epigenetic targets like Tet and FTO, metabolic targets such as sirtuins and CD38+, protein targets like BIN2 and PDGF-BB, and transcription factors such as FOXP1. The advent of cutting-edge omics technologies, especially single-cell technologies and spatial transcriptomics, has provided valuable insights for guiding treatment decisions and has become a powerful tool in drug discovery aimed at mitigating or reversing ovarian aging. As technology advances, the integration of single-cell multi-omics data with AI models holds the potential to more accurately predict candidate drug targets. This convergence offers promising new avenues for personalized medicine and precision therapies, paving the way for tailored interventions in ovarian aging. Not applicable.

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand.