We deliver comprehensive multi-omics data integration powered by advanced AI algorithms. Enginoma combines variational autoencoders, graph neural networks, and attention mechanisms to harmonize heterogeneous datasets across genomics, transcriptomics, proteomics, and metabolomics for biomarker discovery and precision medicine.
Single-omics approaches capture only a fraction of biological complexity. By integrating multiple molecular layers using deep learning, our platform provides a holistic view of cellular systems, revealing regulatory mechanisms invisible to any individual data type.
We combine proven bioinformatics tools with cutting-edge AI to deliver accurate, comprehensive multi-omics integration.
Our VAE-based integration learns low-dimensional latent representations that capture shared variation across omics layers while preserving modality-specific signals.
GNN-based integration leverages biological network structure to model interactions between genes, proteins, and metabolites across omics layers.
Transformer-based architectures dynamically weight contributions from each omics layer, enabling interpretable and adaptive multi-modal fusion.
From raw omics data to actionable biological insights, our integration pipeline covers every step.
Concatenate raw omics data before feature extraction. Ideal for datasets with strong cross-modal correlations.
Process each omics modality through dedicated neural networks before fusion for heterogeneous data types.
Independent analysis of each omics layer with results combined post-hoc for specialized processing.
Identify multi-omics signatures and cross-platform biomarkers for disease diagnosis and prognosis.
Traditional single-omics approaches provide fragmented views of biological systems. Our AI-powered integration reveals the full picture of cellular mechanisms.
Integrate multiple molecular layers simultaneously to capture gene-regulatory, protein-level, and metabolic interactions in context.
Multi-omics biomarkers consistently outperform single-omics markers for disease classification and patient stratification.
Move beyond correlations to identify causal regulatory mechanisms driving biological phenotypes.
Our AI architectures handle missing data across modalities, accommodating real-world incomplete datasets.
See how our multi-omics integration has helped researchers achieve breakthrough discoveries.
Integrated genomics, transcriptomics, and proteomics data to identify novel cancer subtypes with distinct therapeutic vulnerabilities.
OncologyPrecision MedicineCombined proteomics and metabolomics to uncover shared pathological pathways across Alzheimer's and Parkinson's disease cohorts.
NeurologyBiomarkersDeveloped multi-omics signatures predicting patient response to immunotherapy across multiple cancer types.
PharmacogenomicsImmunotherapyOur systematic approach ensures robust and reproducible multi-omics integration results.
Aggregate multi-omics data from genomics, transcriptomics, proteomics, and metabolomics platforms.
Quality control, normalization, batch correction, and missing value imputation.
Model training with VAEs, GNNs, attention networks, and multi-task learning.
Biomarker extraction, pathway analysis, and network visualization.
Our methods are based on peer-reviewed research from leading bioinformatics and computational biology journals.
Chen, R., Wang, L., Zhang, Y., et al. (2023). CustOmics: A versatile deep-learning based strategy for multi-omics integration. PLoS Computational Biology, 19(2), e1010921.
Liu, H., Wang, J., Chen, Y., et al. (2023). Multi-omics integration method based on attention deep learning network. Computer Methods and Programs in Biomedicine, 230, 107352.
Wang, B., Dai, Q., Li, F., et al. (2021). MOGONET integrates multi-omics data using graph convolutional networks. Nature Communications, 12(1), 2855.
Nilsson, A., Peters, J.M., Meimetis, N., et al. (2022). Artificial neural networks enable genome-scale simulations of intracellular signaling. Nature Communications, 13(1), 3069.
Shu, H., Zhou, J., Lian, Q., et al. (2021). Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 1(7), 491-501.
We integrate all major omics layers including genomics, transcriptomics (mRNA, miRNA, lncRNA), proteomics, phosphoproteomics, metabolomics, epigenomics, and single-cell omics data.
Our platform employs state-of-the-art methods including variational autoencoders (VAEs), graph neural networks (GNNs), attention-based Transformers, and custom multi-modal fusion architectures tailored to your specific research questions.
We have pre-built models for all major model organisms including human, mouse, rat, zebrafish, fruit fly, C. elegans, yeast, E. coli, and Arabidopsis. We can also create custom references for any organism.
Our AI algorithms are specifically trained to handle missing data across omics layers. We utilize imputation strategies, attention mechanisms, and flexible architectures that can accommodate partial data availability.
Yes. Beyond integrated embeddings and predictions, we offer functional annotation, pathway analysis, network visualization, and biomarker validation services to help you understand the biological significance of your results.
Upload your omics datasets and let our AI-powered platform deliver comprehensive integrated insights.
Single-omics approaches capture only a fraction of biological complexity. By integrating multiple molecular layers using deep learning, our platform provides a holistic view of cellular systems, revealing regulatory mechanisms invisible to any individual data type.
We combine proven bioinformatics tools with cutting-edge AI to deliver accurate, comprehensive multi-omics integration.
Our VAE-based integration learns low-dimensional latent representations that capture shared variation across omics layers while preserving modality-specific signals.
GNN-based integration leverages biological network structure to model interactions between genes, proteins, and metabolites across omics layers.
Transformer-based architectures dynamically weight contributions from each omics layer, enabling interpretable and adaptive multi-modal fusion.
From raw omics data to actionable biological insights, our integration pipeline covers every step.
Concatenate raw omics data before feature extraction. Ideal for datasets with strong cross-modal correlations.
Process each omics modality through dedicated neural networks before fusion for heterogeneous data types.
Independent analysis of each omics layer with results combined post-hoc for specialized processing.
Identify multi-omics signatures and cross-platform biomarkers for disease diagnosis and prognosis.
Traditional single-omics approaches provide fragmented views of biological systems. Our AI-powered integration reveals the full picture of cellular mechanisms.
Integrate multiple molecular layers simultaneously to capture gene-regulatory, protein-level, and metabolic interactions in context.
Multi-omics biomarkers consistently outperform single-omics markers for disease classification and patient stratification.
Move beyond correlations to identify causal regulatory mechanisms driving biological phenotypes.
Our AI architectures handle missing data across modalities, accommodating real-world incomplete datasets.
See how our multi-omics integration has helped researchers achieve breakthrough discoveries.
Integrated genomics, transcriptomics, and proteomics data to identify novel cancer subtypes with distinct therapeutic vulnerabilities.
OncologyPrecision MedicineCombined proteomics and metabolomics to uncover shared pathological pathways across Alzheimer's and Parkinson's disease cohorts.
NeurologyBiomarkersDeveloped multi-omics signatures predicting patient response to immunotherapy across multiple cancer types.
PharmacogenomicsImmunotherapyOur systematic approach ensures robust and reproducible multi-omics integration results.
Aggregate multi-omics data from genomics, transcriptomics, proteomics, and metabolomics platforms.
Quality control, normalization, batch correction, and missing value imputation.
Model training with VAEs, GNNs, attention networks, and multi-task learning.
Biomarker extraction, pathway analysis, and network visualization.
Our methods are based on peer-reviewed research from leading bioinformatics and computational biology journals.
Chen, R., Wang, L., Zhang, Y., et al. (2023). CustOmics: A versatile deep-learning based strategy for multi-omics integration. PLoS Computational Biology, 19(2), e1010921.
Liu, H., Wang, J., Chen, Y., et al. (2023). Multi-omics integration method based on attention deep learning network. Computer Methods and Programs in Biomedicine, 230, 107352.
Wang, B., Dai, Q., Li, F., et al. (2021). MOGONET integrates multi-omics data using graph convolutional networks. Nature Communications, 12(1), 2855.
Nilsson, A., Peters, J.M., Meimetis, N., et al. (2022). Artificial neural networks enable genome-scale simulations of intracellular signaling. Nature Communications, 13(1), 3069.
Shu, H., Zhou, J., Lian, Q., et al. (2021). Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 1(7), 491-501.
We integrate all major omics layers including genomics, transcriptomics (mRNA, miRNA, lncRNA), proteomics, phosphoproteomics, metabolomics, epigenomics, and single-cell omics data.
Our platform employs state-of-the-art methods including variational autoencoders (VAEs), graph neural networks (GNNs), attention-based Transformers, and custom multi-modal fusion architectures tailored to your specific research questions.
We have pre-built models for all major model organisms including human, mouse, rat, zebrafish, fruit fly, C. elegans, yeast, E. coli, and Arabidopsis. We can also create custom references for any organism.
Our AI algorithms are specifically trained to handle missing data across omics layers. We utilize imputation strategies, attention mechanisms, and flexible architectures that can accommodate partial data availability.
Yes. Beyond integrated embeddings and predictions, we offer functional annotation, pathway analysis, network visualization, and biomarker validation services to help you understand the biological significance of your results.
Upload your omics datasets and let our AI-powered platform deliver comprehensive integrated insights.
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