MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

BACKGROUND: Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS: We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS: Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.

A new Endoclita (C. . R. Felder, 1874) from the High Mountains of Taiwan with notes about its evolutionary origins (Lepidoptera: Hepialidae).

A new species Endoclita atayala Buchsbaum Hsu sp. n. is based on a single female specimen collected at light in the high mountains of northern Taiwan. Comparisons with all other Endoclita (C. R. Felder, 1874) species confirms that E. atayala sp. n. exhibits distinctive and unique wing pattern characteristics and probably has a sister group relationship with E. crenilimbata (Le Cerf, 1919) of southeastern China and illustrated here for the first time, and E. nodus (Chu Wang, 1985) of northeastern China. The species diversity of Taiwanese Hepialidae is described and the biogeographic implications of the E. atayala sp. n. relationship for the origin of endemic species in Taiwan are reviewed.