MOTIVATION
Accurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting prognosis of complex diseases are required.


RESULTS
Here, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various classification tasks demonstrated the superior performance of this approach. The utility of MOMA was also verified using a comparison experiment with an attention mechanism that was turned on or off and biological analysis.


AVAILABILITY
The source codes are available at https://github.com/dmcb-gist/MOMA.


SUPPLEMENTARY INFORMATION
Supplementary materials are available at Bioinformatics online.

MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Young children have long been known to act selfishly and gradually appear to become more generous across middle childhood. While this apparent change has been well documented, the underlying mechanisms supporting this remain unclear. The current study examined the role of early theory of mind and executive functioning in facilitating sharing in a large sample (N = 98) of preschoolers. Results reveal a curious relation between early false-belief understanding and sharing behavior. Contrary to many commonsense notions and predominant theories, competence in this ability is actually related to less sharing. Thus, the relation between developing theory of mind and sharing may not be as straightforward as it seems in preschool age children. It is precisely the children who can engage in theory of mind that decide to share less with others.

The Curious Relation Between Theory of Mind and Sharing in Preschool Age Children

https://doi.org/10.1016/j.isci.2022.105163

Deep learning explains the biology of branched glycans from single-cell sequencing data

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. Classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of graph convolution network (GCN), we presented a multi-omics integrative method, attention-based GCN, for breast cancer molecular subtype classification using mRNA expression, copy number variation, and DNA methylation multi-omics data. Several attention mechanisms were performed and all exhibited effectiveness in integrating heterogeneous data. Column-wise attention-based GCN outperformed the other baseline methods, achieving AUC of 0.9816, ACC of 0.8743 and MCC of 0.8151 in 5-fold cross validation. Besides, Layer-wise Relevance Propagation (LRP) algorithm was used for interpretation of model decision and could identify patient-specific important biomarkers which were reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of GCN and attention mechanisms in multi-omics integrative analysis and implement of LRP algorithm could provide biologically reasonable insights into model decision. Author Summary Identification of molecular subtype is essential to understanding the pathogenesis of cancer and advancing the development of cancer precision treatment. We have developed a graph convolution network architecture to predict the molecular subtype of breast cancer patients based on multi-omics data. The major difference between our work and other methods is that we have offered a new view for multi-omics data integration with the assistance of graph convolution network. Besides, we adopted an interpretability method to explain the model decision and prioritize the genes for biomarker discovery.

/pdf/attention-based-gcn-integrates-multi-omics-data-for-breast-1hm2n782.pdf

Attention-based GCN Integrates Multi-omics Data for Breast Cancer Subtype Classification and Patient-specific Gene Marker Identification

The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field. We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians. We propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.

/pdf/biological-interpretation-of-deep-neural-network-for-aulczppwsc.pdf

Biological interpretation of deep neural network for phenotype prediction based on gene expression

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Biological interpretation of deep neural network for phenotype prediction based on gene expression