Deep collective matrix factorization for augmented multi-view learning

From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare

BACKGROUND

The integration of data from diverse sources is not only crucial for addressing data scarcity in health informatics but also enables the use of complementary information from multiple datasets. However, the isolated nature of data collected from disparate sources (referred to as 'Silos') presents significant challenges in multi-source data integration due to inherent heterogeneity and differences in data structures, formats, and standards. Domain adaptation emerges as a key framework to transition from 'Silos' to 'Synthesis' by measuring and mitigating such discrepancies, enabling uniform representation and harmonization of multi-source data.

METHODS

This study explores different approaches to healthcare data integration, highlighting the challenges associated with each type and discussing both general-purpose and healthcare-specific adaptation methods. We examine key research challenges and evaluate leading domain adaptation approaches, demonstrating their effectiveness and limitations in advancing healthcare data integration.

RESULTS

The findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.

CONCLUSION

Current research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.

Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data

MOTIVATION

In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative representations that can be used in downstream tasks, such as clustering or relation prediction with standard machine-learning models. Previous CMF-based methods have numerous modeling limitations. They do not adequately capture complex non-linear interactions and do not explicitly model varying sparsity and noise levels in the inputs, and some cannot model inputs with multiple datatypes. These inadequacies limit their use on many biomedical datasets.

RESULTS

To address these limitations, we develop Neural Collective Matrix Factorization (NCMF), the first fully neural approach to CMF. We evaluate NCMF on relation prediction tasks of gene-disease association prediction and adverse drug event prediction, using multiple datasets. In each case, data are obtained from heterogeneous publicly available databases and used to learn representations to build predictive models. NCMF is found to outperform previous CMF-based methods and several state-of-the-art graph embedding methods for representation learning in our experiments. Our experiments illustrate the versatility and efficacy of NCMF in representation learning for seamless integration of heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ajayago/NCMF_bioinformatics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

Simple Summary

The diagnostic and treatment strategies of cancer remain generally suboptimal resulting in over-diagnosis or under-treatment. Though many attempts on optimizing treatment decisions by early prediction of disease progression have been undertaken, these efforts yielded only modest success so far due to the heterogeneity of cancer with multifactorial etiology. Here, we propose a deep-learning based data integration model capable of predicting disease progression by integrating collective information available through multiple studies with different cohorts and heterogeneous data types. The results have shown that the proposed data integration pipeline is able to identify disease progression with higher accuracy and robustness compared to using a single cohort, by offering a more complete picture of the specific disease on patients with brain, blood, and pancreatic cancers.

Abstract

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

BACKGROUND

Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-intensive manual feature engineering, especially from unstructured data such as text, images, or graphs. Most previous techniques have used neural network-based autoencoders to learn patient representations, primarily from clinical notes in electronic medical records (EMRs). Knowledge graphs (KGs), with clinical entities as nodes and their relations as edges, can be extracted automatically from biomedical literature and provide complementary information to EMR data that have been found to provide valuable predictive signals.

OBJECTIVE

This study aims to evaluate the efficacy of collective matrix factorization (CMF), both the classical variant and a recent neural architecture called deep CMF (DCMF), in integrating heterogeneous data sources from EMR and KG to obtain patient representations for clinical decision support tasks.

METHODS

Using a recent formulation for obtaining graph representations through matrix factorization within the context of CMF, we infused auxiliary information during patient representation learning. We also extended the DCMF architecture to create a task-specific end-to-end model that learns to simultaneously find effective patient representations and predictions. We compared the efficacy of such a model to that of first learning unsupervised representations and then independently learning a predictive model. We evaluated patient representation learning using CMF-based methods and autoencoders for 2 clinical decision support tasks on a large EMR data set.

RESULTS

Our experiments show that DCMF provides a seamless way for integrating multiple sources of data to obtain patient representations, both in unsupervised and supervised settings. Its performance in single-source settings is comparable with that of previous autoencoder-based representation learning methods. When DCMF is used to obtain representations from a combination of EMR and KG, where most previous autoencoder-based methods cannot be used directly, its performance is superior to that of previous nonneural methods for CMF. Infusing information from KGs into patient representations using DCMF was found to improve downstream predictive performance.

CONCLUSIONS

Our experiments indicate that DCMF is a versatile model that can be used to obtain representations from single and multiple data sources and combine information from EMR data and KGs. Furthermore, DCMF can be used to learn representations in both supervised and unsupervised settings. Thus, DCMF offers an effective way of integrating heterogeneous data sources and infusing auxiliary knowledge into patient representations.

Predicting synthetic lethal interactions using heterogeneous data sources

MOTIVATION

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.

RESULTS

In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/lianyh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.