Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Advancing translational research in neuroscience through multi-task learning

Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.

Vertical integration methods for gene expression data analysis

Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

RMTL: an R library for multi-task learning

MOTIVATION

Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research.

RESULTS

We developed an efficient, easy-to-use R library for MTL (www.r-project.org) comprising 10 algorithms applicable for regression, classification, joint predictor selection, task clustering, low-rank learning and incorporation of biological networks. We demonstrate the utility of the algorithms using simulated data.

AVAILABILITY AND IMPLEMENTATION

The RMTL package is an open source R package and is freely available at https://github.com/transbioZI/RMTL. RMTL will also be available on cran.r-project.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ontology-based metabolomics data integration with quality control

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.

Exclusive Sparsity Norm Minimization With Random Groups via Cone Projection

Many practical applications such as gene expression analysis, multitask learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the feature selection purpose and particularly favor the nonzeros evenly distributed in different groups. The exclusive sparsity norm has been widely used to serve to this purpose. However, it still lacks systematical studies for exclusive sparsity norm optimization. This paper offers two main contributions from the optimization perspective: 1) we provide several efficient algorithms to solve exclusive sparsity norm minimization with either smooth loss or hinge loss (nonsmooth loss). All algorithms achieve the optimal convergence rate . ( is the iteration number.) To the best of our knowledge, this is the first time to guarantee such convergence rate for the general exclusive sparsity norm minimization and 2) when the group information is unavailable to define the exclusive sparsity norm, we propose to use the random grouping scheme to construct groups and prove that if the number of groups is appropriately chosen, the nonzeros (true features) would be grouped in the ideal way with high probability. Empirical studies validate the efficiency of the proposed algorithms, and the effectiveness of random grouping scheme on the proposed exclusive support vector machine formulation.

Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework

Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this paper, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a "CCA-type" formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem. We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy data set and a more advanced simulated imaging genomics data set in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms data and functional magnetic resonance imaging data from a population of adolescents ( subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance analysis of the resulting features that allow us to carefully extract brain regions and genes linked to learning and cognitive ability.

Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry

The requirement of innovative big data analytics has become a critical success factor for research in biological psychiatry. Integrative analyses across distributed data resources are considered essential for untangling the biological complexity of mental illnesses. However, little is known about algorithm properties for such integrative machine learning. Here, we performed a comparative analysis of eight machine learning algorithms for identification of reproducible biological fingerprints across data sources, using five transcriptome-wide expression datasets of schizophrenia patients and controls as a use case. We found that multi-task learning (MTL) with network structure (MTL_NET) showed superior accuracy compared to other MTL formulations as well as single task learning, and tied performance with support vector machines (SVM). Compared to SVM, MTL_NET showed significant benefits regarding the variability of accuracy estimates, as well as its robustness to cross-dataset and sampling variability. These results support the utility of this algorithm as a flexible tool for integrative machine learning in psychiatry.

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease

It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.

A novel scoring system for acute myeloid leukemia risk assessment based on the expression levels of six genes

Acute myeloid leukemia (AML) is the most common type of acute leukemia and is a heterogeneous clonal disorder. At present, the pathogenesis of AML and potential methods to effectively prevent AML have become areas of interest in research. In the present study, two messenger ribonucleic acid sequencing datasets of patients with AML were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) of the poor and good prognosis groups were screened using the Linear Models for Microarray Data package, and the prognosis-related genes were screened using univariate Cox regression analysis. A total of 206 significant DEGs were identified. Following univariate and multivariate Cox regression analysis, 14 genes significantly associated with prognosis were screened and six of these genes, including triggering receptor expressed on myeloid cells 2 (TREML2), cysteine-glutamate transporter (SLC7A11), NACHT, LRR, and PYD domains-containing protein 2 (NLRP2), DNA damage-inducible transcript 4 protein (DDIT4), lymphocyte‑specific protein 1 (LSP1) and C-type lectin domain family 11 member A (CLEC11A), were used to construct model equations for risk assessment. The prognostic scoring system was used to evaluate risk for each patient, and the results showed that patients in the low-risk group had a longer survival time, compared with those in the high-risk group (P=9.59e-06 for the training dataset and P=0.00543 for the validation dataset). A total of eight main Kyoto Encyclopedia of Genes and Genomes pathways were identified, the top three of which were hematopoietic cell lineage, focal adhesion, and regulation of actin cytoskeleton. Taken together, the results showed that the scoring system established in the present study was credible and that the six genes were identified, which were significantly associated with the risk assessment of AML, offer potential as prognostic biomarkers. These findings may provide clues for further clarifying the pathogenesis of AML.

Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data

In order to comprehensively utilize complementary information from multiple types of data for better disease diagnosis, in this study, we applied a network fusion based approach to integrating three types of data including genetic, epigenetic and neuroimaging data from a study of schizophrenia patients (SCZ). A network is a map of interactions, which contributes to investigating the connectivity of components or links between sub-units. We exploited the potential of using networks as features for discriminating SCZ from healthy controls. We first constructed a single network from each type of data. Then we built four fused networks by the network fusion method: three fused networks for each combination of two types of data and one fused network for all three data types. Based on the local consistency of network, we can predict the group of the unlabeled SCZ subjects. The group prediction method was applied to test the power of network-based features and the performance was evaluated by a 10-fold cross validation. The results show that the prediction accuracy is the highest when applying our prediction method to the fused network derived from three data types among 7 tested networks. As a conclusion, integrative approaches that can comprehensively utilize multiple types of data are more useful for diagnosis and prediction.

Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Precise prediction for genetic architecture of complex traits is impeded by the limited understanding on genetic effects of complex traits, especially on gene-by-gene (GxG) and gene-by-environment (GxE) interaction. In the past decades, an explosion of high throughput technologies enables omics studies at multiple levels (such as genomics, transcriptomics, proteomics, and metabolomics). The analyses of large omics data, especially two-loci interaction analysis, are very time intensive. Integrating the diverse omics data and environmental effects in the analyses also remain challenges. We proposed mixed linear model approaches using GPU (Graphic Processing Unit) computation to simultaneously dissect various genetic effects. Analyses can be performed for estimating genetic main effects, GxG epistasis effects, and GxE environment interaction effects on large-scale omics data for complex traits, and for estimating heritability of specific genetic effects. Both mouse data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be unbiasedly estimated with high statistical power by using the proposed approaches.