MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

OMICS Approaches to Assess Dinoflagellate Responses to Chemical Stressors

Dinoflagellates are important primary producers known to form Harmful Algae Blooms (HABs). In water, nutrient availability, pH, salinity and anthropogenic contamination constitute chemical stressors for them. The emergence of OMICs approaches propelled our understanding of dinoflagellates' responses to stressors. However, in dinoflagellates, these approaches are still biased, as transcriptomic approaches are largely conducted compared to proteomic and metabolomic approaches. Furthermore, integrated OMICs approaches are just emerging. Here, we report recent contributions of the different OMICs approaches to the investigation of dinoflagellates' responses to chemical stressors and discuss the current challenges we need to face to push studies further despite the lack of genomic resources available for dinoflagellates.

Functional impact of multi-omic interactions in breast cancer subtypes

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Supervised Methods for Biomarker Detection from Microarray Experiments

Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.

CorDiffViz: an R package for visualizing multi-omics differential correlation networks

BACKGROUND

Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.

RESULTS

We present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz . Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html .

CONCLUSIONS

Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.