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Computational approaches for network-based integrative multi-omics analysis

FRONTIERS IN MOLECULAR BIOSCIENCES (2022)

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Cecilia Noecker et al.

Summary: This study introduces MIMOSA2, a model-based integrative analysis tool for microbiome-metabolome datasets. MIMOSA2 utilizes genomic and metabolic reference databases to construct a community metabolic model based on microbiome data, predicting differences in metabolite levels and identifying taxonomic contributors to metabolite variation. It supports various input data types and customization for user-defined pathways.

BIOINFORMATICS (2022)

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Review Biochemical Research Methods

A roadmap for multi-omics data integration using deep learning

Mingon Kang et al.

Summary: High-throughput next-generation sequencing allows for the generation of large amounts of multi-omics data, which has revolutionized biomedical research by providing a more comprehensive understanding of biological systems and disease development mechanisms. Deep learning algorithms have emerged as a promising method in multi-omics data analysis due to their predictive performance and ability to capture nonlinear and hierarchical features. However, integrating and translating multi-omics data into functional insights remains a challenge, but there is a clear trend towards incorporating multi-omics analysis in biomedical research to explain complex relationships between molecular layers.

BRIEFINGS IN BIOINFORMATICS (2022)

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Article Biochemical Research Methods

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BRIEFINGS IN BIOINFORMATICS (2022)

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Article Biochemistry & Molecular Biology

Interpretation of network-based integration from multi-omics longitudinal data

Antoine Bodein et al.

Summary: Multi-omics integration is crucial for understanding complex biological processes, but the challenge lies in interpretation. A generic approach is proposed to build hybrid multi-omics networks for predicting regulatory mechanisms and functional modules.

NUCLEIC ACIDS RESEARCH (2022)

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Article Biochemical Research Methods

MDICC: novel method for multi-omics data integration and cancer subtype identification

Ying Yang et al.

Summary: Each type of cancer usually has multiple subtypes, and discovering and predicting these subtypes is crucial for disease diagnosis and treatment. Using single-omics data for prediction is challenging due to dysregulated genomes and complex molecular mechanisms. However, using multi-omics data is a promising approach, although it presents integration challenges. This study proposes a novel method, MDICC, for integrating multi-omics data to identify cancer subtypes, which outperforms current state-of-the-art clustering methods.

BRIEFINGS IN BIOINFORMATICS (2022)

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Article Biochemistry & Molecular Biology

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics

Guangyan Zhou et al.

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NUCLEIC ACIDS RESEARCH (2022)

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Review Nutrition & Dietetics

Tryptophan Metabolism and COVID-19-Induced Skeletal Muscle Damage: Is ACE2 a Key Regulator?

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FRONTIERS IN NUTRITION (2022)

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i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

Xingxin Pan et al.

Summary: Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. However, a robust framework for integrating multi-omics data types to efficiently and precisely predict survival prognosis is still lacking. Additionally, effective therapeutic targets for treating glioma patients with poor prognoses are urgently needed.

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A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

Harold Durufle et al.

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BRIEFINGS IN BIOINFORMATICS (2021)

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Review Biochemical Research Methods

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

Dohoon Lee et al.

Summary: The multi-omics molecular characterization of cancer provides a new perspective on cancer biology and treatment strategies. Tumor biopsy samples contain diverse cell types, including cancerous cells, tumor microenvironmental cells, and adjacent normal cells, which present a challenge for robust and reproducible bioinformatic analysis. Computational approaches that focus on individual omics layers may not fully capture the heterogeneity of cells across different layers, prompting the need for comprehensive methodologies that consider multiple omics profiles simultaneously. This review discusses various strategies to characterize tumor heterogeneity based on genome, epigenome, and transcriptome layers, offering valuable insights for analyzing multi-omics data generated by large-scale consortia.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemistry & Molecular Biology

Large-Scale Multi-omic Analysis of COVID-19 Severity

Katherine A. Overmyer et al.

Summary: The study conducted RNA-seq and high-resolution mass spectrometry on blood samples from COVID-19-positive and COVID-19-negative patients, identifying molecular features associated with disease severity and outcomes, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. The findings are presented through a web-based tool enabling interactive exploration and machine learning prediction of COVID-19 severity.

CELL SYSTEMS (2021)

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Article Biochemical Research Methods

MultiPaths: a Python framework for analyzing multi-layer biological networks using diffusion algorithms

Josep Marin-Llao et al.

Summary: High-throughput screening generates large amounts of biological data, which can be challenging to interpret. Knowledge-driven methods and computational approaches, such as diffusion algorithms, are essential for analyzing the large and complex biological networks. The MultiPaths framework, consisting of two independent Python packages, facilitates network analysis by providing command line interface, reproducible examples, and documentation.

BIOINFORMATICS (2021)

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C-reactive protein and clinical outcomes in patients with COVID-19

Nathaniel R. Smilowitz et al.

Summary: Elevated CRP levels at initial hospital presentation in COVID-19 patients are associated with an increased risk of venous thromboembolism, acute kidney injury, critical illness, and mortality. There is a dose-response relationship between CRP concentration and adverse outcomes, with patients with high levels of both CRP and D-dimer showing the greatest risk of adverse outcomes. Systemic inflammation, as measured by CRP, plays a significant role in predicting clinical outcomes in COVID-19.

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An integrated data-dependent and data-independent acquisition method for hazardous compounds screening in foods using a single UHPLC-Q-Orbitrap run

Feifei Sun et al.

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DOMINO: a network-based active module identification algorithm with reduced rate of false calls

Hagai Levi et al.

Summary: Algorithms for active module identification (AMI) analyze gene networks and activity scores to identify active modules representing key biological processes. However, these methods often report modules not specific to the analyzed biological context. To address this bias, a permutation-based method was developed to evaluate GO terms reported by AMI methods. The novel AMI algorithm, DOMINO, outperformed other algorithms in testing on GE and GWAS data.

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Multi-dimensional data integration algorithm based on random walk with restart

Yuqi Wen et al.

Summary: This study introduces a random walk algorithm based on multiplex networks for multi-omics data integration, which can better identify subtypes in cancer datasets and performs better than single data type analysis and previous integrative methods in experimental results.

BMC BIOINFORMATICS (2021)

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Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets

Francis E. Agamah et al.

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Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19

Marios Tomazou et al.

Summary: The SARS-CoV-2 pandemic is considered the most severe global health emergency since the 1918 Influenza outbreak and is expected to become an endemic infectious respiratory disease with seasonal flare-ups. While efforts are being made to develop a vaccine, it is equally important to find effective prevention and treatment options for COVID-19. Developing a multiplex drug repurposing approach based on multi-omics data could offer a rapid mapping and prioritization of drugs against any pathogen-related disease.

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The role of autophagy in controlling SARS-CoV-2 infection: An overview on virophagy-mediated molecular drug targets

Saman Sargazi et al.

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CELL BIOLOGY INTERNATIONAL (2021)

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Between inflammation and thrombosis: endothelial cells in COVID-19

Anna Birnhuber et al.

EUROPEAN RESPIRATORY JOURNAL (2021)

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Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

Alexey Stukalov et al.

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NATURE (2021)

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Article Multidisciplinary Sciences

A graph neural network framework for causal inference in brain networks

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SCIENTIFIC REPORTS (2021)

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Review Biotechnology & Applied Microbiology

Using machine learning approaches for multi-omics data analysis: A review

Parminder S. Reel et al.

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RNCE: network integration with reciprocal neighbors contextual encoding for multi-modal drug community study on cancer targets

Junyi Chen et al.

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Longitudinal multi-omics transition associated with fatality in critically ill COVID-19 patients

Chaoyang Sun et al.

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An integrative multi-omics network-based approach identifies key regulators for breast cancer

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