The TissueNet v.3 Database: Protein-protein Interactions in Adult and Embryonic Human Tissue contexts

Ziv, Maya; Gruber, Gil; Sharon, Moran; Vinogradov, Ekaterina; Yeger‐Lotem, Esti

doi:10.1016/j.jmb.2022.167532

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…In order to enrich visualization information, we leveraged data from the STRING ( 43 ) database and WashU EpiGenome Browser ( 44 ). To assist in downstream analyses, we also utilized network data from STRING ( 43 ), RNAInter ( 45 ), the Biological General Repository for Interaction Datasets ( 46 ), HumanNet ( 47 ), TissueNet ( 48 ) and HumanBase ( 49 ), as well as several datasets mentioned in the Weighted Enrichment Analysis Tool (WEAT) ( 50 ) method.…”

Section: Methodsmentioning

confidence: 99%

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MDDOmics: multi-omics resource of major depressive disorder

Zhao,

Xiang,

Shi

et al. 2024

Database

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Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/

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Section: Methodsmentioning

confidence: 99%

“…Several PPI networks are integrated as options, which include comprehensive and tissue-specific PPI networks. We selected the comprehensive PPI networks from STRING ( 43 ), BIOGRID ( 46 ) and HumanNet ( 47 ) databases, along with the brain tissue-specific networks sourced from the TissueNet ( 48 ) and HumanBase ( 49 ) databases.…”

Section: Methodsmentioning

confidence: 99%

MDDOmics: multi-omics resource of major depressive disorder

Zhao,

Xiang,

Shi

et al. 2024

Database

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“…These approaches can resolve protein interaction networks across tissues [29][30][31][32][33] and cell types by integrating molecular cell atlases [34,35] and extending our understanding of the relationship between protein and function [36][37][38]. Protein representation learning methods can predict multicellular functions across human tissues [31], design target-binding proteins [39][40][41][42] and novel protein interactions [43], and predict interactions between transcription factors and genes [37,38].…”

Section: Mainmentioning

confidence: 99%

Contextual AI models for single-cell protein biology

Huang

Sumathipala

et al. 2023

Preprint

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Understanding protein function and discovering molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce PINNACLE, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, PINNACLE provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. PINNACLE's contextualized representations of proteins reflect cellular and tissue organization and PINNACLE's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained PINNACLE protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations (PD-1/PD-L1 and B7-1/CTLA-4) at cellular resolution and to study the genomic effects of drugs across cellular contexts. PINNACLE outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that are more predictive of therapeutic targets than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). PINNACLE is a network-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

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“…We plan to include more gene interaction networks, particularly the ones that are context-specific, such as HumanBase (C. S. Greene et al ., 2015), CompPPI (Veres et al ., 2015), and TissueNet (Ziv et al ., 2022).…”

Section: Future Workmentioning

confidence: 99%

Open Biomedical Network Benchmark A Python Toolkit for Benchmarking Datasets with Biomedical Networks

Liu

Krishnan

2023

Preprint

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Recent breakthroughs in graph representation learning (GRL) methods have vastly advanced many real-world applications where graph structures naturally arise, such as drug discoveries, molecular property predictions, and social recommendation systems. The rapid developments of GRL with applications in specific scientific domains are largely enabled by graph learning libraries such as PyTorch Geometric, which provide infrastructures that allow domain scientists to share domain-specific benchmarking datasets, and GRL researchers to adapt and design specialized GRL methods for these particular tasks. Meanwhile, over the past two decades, network biology has demonstrated superior value in harvesting biological insights from network data, such as identifying genes' associated functions, diseases, and traits. Nevertheless, the GRL community faces a significant barrier in working with biological networks because of the tedious and specialized (pre-)processing steps required to set up machine learning (ML)-ready benchmarking datasets. Here, we present nleval, a Python package containing reusable modules that enable researchers to effortlessly set up PyG-compatible ML-ready datasets using data downloaded from public databases. We expect nleval to help network biologists set up custom benchmarking datasets for answering specific biological questions of their interests and help GRL researchers adapt these datasets for designing new specialized GRL architectures.

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The TissueNet v.3 Database: Protein-protein Interactions in Adult and Embryonic Human Tissue contexts

Cited by 12 publications

References 31 publications

MDDOmics: multi-omics resource of major depressive disorder

MDDOmics: multi-omics resource of major depressive disorder

Contextual AI models for single-cell protein biology

Open Biomedical Network Benchmark A Python Toolkit for Benchmarking Datasets with Biomedical Networks

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