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Article: Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends.

Gogoshin, Grigoriy / Rodin, Andrei S

2023 Volume 15, Issue 24

Abstract: Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological ... ...

Abstract	Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.
Language	English
Publishing date	2023-12-15
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15245858
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Article: The Human Brainome: changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.

Branciamore, Sergio / Gogoshin, Grigoriy / Rodin, Andrei S / Myers, Amanda J

Research square

2023

Abstract: While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), ... ...

Abstract	While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the
Language	English
Publishing date	2023-12-14
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-3678057/v1
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Article ; Online: Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling.

Mukhaleva, Elizaveta / Ma, Ning / van der Velden, Wijnand J C / Gogoshin, Grigoriy / Branciamore, Sergio / Bhattacharya, Supriyo / Rodin, Andrei S / Vaidehi, Nagarajan

The Journal of biological chemistry

2024 , Page(s) 107362

Abstract: Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network- like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance ...

Abstract	Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network- like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces, it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein coupled receptor (GPCR) complexes with Gα subunits. Our results reveal six GPCR:Gα contacts that are common to the different Gα subtypes and show strong cooperativity in the formation of interface. Both the C-terminus helix5 and the core of the G protein are codependent entities and play an important role in GPCR coupling. We show that a promiscuous GPCR coupling to different Gα subtypes, makes all the GPCR:Gα contacts that are specific to each Gα subtype (Gαs, Gαi and Gαq). This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.
Language	English
Publishing date	2024-05-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1016/j.jbc.2024.107362
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Article ; Online: Synthetic data generation with probabilistic Bayesian Networks.

Gogoshin, Grigoriy / Branciamore, Sergio / Rodin, Andrei S

Mathematical biosciences and engineering : MBE

2021 Volume 18, Issue 6, Page(s) 8603–8621

Abstract: Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. ... ...

Abstract	Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.
MeSH term(s)	Bayes Theorem ; Computer Simulation ; Systems Biology
Language	English
Publishing date	2021-11-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2021426
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Article: Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling.

Mukhaleva, Elizaveta / Ma, Ning / van der Velden, Wijnand J C / Gogoshin, Grigoriy / Branciamore, Sergio / Bhattacharya, Supriyo / Rodin, Andrei S / Vaidehi, Nagarajan

bioRxiv : the preprint server for biology

2023

Abstract: Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or ... ...

Abstract	Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes (Gs, Gi and Gq). These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.
Language	English
Publishing date	2023-10-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.09.561618
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Article ; Online: Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design.

Hilliard, Seth / Mosoyan, Karen / Branciamore, Sergio / Gogoshin, Grigoriy / Zhang, Alvin / Simons, Diana L / Rockne, Russell C / Lee, Peter P / Rodin, Andrei S

iScience

2023 Volume 26, Issue 2, Page(s) 106041

Abstract: Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological ... ...

Abstract	Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.
Language	English
Publishing date	2023-01-25
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2023.106041
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Article: Regulation of Enhancers by SUMOylation Through TFAP2C Binding and Recruitment of HDAC Complex to the Chromatin.

Abeywardana, Tharindumala / Wu, Xiwei / Huang, Shih-Ting / Aldana Masangkay, Grace / Rodin, Andrei S / Branciamore, Sergio / Gogoshin, Grigoriy / Li, Arthur / Du, Li / Tharuka, Neranjan / Tomaino, Ross / Chen, Yuan

Research square

2024

Abstract: Enhancers are fundamental to gene regulation. Post-translational modifications by the small ubiquitin-like modifiers (SUMO) modify chromatin regulation enzymes, including histone acetylases and deacetylases. However, it remains unclear whether ... ...

Abstract	Enhancers are fundamental to gene regulation. Post-translational modifications by the small ubiquitin-like modifiers (SUMO) modify chromatin regulation enzymes, including histone acetylases and deacetylases. However, it remains unclear whether SUMOylation regulates enhancer marks, acetylation at the 27th lysine residue of the histone H3 protein (H3K27Ac). To investigate whether SUMOylation regulates H3K27Ac, we performed genome-wide ChIP-seq analyses and discovered that knockdown (KD) of the SUMO activating enzyme catalytic subunit UBA2 reduced H3K27Ac at most enhancers. Bioinformatic analysis revealed that TFAP2C-binding sites are enriched in enhancers whose H3K27Ac was reduced by UBA2 KD. ChIP-seq analysis in combination with molecular biological methods showed that TFAP2C binding to enhancers increased upon UBA2 KD or inhibition of SUMOylation by a small molecule SUMOylation inhibitor. However, this is not due to the SUMOylation of TFAP2C itself. Proteomics analysis of TFAP2C interactome on the chromatin identified histone deacetylation (HDAC) and RNA splicing machineries that contain many SUMOylation targets. TFAP2C KD reduced HDAC1 binding to chromatin and increased H3K27Ac marks at enhancer regions, suggesting that TFAP2C is important in recruiting HDAC machinery. Taken together, our findings provide insights into the regulation of enhancer marks by SUMOylation and TFAP2C and suggest that SUMOylation of proteins in the HDAC machinery regulates their recruitments to enhancers.
Language	English
Publishing date	2024-04-02
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-4201913/v1
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Article ; Online: Dependency Between Protein-Protein Interactions and Protein Variability and Evolutionary Rates in Vertebrates: Observed Relationships and Stochastic Modeling.

Wang, Xichun / Branciamore, Sergio / Gogoshin, Grigoriy / Rodin, Andrei S

Journal of molecular evolution

2019 Volume 87, Issue 4-6, Page(s) 184–198

Abstract: Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship ... ...

Abstract	Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship between protein-protein interactions and protein variability and evolutionary rates. This relationship has remained far from unambiguously resolved for quite a long time, reflecting shifting analysis approaches in the literature, and growing data availability. In this study, we utilized several public genomic databases to investigate this relationship in human, mouse, pig, chicken, and zebrafish. We observed strong non-linear relationship patterns (tending towards convex decreasing function shapes) between protein variability and the density of corresponding protein-protein interactions across all five species. To investigate further, we carried out stochastic simulations, modeling the interplay between protein connectivity and variability. Our results indicate that a simple negative linear correlation model, often suggested (or tacitly assumed) in the literature, as either a null or an alternative hypothesis, is not a good fit with the observed data. After considering different (but still relatively simple, and not overfitting) simulation models, we found that a convex decreasing protein variability-connectivity function (specifically, exponential decay) led to a much better fit with the real data. We conclude that simple correlation models might be inadequate for describing protein variability-connectivity interplay in vertebrates; they often tend towards false negatives (showing no more than marginal linear or rank correlation where there are in fact strong non-random patterns).
MeSH term(s)	Animals ; Computational Biology/methods ; Computer Simulation ; Databases, Protein ; Evolution, Molecular ; Humans ; Models, Statistical ; Protein Interaction Domains and Motifs/physiology ; Stochastic Processes ; Vertebrates/genetics
Language	English
Publishing date	2019-07-13
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	120148-7
ISSN	1432-1432 ; 0022-2844
ISSN (online)	1432-1432
ISSN	0022-2844
DOI	10.1007/s00239-019-09899-z
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Article: New Analysis Framework Incorporating Mixed Mutual Information and Scalable Bayesian Networks for Multimodal High Dimensional Genomic and Epigenomic Cancer Data.

Wang, Xichun / Branciamore, Sergio / Gogoshin, Grigoriy / Ding, Shuyu / Rodin, Andrei S

Frontiers in genetics

2020 Volume 11, Page(s) 648

Abstract: We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual ... ...

Abstract	We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.) and (ii) a flexible boundary between the variable selection and network modeling stages - the boundary that can be adjusted in accordance with the investigators' BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.
Language	English
Publishing date	2020-06-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.00648
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Article ; Online: New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.

Gogoshin, Grigoriy / Boerwinkle, Eric / Rodin, Andrei S

Journal of computational biology : a journal of computational molecular cell biology

2017 Volume 24, Issue 4, Page(s) 340–356

Abstract: Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to ... ...

Abstract	Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology-type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types-single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.
MeSH term(s)	Algorithms ; Bayes Theorem ; Computational Biology/methods ; Gene Regulatory Networks ; Genome-Wide Association Study ; Humans ; Metabolomics ; Middle Aged ; Models, Genetic ; Prospective Studies ; Software
Language	English
Publishing date	2017-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2030900-4
ISSN	1557-8666 ; 1066-5277
ISSN (online)	1557-8666
ISSN	1066-5277
DOI	10.1089/cmb.2016.0100
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