TY  - CONF
AU  - Malod-Dognin, Noël
AU  - Xenos, Alexandros
AU  - Doria Belenguer, Sergio
AU  - Pržulj, Nataša
PY  - 2023
UR  - https://belbi.bg.ac.rs/
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/1947
AB  - We are increasingly accumulating large-scale biological omics data that describe different
aspects of cellular functioning. These datasets are typically modelled and analyzed as
networks. To ease the downstream analyses, recent approaches embed the nodes of a
network into a low-dimensional space by using a skip-gram neural network (e.g. DeepWalk,
LINE and node2vec). These methods are implicitly factorizing a positive pointwise mutual
information (PPMI) matrix, which could be explicitly factorized with Non-negative Matrix
Tri-Factorization (NMTF). Importantly, in Natural Language Processing (NLP), word
embeddings obtained by using similar approaches showed linear algebraic structures,
which allows for answering analogy questions by using simple linear vector operations.
Thus, we investigate if we can obtain and exploit similar linear embedding spaces for the
biological omics networks.
We initiate the use of the PPMI matrices to capture the neighborhood relationship or the
structural (topological) similarities of nodes in the network. By embedding the human
Protein-Protein Interaction (PPI) network by factorizing its PPMI matrix representations
with NMTF, we demonstrate that the embedding vectors of genes having different Gene
Ontology (GO) annotations are linearly separated in the PPI embedding space.
Then, in analogy to the embedding vector of a sentence being obtained as the sum
(average) of the embedding vectors of its constituent words in NLP, we show that the
embedding vectors of biological functions and of protein complexes can be obtained by
averaging he embedding vectors of the genes that participate in then, and that these
embeddings can be used to predict protein complex memberships and cancer genes.
Finally, we investigate the embeddings of cancer and control tissue specific PPI networks
and show that simple subtractions allow for identifying cancer altered biological functions
and cancer genes.
PB  - Belgrade : Institute of molecular genetics and genetic engineering
C3  - 4th Belgrade Bioinformatics Conference
T1  - Exploiting the linear organisation of omics network embedding spaces
EP  - 12
SP  - 12
VL  - 4
UR  - https://hdl.handle.net/21.15107/rcub_imagine_1947
ER  - 

@conference{
author = "Malod-Dognin, Noël and Xenos, Alexandros and Doria Belenguer, Sergio and Pržulj, Nataša",
year = "2023",
abstract = "We are increasingly accumulating large-scale biological omics data that describe different
aspects of cellular functioning. These datasets are typically modelled and analyzed as
networks. To ease the downstream analyses, recent approaches embed the nodes of a
network into a low-dimensional space by using a skip-gram neural network (e.g. DeepWalk,
LINE and node2vec). These methods are implicitly factorizing a positive pointwise mutual
information (PPMI) matrix, which could be explicitly factorized with Non-negative Matrix
Tri-Factorization (NMTF). Importantly, in Natural Language Processing (NLP), word
embeddings obtained by using similar approaches showed linear algebraic structures,
which allows for answering analogy questions by using simple linear vector operations.
Thus, we investigate if we can obtain and exploit similar linear embedding spaces for the
biological omics networks.
We initiate the use of the PPMI matrices to capture the neighborhood relationship or the
structural (topological) similarities of nodes in the network. By embedding the human
Protein-Protein Interaction (PPI) network by factorizing its PPMI matrix representations
with NMTF, we demonstrate that the embedding vectors of genes having different Gene
Ontology (GO) annotations are linearly separated in the PPI embedding space.
Then, in analogy to the embedding vector of a sentence being obtained as the sum
(average) of the embedding vectors of its constituent words in NLP, we show that the
embedding vectors of biological functions and of protein complexes can be obtained by
averaging he embedding vectors of the genes that participate in then, and that these
embeddings can be used to predict protein complex memberships and cancer genes.
Finally, we investigate the embeddings of cancer and control tissue specific PPI networks
and show that simple subtractions allow for identifying cancer altered biological functions
and cancer genes.",
publisher = "Belgrade : Institute of molecular genetics and genetic engineering",
journal = "4th Belgrade Bioinformatics Conference",
title = "Exploiting the linear organisation of omics network embedding spaces",
pages = "12-12",
volume = "4",
url = "https://hdl.handle.net/21.15107/rcub_imagine_1947"
}

Malod-Dognin, N., Xenos, A., Doria Belenguer, S.,& Pržulj, N.. (2023). Exploiting the linear organisation of omics network embedding spaces. in 4th Belgrade Bioinformatics Conference
Belgrade : Institute of molecular genetics and genetic engineering., 4, 12-12.
https://hdl.handle.net/21.15107/rcub_imagine_1947

Malod-Dognin N, Xenos A, Doria Belenguer S, Pržulj N. Exploiting the linear organisation of omics network embedding spaces. in 4th Belgrade Bioinformatics Conference. 2023;4:12-12.
https://hdl.handle.net/21.15107/rcub_imagine_1947 .

Malod-Dognin, Noël, Xenos, Alexandros, Doria Belenguer, Sergio, Pržulj, Nataša, "Exploiting the linear organisation of omics network embedding spaces" in 4th Belgrade Bioinformatics Conference, 4 (2023):12-12,
https://hdl.handle.net/21.15107/rcub_imagine_1947 .

TY  - JOUR
AU  - Malod-Dognin, Noël
AU  - Ceddia, Gaia
AU  - Gvozdenov, Maja
AU  - Tomić, Branko
AU  - Manevski Dunjić, Sofija 
AU  - Đorđević, Valentina
AU  - Pržulj, Nataša
PY  - 2023
UR  - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284084
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/1920
AB  - Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects’ phenotypes and the genes’ molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.
T2  - Plos one
T1  - A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia
IS  - 4
SP  - e0284084
VL  - 18
DO  - 10.1371/journal.pone.0284084
ER  - 

@article{
author = "Malod-Dognin, Noël and Ceddia, Gaia and Gvozdenov, Maja and Tomić, Branko and Manevski Dunjić, Sofija  and Đorđević, Valentina and Pržulj, Nataša",
year = "2023",
abstract = "Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects’ phenotypes and the genes’ molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.",
journal = "Plos one",
title = "A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia",
number = "4",
pages = "e0284084",
volume = "18",
doi = "10.1371/journal.pone.0284084"
}

Malod-Dognin, N., Ceddia, G., Gvozdenov, M., Tomić, B., Manevski Dunjić, S., Đorđević, V.,& Pržulj, N.. (2023). A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia. in Plos one, 18(4), e0284084.
https://doi.org/10.1371/journal.pone.0284084

Malod-Dognin N, Ceddia G, Gvozdenov M, Tomić B, Manevski Dunjić S, Đorđević V, Pržulj N. A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia. in Plos one. 2023;18(4):e0284084.
doi:10.1371/journal.pone.0284084 .

Malod-Dognin, Noël, Ceddia, Gaia, Gvozdenov, Maja, Tomić, Branko, Manevski Dunjić, Sofija , Đorđević, Valentina, Pržulj, Nataša, "A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia" in Plos one, 18, no. 4 (2023):e0284084,
https://doi.org/10.1371/journal.pone.0284084 . .