Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Fragkiskos D. Malliaros"'
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-17 (2022)
Abstract Background Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to
Externí odkaz:
https://doaj.org/article/838ba0c2b097494ab47a6f5c1d2e3953
Autor:
Kavya Gupta, Fateh Kaakai, Beatrice Pesquet-Popescu, Jean-Christophe Pesquet, Fragkiskos D. Malliaros
Publikováno v:
Frontiers in Signal Processing, Vol 2 (2022)
The stability of neural networks with respect to adversarial perturbations has been extensively studied. One of the main strategies consist of quantifying the Lipschitz regularity of neural networks. In this paper, we introduce a multivariate Lipschi
Externí odkaz:
https://doaj.org/article/408536be1dae4df79f59d704afe5008b
Autor:
Maria-Evgenia G Rossi, Bowen Shi, Nikolaos Tziortziotis, Fragkiskos D Malliaros, Christos Giatsidis, Michalis Vazirgiannis
Publikováno v:
PLoS ONE, Vol 13, Iss 11, p e0206318 (2018)
Influence maximization has attracted a lot of attention due to its numerous applications, including diffusion of social movements, the spread of news, viral marketing and outbreak of diseases. The objective is to discover a group of users that are ab
Externí odkaz:
https://doaj.org/article/88fd3fcf771d4ed5a8c14bac098f7ad9
Autor:
Surabhi Jagtap, Abdulkadir Çelikkanat, Aurélie Pirayre, Frédérique Bidard, Laurent Duval, Fragkiskos D Malliaros
Publikováno v:
Bioinformatics
Bioinformatics, 2022, 38 (24), pp.5383-5389. ⟨10.1093/bioinformatics/btac691⟩
Bioinformatics, 2022, 38 (24), pp.5383-5389. ⟨10.1093/bioinformatics/btac691⟩
MotivationThe cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics techn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e301edd534a62b3542b32aaea07966b5
https://pubmed.ncbi.nlm.nih.gov/36321881
https://pubmed.ncbi.nlm.nih.gov/36321881
Autor:
Bin Liu, Dimitrios Papadopoulos, Fragkiskos D Malliaros, Grigorios Tsoumakas, Apostolos N Papadopoulos
Publikováno v:
Briefings in Bioinformatics
Briefings in Bioinformatics, 2022, 23 (5), ⟨10.1093/bib/bbac353⟩
Briefings in Bioinformatics, 2022, 23 (5), ⟨10.1093/bib/bbac353⟩
The discovery of drug–target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5394d5642952dc09eb899071b211838
Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings
Autor:
Surabhi Jagtap, Abdulkadir Celikkanat, Aurelic Piravre, Frederiuue Bidard, Laurent Duval, Fragkiskos D. Malliaros
Publikováno v:
EUSIPCO-29th European Signal Processing Conference
EUSIPCO-29th European Signal Processing Conference, Aug 2021, Dublin / Online, Ireland
EUSIPCO-29th European Signal Processing Conference, Aug 2021, Dublin / Online, Ireland
International audience; The advent of omics technologies has enabled the generation of huge, complex, heterogeneous, and high-dimensional omics data. Imposing numerous challenges in data integration, these data could lead to a better understanding of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a31c35265bf762f693895c5841d6c99f
https://hal.archives-ouvertes.fr/hal-03336884/file/Multiomics_GRN_EUSIPCO_2021.pdf
https://hal.archives-ouvertes.fr/hal-03336884/file/Multiomics_GRN_EUSIPCO_2021.pdf
Publikováno v:
Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2021
Pattern Recognition Letters, 2021
Pattern Recognition Letters, Elsevier, 2021
Pattern Recognition Letters, 2021
Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering. Such methods aim to map
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5061ad4cbbc99a3d10f664a582e14305
https://hal.archives-ouvertes.fr/hal-03420690v1/file/TNE_Pattern_Recognition_Letters.pdf
https://hal.archives-ouvertes.fr/hal-03420690v1/file/TNE_Pattern_Recognition_Letters.pdf
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030865191
ECML/PKDD (2)
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Sep 2021, Bilbao, Spain
ECML/PKDD (2)
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Sep 2021, Bilbao, Spain
Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging. In this pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dac56101f23403e443bcf40cc9afc4e
https://doi.org/10.1007/978-3-030-86520-7_19
https://doi.org/10.1007/978-3-030-86520-7_19
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030742959
ICWE
21st International Conference on Web Engineering (ICWE)
21st International Conference on Web Engineering (ICWE), May 2021, Biarritz, France
ICWE
21st International Conference on Web Engineering (ICWE)
21st International Conference on Web Engineering (ICWE), May 2021, Biarritz, France
International audience; The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aea019a529f11df6680779b60a50ca8b
https://doi.org/10.1007/978-3-030-74296-6_48
https://doi.org/10.1007/978-3-030-74296-6_48
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering, 2020, ⟨10.1109/TKDE.2020.3040028⟩
IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TKDE.2020.3040028⟩
IEEE Transactions on Knowledge and Data Engineering, 2020, ⟨10.1109/TKDE.2020.3040028⟩
IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TKDE.2020.3040028⟩
We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dbcd7ecc7c62d75c1bc05c32b8f129c
https://hal.science/hal-03088930
https://hal.science/hal-03088930