Submitted

2024

Moreno-Vega*, M. Zambrano*, J. Puig*, F. Dufour, X. Meng, J. Fontugne, C. Groeneveld, M. Shi, S. Lindskrog, C. Beraud, E. Chapeaublanc, Th. Lebret, A. Eijan, Y. Allory, Ph. Luel, L. Dyrskjot, M. Elati#, F. Radvanyi#, C. Lodillinsky# and I. Bernard-Pierrot#, Identification of a pro-tumoral role of p63 in altered-FGFR3 tumors through inference of bladder cancer gene regulatory network and functional validations, 2023 ( https://hal.science/hal-04687147)

N.E.I. Karabadji, A. A. Korba, A. Assi, H. Seridi, S. Aridhi, W. Dhifli. Accuracy and Diversity-Aware Multi-Objective Approach for Random
Forest Construction. Expert Systems With Applications (Elsevier) 225:120138, 2023. (DOI: https://doi.org/10.1016/j.eswa.2023.120138 ) 

Wang, X., Liu, J., Azoitei, A., Eiseler, T., Meessen, S., Jiang, W., Elati M, … & Günes, C. (2023). Loss of ORP3 induces aneuploidy and
promotes bladder cancer cell invasion through deregulated microtubule and actin dynamics. Cellular and Molecular Life Sciences, 80(10). (DOI: https://doi.org/10.1007/s00018-023-04959-6 )

Fruchart M, El Idrissi F, Lamer A, Belarbi K, Lemdani M, Zitouni D, Guinhouya BC. Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study. Digit Health. 2023;9:20552076231176114. (DOI: https://doi.org/10.1177/20552076231176114 )

Fruchart, M., El Idrissi, F., Lamer, A., Belarbi, K., Lemdani, M., Zitouni, D., & Guinhouya, B. (2023). Une approche patient-centrée pour

l’identification des symptômes précoces de l’endométriose – fouille de texte des réseaux sociaux en ligne. Journal of Obstetrics and Gynaecology (Vol. 45, Issue 5, p. 356). Elsevier BV. (DOI: https://doi.org/10.1016/j.jogc.2023.03.050 )

2022

K. M. Pékpé, D. Zitouni, G. Gasso, W. Dhifli, B.C. Guinhouya. From SIR to SEAIRD: a novel data-driven modeling approach based on the Grey System Theory to predict the dynamics of COVID-19. Applied Intelligence (Springer) 1-10, 2022 (DOI: https://doi.org/10.1007/s10489-021-02379-2 )

Miagoux, Q., Singh, V., Dereck, D. M., Chaudru, V., Elati, M., Teixeira, E., & Niarakis, A. (2021). De la création d’un réseau intégratif
spécifique de la polyarthrite rhumatoïde à l’étude d’un modèle personnalisé relié au traitement des patients. Revue du Rhumatisme, 88, A151. (DOI : https://doi.org/10.1016/j.rhum.2021.10.238 )

El Idrissi, F., Fruchart, M., Belarbi, K., Lamer, A., Dubois-Deruy, E., Lemdani, M., N’Guessan, A. L., Guinhouya, B. C., & Zitouni, D. (2022). Exploration of the core protein network under endometriosis symptomatology using a computational approach. In Frontiers in Endocrinology (Vol. 13). Frontiers Media SA.  (DOI : https://doi.org/10.3389/fendo.2022.869053 )

Da Silva, C., Zitouni, D., Guinhouya, K. H., Storme, L., Hubert, H., Garat, A., Lamer, A., Lemdani, M., Tchédré, K. B., Gbenyo, K. F., Gasso, G., & Guinhouya, B. C. (2022). 414 – Approche computationnelle – Médicaments et voies biologiques dans les troubles du spectre autistique infantiles. In Revue d’Épidémiologie et de Santé Publique (Vol. 70, p. S158). Elsevier BV. (DOI: https://doi.org/10.1016/j.respe.2022.06.083 )

Patel, H., Patel, R., Zitouni, D., Guinhouya, B., Fruchart, M., & Lamer, A. (2022). Automated Twitter Extraction and Visual Analytics with Dashboards: Development and First Experimentations. Studies in Health Technology and Informatics. IOS Press. (DOI: https://doi.org/10.3233/SHTI220562 )

M Messé, C Bernhard, M Mercier, Q Fuchs, S Foppolo, C Herold-Mende, I Namer, C Bund, M Elati, N Entz-Werlé, M Dontenwill, Heterogeneity and plasticity of integrin α5β1 expression in glioblastoma stem cells, Neuro-Oncology, Volume 23, Issue Supplement_2, September 2021, Page ii22. (DOI: https://doi.org/10.1093/neuonc/noab180.072 )

2021

A. Assi, W. Dhifli. Instance Matching in Knowledge Graphs through Random Walks and Semantics. Future Generation Computer Systems (Elsevier) 123:73-84, 2021. (DOI: https://doi.org10.1016/j.future.2021.04.015 )

Champion, M., Chiquet, J., Neuvial, P., Elati, M., Radvanyi, F., & Birmelé, E. (2021). Identification of deregulation mechanisms specific to cancer subtypes. Journal of Bioinformatics and Computational Biology, 19(01), 2140003. (DOI: https://doi.org/10.1142/S0219720021400035 )

Miagoux, Q., Singh, V., de Mézquita, D., Chaudru, V., Elati, M., Petit-Teixeira, E., & Niarakis, A. (2021). Inference of an integrative, executable network for rheumatoid arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map. Journal of Personalized Medicine, 11(8), 785. (DOI : https://doi.org/10.3390/jpm11080785 )

C.E. Berbague, N.E.I. Karabadji, H. Seridi, P. Symeonidis, Y. Manolopoulos, W. Dhifli. An Overlapping Clustering Approach for Precision, Diversity and Novelty-Aware Recommendations. Expert Systems With Applications (Elsevier) 177:114917, 2021. (DOI: https://doi.org/10.1016/j.eswa.2021.114917 )

Geles K, Palumbo D, Sellitto A et al. WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data.
F1000Research 2021, 10:1 (DOI : https://doi.org/10.12688/f1000research.27868.3 )

2020

W. Dhifli, N.E.I. Karabadji, M. Elati. Evolutionary Mining of Skyline Clusters of Attributed Graph Data. Information Sciences (Elsevier) 509:501-514, 2020. (DOI: https://doi.org/10.1016/j.ins.2018.09.053 )

Zerrouk, N., Miagoux, Q., Dispot, A., Elati, M., & Niarakis, A. (2020). Identification of putative master regulators in rheumatoid arthritis
synovial fibroblasts using gene expression data and network inference. Scientific reports, 10(1), 1-13. (DOI: https://doi.org/10.1038/s41598-020-73147-4 )

El Ati, Z., Machfar, H., Boussafa, H., Ati, N., Sioud, O. B. O., Zantour, B., … & Elati, M. (2020). Metabolic syndrome, malnutrition, and its associations with cardiovascular and all-cause mortality in hemodialysis patients: Follow-up for three years. Journal of Kidney Diseases and Transplantation, 31(1), 129. (DOI: https://doi.org/10.4103/1319-2442.279932 )

A. Assi, H. Mcheick, W. Dhifli. Data Linking Over RDF Knowledge Graphs: A Survey. Concurrency and Computation: Practice and Experience (Wiley) 32(19):e5746, 2020. (DOI: https://doi.org/10.1002/cpe.5746 )

2019

Coutant, A., Roper, K., Trejo-Banos, D., Bouthinon, D., Carpenter, M., Grzebyta, Santini, G.,  Soldano, H., Elati, M. # , Ramon, J. #,  Rouveirol, C. #, Saldatova, L. #, Ross, K. #,  Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. Proceedings of the National Academy of Sciences (PNAS), 116(36), 18142-18147. 2019. # These authors co-supervised the study (EU Chist-Era AdaLab projet). (DOI: https://doi.org/10.1073/pnas.190054811 )

W. Dhifli, Julia Puig, Aurélien Dispot, M. Elati. Latent network-based representations for large-scale gene expression data analysis. BMC Bioinformatics 19(13):466, 2019. (DOI: https://doi.org10.1186/s12859-018-2481-y )

A. Assi, H. Mcheick, A. Karawash, W. Dhifli. Context-Aware Instance Matching Through Graph Embedding in Lexical Semantic Space. 
Knowledge-Based Systems (Elsevier) 186:104925, 2019. (DOI: https://doi.org/10.1016/j.knosys.2019.104925 )

N.E.I. Karabadji, I. Khelf, H. Seridi, S. Aridhi, D. Remond, W. Dhifli. A Data Sampling and Attribute Selection Strategy for Improving Decision Tree Construction. Expert Systems With Applications (Elsevier) 129:84-96, 2019 (DOI: https://doi.org/10.1016/j.eswa.2019.03.052 )

T. K. Saha, A. Katebi, W. Dhifli, M. Al Hassan. Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins using Frequent Subgraph Mining. IEEE/ACM Transactions on Computational Biology and Bioinformatics 16(5): 1537 – 1549, 2019 (DOI: https://doi.org/10.1109/TCBB.2017.2756879 )

R. Saidi, W. Dhifli, M. Maddouri, E. M. Nguifo. Efficiently mining recurrent substructures from protein 3D-structure graphs. Journal of Computational Biology (JCB)(Mary Ann Liebert, Inc.) 26(6):561–571, 2019. (DOI: https://doi.org/10.1089/cmb.2018.0171 )

A. Assi, H. Mcheick, W. Dhifli. BIGMat: A Distributed Affinity-Preserving Random Walk Strategy for Instance Matching on Knowledge Graphs. IEEE BigData: pages 1028-1033, 2019. (DOI: https://doi.org/10.1109/BigData47090.2019.9006348 )

2018

A. Lopez-Rincon, A. Tonda, M. Elati, O. Schwander, B. Piwowarski, P. Gallinari. Evolutionary Optimization of Convolutional Neural Networks for Cancer miRNA Biomarkers Classification. Applied Soft Computing, 65: 91-100, 2019. DOI: https://doi.org/10.1016/j.asoc.2017.12.036 )

Karabadji, N. E. I., Beldjoudi, S., Seridi, H., Aridhi, S., & Dhifli, W. (2018). Improving memory-based user collaborative filtering with evolutionary multi-objective optimization. Expert Systems with Applications, 98, 153-165 (DOI: https://doi.org/10.1016/j.eswa.2018.01.015 )

SOFTWARE

CoRegFlux : R Bioconductor package (Trejo el al., BMC Systems Biology2017, Coutant et al., PNAS 2019). Integrating transcriptional activity in genome-scale models of metabolism.

LatNet : R package (Dhifli el al., BMC Bioinformatics 2018). Latent network-based representations for large-scale gene expression data analysis.

CoRegNet : is an R bioconductor package, which enables learning of gene regulatory networks from transcriptome data and infers master regulators controlling the transition between phenotypes.  (Download stats: https://bioconductor.org/packages/stats/bioc/CoRegNet/).

PEPPER : is a plugin cytoscape, which enables finding pathways connecting a protein set within a PPI-network using multi-objective optimization. Published in 2014  (Download stats: apps.cytoscape.org/download/stats/pepper/)

GREAT : The Genome REgulatory and Architecture Tools (GREAT). GREAT is a software suite of related and interconnected tools, currently able to perform systematic analyses of genome regularities (GREAT-Scan-Pattern) as well as improve TFBS prediction based on gene position information (GREATScan- PreCisIon).

Galaxy-X : An open-set multi-class classification method. Existing classification methods are designed to classify unknown instances within a set of previously known training classes. In open-set classification, the classifier may be confronted, during prediction, to observations that do not belong to any of the classes seen in training.

PROTNN : PROTNN is a function prediction approach for protein 3D-structures. PROTNN assigns to a query protein the function with the highest number of votes across the set of k nearest neighbor reference proteins based on a graph representation model and a pairwise similarity between vector embedding of both the query and the reference protein-graphs in structural and topological spaces.

PGR : An online repository of graphs representing all known protein 3D-structures. It aims at providing bioinformatics tools that facilitate the integration of graph theory techniques in the core of protein 3D-structure studies.

UnSubPatt : It is a feature selection approach for subgraphs. It selects a subset of representative subgraphs (called unsubstituted) from a given set of subgraphs based on label similarity defined in the domain knowledge.

TRS : Feature selection approach for subgraphs. It selects a small subset of topological representative subgraphs from a large set of subgraphs based on an embedding of subgraphs using a set of topological descriptors.

Ant-Motif : It is used to extract common substructures with an ant-like-shape (called ant-motifs) from a set of traceable graphs i.e., graphs having Hamiltonian path (eg, graphs of protein structures).