Advances in de novo drug design: from conventional to machine learning methods VD Mouchlis, A Afantitis, A Serra, M Fratello, AG Papadiamantis, V Aidinis, ... International journal of molecular sciences 22 (4), 1676, 2021 | 215 | 2021 |
NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment A Afantitis, G Melagraki, P Isigonis, A Tsoumanis, DD Varsou, ... Computational and Structural Biotechnology Journal 18, 583-602, 2020 | 99 | 2020 |
MVDA: a multi-view genomic data integration methodology A Serra, M Fratello, V Fortino, G Raiconi, R Tagliaferri, D Greco BMC bioinformatics 16, 1-13, 2015 | 88 | 2015 |
Cobalt nanoparticles trigger ferroptosis-like cell death (oxytosis) in neuronal cells: potential implications for neurodegenerative disease G Gupta, A Gliga, J Hedberg, A Serra, D Greco, I Odnevall Wallinder, ... | 54 | 2020 |
Transcriptomics in toxicogenomics, part I: experimental design, technologies, publicly available data, and regulatory aspects PAS Kinaret, A Serra, A Federico, P Kohonen, P Nymark, I Liampa, MK Ha, ... Nanomaterials 10 (4), 750, 2020 | 50 | 2020 |
Machine learning for bioinformatics and neuroimaging A Serra, P Galdi, R Tagliaferri Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1248, 2018 | 48 | 2018 |
Transcriptomics in toxicogenomics, part III: data modelling for risk assessment A Serra, M Fratello, L Cattelani, I Liampa, G Melagraki, P Kohonen, ... Nanomaterials 10 (4), 708, 2020 | 47 | 2020 |
eUTOPIA: solUTion for Omics data PreprocessIng and Analysis VS Marwah, G Scala, PAS Kinaret, A Serra, H Alenius, V Fortino, D Greco Source code for biology and medicine 14 (1), 1, 2019 | 45 | 2019 |
Transcriptomics in toxicogenomics, part II: preprocessing and differential expression analysis for high quality data A Federico, A Serra, MK Ha, P Kohonen, JS Choi, I Liampa, P Nymark, ... Nanomaterials 10 (5), 903, 2020 | 40 | 2020 |
Can an InChI for nano address the need for a simplified representation of complex nanomaterials across experimental and nanoinformatics studies? I Lynch, A Afantitis, T Exner, M Himly, V Lobaskin, P Doganis, D Maier, ... Nanomaterials 10 (12), 2493, 2020 | 39 | 2020 |
FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments DG Giovanni Scala, Angela Serra, Veer Singh Marwah, Laura Aliisa Saarimäki BMC Bioinformatics 20 (1), 79, 2019 | 36* | 2019 |
INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials A Serra, I Letunic, V Fortino, RD Handy, B Fadeel, R Tagliaferri, D Greco Scientific Reports 9, 179, 2019 | 35 | 2019 |
Representing and describing nanomaterials in predictive nanoinformatics E Wyrzykowska, A Mikolajczyk, I Lynch, N Jeliazkova, N Kochev, ... Nature Nanotechnology 17 (9), 924-932, 2022 | 34 | 2022 |
Inform: inference of network response modules VS Marwah, PAS Kinaret, A Serra, G Scala, A Lauerma, V Fortino, ... Bioinformatics 34 (12), 2136-2138, 2018 | 33 | 2018 |
Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment A Pavel, G Del Giudice, A Federico, A Di Lieto, PAS Kinaret, A Serra, ... Briefings in bioinformatics 22 (2), 1430-1441, 2021 | 31 | 2021 |
Robust clustering of noisy high-dimensional gene expression data for patients subtyping P Coretto, A Serra, R Tagliaferri Bioinformatics, 2018 | 31 | 2018 |
Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data A Serra, P Coretto, M Fratello, R Tagliaferri Bioinformatics 34 (4), 625-634, 2018 | 26 | 2018 |
Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials LA Saarimäki, A Federico, I Lynch, AG Papadiamantis, A Tsoumanis, ... Scientific data 8 (1), 49, 2021 | 25 | 2021 |
BMDx: a graphical Shiny application to perform Benchmark Dose analysis for transcriptomics data DG Angela Serra, Laura Saarimäki, Michele Fratello, Marwah Veer Singh Bioinformatics, 2020 | 24 | 2020 |
Phosphorylation of NFATC1 at PIM1 target sites is essential for its ability to promote prostate cancer cell migration and invasion SK Eerola, NM Santio, S Rinne, P Kouvonen, GL Corthals, M Scaravilli, ... Cell Communication and Signaling 17, 1-16, 2019 | 23 | 2019 |