| nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer R Hollandi, A Szkalisity, T Toth, E Tasnadi, C Molnar, B Mathe, I Grexa, ... Cell Systems, 2020 | 191 | 2020 |
| Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays K Smith, F Piccinini, T Balassa, K Koos, T Danka, H Azizpour, P Horvath Cell systems 6 (6), 636-653, 2018 | 86 | 2018 |
| Intelligent image-based in situ single-cell isolation C Brasko, K Smith, C Molnar, N Farago, L Hegedus, A Balind, T Balassa, ... Nature communications 9 (1), 226, 2018 | 85 | 2018 |
| Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data F Piccinini, T Balassa, A Szkalisity, C Molnar, L Paavolainen, K Kujala, ... Cell systems 4 (6), 651-655. e5, 2017 | 84 | 2017 |
| A deep convolutional neural network approach for astrocyte detection I Suleymanova, T Balassa, S Tripathi, C Molnar, M Saarma, Y Sidorova, ... Scientific reports 8, 2018 | 61 | 2018 |
| A deep learning framework for nucleus segmentation using image style transfer R Hollandi, A Szkalisity, T Toth, E Tasnadi, C Molnar, B Mathe, I Grexa, ... Biorxiv, 580605, 2019 | 46 | 2019 |
| Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates F Piccinini, T Balassa, A Carbonaro, A Diosdi, T Toth, N Moshkov, ... Computational and structural biotechnology journal 18, 1287-1300, 2020 | 41 | 2020 |
| Hsp70-associated chaperones have a critical role in buffering protein production costs Z Farkas, D Kalapis, Z Bodi, B Szamecz, A Daraba, K Almasi, K Kovacs, ... Elife 7, e29845, 2018 | 37 | 2018 |
| Automatic deep learning-driven label-free image-guided patch clamp system K Koos, G Oláh, T Balassa, N Mihut, M Rózsa, A Ozsvár, E Tasnadi, ... Nature communications 12 (1), 1-11, 2021 | 32 | 2021 |
| Neuroinflammatory processes are augmented in mice overexpressing human heat-shock protein B1 following ethanol-induced brain injury B Dukay, FR Walter, JP Vigh, B Barabási, P Hajdu, T Balassa, E Migh, ... Journal of Neuroinflammation 18, 1-24, 2021 | 26 | 2021 |
| Environmental properties of cells improve machine learning-based phenotype recognition accuracy T Toth, T Balassa, N Bara, F Kovacs, A Kriston, C Molnar, L Haracska, ... Scientific reports 8 (1), 10085, 2018 | 20 | 2018 |
| Regression plane concept for analysing continuous cellular processes with machine learning A Szkalisity, F Piccinini, A Beleon, T Balassa, IG Varga, E Migh, C Molnar, ... Nature communications 12 (1), 2532, 2021 | 10 | 2021 |
| Probe set selection for targeted spatial transcriptomics LB Kuemmerle, MD Luecken, AB Firsova, LB de Andrade e Sousa, ... bioRxiv, 2022.08. 16.504115, 2022 | 7 | 2022 |
| Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology K Koos, G Oláh, T Balassa, N Mihut, M Rózsa, A Ozsvár, E Tasnadi, ... bioRxiv, 2020.05. 05.078162, 2020 | 4 | 2020 |
| Topographic atlas of cell states identifies regional gene expression in the adult human lung A Firsova, S Marco Salas, L Kuemmerle, X Abalo, L Larsson, ... | | 2024 |
| Cell identification and phenotyping using classical machine learning and deep learning T Balassa | | 2021 |
Peter HorvathSzeged, Helsinki, ETH Zurich, INRIA, Sophia Antipolis在 brc.hu 的电子邮件经过验证
