| Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data A Pratapa, AP Jalihal, JN Law, A Bharadwaj, TM Murali Nature Methods 17 (2), 147-154, 2020 | 316 | 2020 |
| Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks A Pratapa, S Balachandran, K Raman Bioinformatics 31 (20), 3299-3305, 2015 | 47 | 2015 |
| Image-based cell phenotyping with deep learning. A Pratapa, M Doron, JC Caicedo Current Opinion in Chemical Biology 65, 9-17, 2021 | 40 | 2021 |
| Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease MJ Mason, C Schinke, CLP Eng, F Towfic, F Gruber, A Dervan, BS White, ... Leukemia 34 (7), 1866-1874, 2020 | 28 | 2020 |
| Reconstructing signaling pathways using regular language constrained paths MJ Wagner, A Pratapa, TM Murali Bioinformatics 35 (14), i624-i633, 2019 | 6 | 2019 |
| Computational prediction of synthetic lethals in genome-scale metabolic models using fast-SL K Raman, A Pratapa, O Mohite, S Balachandran Metabolic network reconstruction and modeling: methods and protocols, 315-336, 2018 | 5 | 2018 |
| CrossPlan: systematic planning of genetic crosses to validate mathematical models A Pratapa, N Adames, P Kraikivski, N Franzese, JJ Tyson, J Peccoud, ... Bioinformatics 34 (13), 2237–2244, 2018 | 3 | 2018 |
| 654MO High dimensional immuno-phenotyping of immunotherapy response in head and neck cancer KJ O'Byrne, N Ma, H Sadeghirad, N Jhaveri, J Monkman, A Pratapa, ... Annals of Oncology 33, S841, 2022 | 1 | 2022 |
| Deep ultrahigh-plex spatial phenotyping of human cancer tissues N Jhaveri, N Nikulina, H Zong, N Ma, BB Cheikh, A Pratapa, Y Kassim, ... Cancer Research 82 (12_Supplement), 3877-3877, 2022 | 1 | 2022 |
| Deep spatial phenotyping of microglial populations and pathological features in Alzheimer’s disease brains using CODEX Multiplexed Imaging P Sanchez‐Molina, A Pratapa, J Singh, A Chiot, A Bogachuk, N Nikulina, ... Alzheimer's & Dementia 18, e066433, 2022 | | 2022 |
| 120 Decoding immunotherapy responses with high-dimensional and high-throughput spatial phenotyping of non-small cell lung cancer (NSCLC) N Ma, A Pratapa, J Monkman, K O’Byrne, O Braubach, A Kulasinghe Journal for ImmunoTherapy of Cancer 10 (Suppl 2), 2022 | | 2022 |
| Spatially resolved single-cell features reveal distinct cellular maps in the tumour microenvironment of melanoma patients with primary and acquired resistance to immunotherapies C Quek, A Pratapa, I Silva, G Al-Eryani, A Mayer, N Bartonicek, K Harvey, ... HISTOPATHOLOGY 81, 139-140, 2022 | | 2022 |
| EP16. 04-003 High Dimensional Spatial Profiling of the NSCLC Tumour Microenvironment A Kulasinghe, N Ma, J Monkman, A Pratapa, O Braubach, K O'Byrne Journal of Thoracic Oncology 17 (9), S611, 2022 | | 2022 |
| DEEP-LEARNING BASED CELLULAR PROFILING TO ENABLE PATIENT-SPECIFIC GENETIC AND PHARMACOLOGICAL PERTURBATION SCREENS IN INHERITED NEUROMUSCULAR DISEASES W Pernice, M Doron, A Quach, A Pratapa, S Kenjeyev, J Shintaku, ... JOURNAL OF THE PERIPHERAL NERVOUS SYSTEM 27, S105-S105, 2022 | | 2022 |
| How to build regulatory networks from single-cell gene expression data A Pratapa, AP Jalihal, JN Law, A Bharadwaj, TM Murali Proceedings of the 11th ACM International Conference on Bioinformatics …, 2020 | | 2020 |
| Algorithms for regulatory network inference and experiment planning in systems biology A Pratapa Virginia Tech, 2020 | | 2020 |
| Efficient Synthesis of Mutants Using Genetic Crosses A Pratapa, AP Jalihal, SS Ravi, TM Murali Proceedings of the 2018 ACM International Conference on Bioinformatics …, 2018 | | 2018 |
| Birds in a Forest S Mehta, A Pratapa, P Summers | | |
T. M. MuraliDepartment of Computer Science, Virginia TechZweryfikowany adres z cs.vt.edu
