Author

K. Maksymenko

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:electrophysiology· 2 Organism:human· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Assay:proteomics· 1 Research Focus:synaptic & neural circuits· 1

Frequent Co-Authors

A. K. Clarke· 2 shared D. Farina· 2 shared I. M. Guerra· 2 shared S. Deslauriers-Gauthier· 2 shared A. H. Caillet· 1 shared A. N. Lupas· 1 shared B. Hernandez Alvarez· 1 shared C. Lengerke· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioengineering

A. K. Clarke

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Tuning of granulopoietic signaling by de novo designed agonists

T. Ullrich, C. Pollmann, M. Ritter, J. Haaf, N. Aghaallaei, I. Tesakov, M. El-Riz, K. Maksymenko, V. Hatskovska, S. Kandabarau, M. Klimiankou, C. Lengerke, K. Welte, B. Hernandez Alvarez, P. Müller, , M. ElGamacy

This paper designs and characterizes agonists that tune G-CSFR activity to bias granulopoiesis over proliferation, showing improved stability and selectivity compared to native G-CSF.

bioRxiv · Nov 25, 2023 · PreprintBioengineering

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

S. Ma, I. M. Guerra, A. H. Caillet, J. Zhao, A. K. Clarke, K. Maksymenko, S. Deslauriers-Gauthier, X. Sheng, X. Zhu, D. Farina

NeuroMotion generates synthetic EMG signals during voluntary movements by integrating musculoskeletal models, neural networks, and motor unit models, enabling analysis of neuromechanical interactions.

bioRxiv · Oct 5, 2023 · PreprintBioengineering

Towards the Myoelectric Digital Twin: Ultra Fast and Realistic Modelling for Deep Learning

K. Maksymenko, A. K. Clarke, I. M. Guerra, S. Deslauriers-Gauthier, D. Farina

This paper introduces a highly realistic and ultra-fast computational model for simulating electromyography signals to train deep learning algorithms, enabling data augmentation and improved decoding of muscular signals.

bioRxiv · Jun 7, 2021 · PreprintBioengineering