Anna Palczewska
Anna Palczewska
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Cited by
Interpreting random forest classification models using a feature contribution method
A Palczewska, J Palczewski, R Marchese Robinson, D Neagu
Integration of reusable systems, 193-218, 2014
Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets
RL Marchese Robinson, A Palczewska, J Palczewski, N Kidley
Journal of chemical information and modeling 57 (8), 1773-1792, 2017
Interpreting random forest models using a feature contribution method
A Palczewska, J Palczewski, RM Robinson, D Neagu
2013 IEEE 14th international conference on Information Reuse & Integration …, 2013
Data governance in predictive toxicology: A review
X Fu, A Wojak, D Neagu, M Ridley, K Travis
Journal of cheminformatics 3, 1-16, 2011
Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.
A Cassano, RL Marchese Robinson, A Palczewska, T Puzyn, A Gajewicz, ...
Altern Lab Anim 44 (6), 533-556, 2016
Towards model governance in predictive toxicology
A Palczewska, X Fu, P Trundle, L Yang, D Neagu, M Ridley, K Travis
International Journal of Information Management 33 (3), 567-582, 2013
A league-wide investigation into variability of rugby league match running from 322 Super League games
N Dalton-Barron, A Palczewska, SJ McLaren, G Rennie, C Beggs, G Roe, ...
Science and Medicine in Football 5 (3), 225-233, 2021
A machine learning approach to short-term body weight prediction in a dietary intervention program
O Babajide, T Hissam, P Anna, G Anatoliy, A Astrup, J Alfredo Martinez, ...
Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020
Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?
R White, A Palczewska, D Weaving, N Collins, B Jones
Journal of Sports Sciences 40 (2), 164-174, 2022
Using Pareto points for model identification in predictive toxicology
A Palczewska, D Neagu, M Ridley
Journal of Cheminformatics 5, 1-16, 2013
Lccspm: l-length closed contiguous sequential patterns mining algorithm to find frequent athlete movement patterns from gps
VE Adeyemo, A Palczewska, B Jones
2021 20th IEEE International Conference on Machine Learning and Applications …, 2021
Development of an expected possession value model to analyse team attacking performances in rugby league
T Sawczuk, A Palczewska, B Jones
Plos one 16 (11), e0259536, 2021
Application of unsupervised learning in weight-loss categorisation for weight management programs
O Babajide, H Tawfik, A Palczewska, A Gorbenko, A Astrup, JA Martinez, ...
2019 10th International Conference on Dependable Systems, Services and …, 2019
Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play
N Collins, R White, A Palczewska, D Weaving, N Dalton-Barron, B Jones
European Journal of Sport Science 23 (2), 201-209, 2023
Clustering of match running and performance indicators to assess between-and within-playing position similarity in professional rugby league
N Dalton-Barron, A Palczewska, D Weaving, G Rennie, C Beggs, G Roe, ...
Journal of Sports Sciences 40 (15), 1712-1721, 2022
Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns
VE Adeyemo, A Palczewska, B Jones, D Weaving
Plos one 19 (5), e0301608, 2024
Markov decision processes with contextual nodes as a method of assessing attacking player performance in rugby league
T Sawczuk, A Palczewska, B Jones
Advances in Computational Intelligence Systems: Contributions Presented at …, 2022
RobustSPAM for inference from noisy longitudinal data and preservation of privacy
A Palczewska, J Palczewski, G Aivaliotis, L Kowalik
2017 16th IEEE international conference on machine learning and applications …, 2017
Advances in Drug Toxicology
U Gundert‑Remy, J Sachs, F Bévalot, IM McIntyre, A Palczewska, ...
Advances in Drug Toxicology, 341, 2016
In silico chemistry-based workflows to facilitate ADMET prediction for cosmetics-related substances
AN Richarz, P Alov, SJ Enoch, S Kovarich, Y Lan, T Meinl, C Mellor, ...
Toxicology Letters 2 (238), S170, 2015
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