ACTION on Cancer
Lead Research Organisation:
Goldsmiths University of London
Department Name: Computing Department
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Gabriel K Reder
(2023)
Genesis-DB: a database for autonomous laboratory systems
in bioinformatics advances
King R
(2018)
Automating Sciences: Philosophical and Social Dimensions
in IEEE Technology and Society Magazine
Roper K
(2022)
Testing the reproducibility and robustness of the cancer biology literature by robot.
in Journal of the Royal Society, Interface
Olier I
(2018)
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
in Machine learning
Orhobor O
(2022)
Imbalanced regression using regressor-classifier ensembles
in Machine Learning
Wang K
(2021)
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.
in NPJ systems biology and applications
| Description | we are developing new ML tools for improving cancer-relevant predictions |
| Exploitation Route | Cancer patients could be prescribed more effective chemotherapy drug combinations |
| Sectors | Healthcare |
| Description | - SkyLab Bio company is using machine learning software developed within this project. - University of London is using our Robot Scientist as a case study in their AI module on online MSc data science programme (~500 students), and online BSc Computer Science programme (~5,000 students) |
| First Year Of Impact | 2022 |
| Sector | Chemicals,Education |
| Impact Types | Societal Economic |
| Title | Supplementary Information showing a list of all the ligand features considered from A simple spatial extension to the extended connectivity interaction features for binding affinity prediction |
| Description | The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplementary_Information_showing_a_list_of_all_the_ligand_... |
| Title | Supplementary Information showing a list of all the ligand features considered from A simple spatial extension to the extended connectivity interaction features for binding affinity prediction |
| Description | The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplementary_Information_showing_a_list_of_all_the_ligand_... |
| Title | Supplementary table showing the selected important features from A simple spatial extension to the extended connectivity interaction features for binding affinity prediction |
| Description | The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplementary_table_showing_the_selected_important_features... |
| Title | Supplementary table showing the selected important features from A simple spatial extension to the extended connectivity interaction features for binding affinity prediction |
| Description | The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplementary_table_showing_the_selected_important_features... |
| Description | Arctoris Ltd |
| Organisation | Arctoris |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Our research team provides description of experiments and research hypotheses. This is a test case for Arctoris to develop an interactive system with external researchers. |
| Collaborator Contribution | Arctoris (https://www.arctoris.com/) is contributing to the project by running experiments in their lab. |
| Impact | experimental data |
| Start Year | 2020 |
| Description | Imagen |
| Organisation | Imagen Therapeutics |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | expert advise on the analysis of data |
| Collaborator Contribution | provided unique datasets |
| Impact | datasets |
| Start Year | 2021 |