| 1. |
- Abdel-Rehim, Abbi, et al.
(författare)
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Protein-ligand binding affinity prediction exploiting sequence constituent homology
- 2023
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Ingår i: Bioinformatics. - 1367-4803 .- 1367-4811. ; 39:8
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS: The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION: Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
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| 2. |
- Brunnsåker, Daniel, 1992, et al.
(författare)
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High-throughput metabolomics for the design and validation of a diauxic shift model
- 2023
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Ingår i: NPJ systems biology and applications. - : Springer Nature. - 2056-7189. ; 9:1, s. 11-
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Tidskriftsartikel (refereegranskat)abstract
- Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.
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| 3. |
- Gower, Alexander, 1993, et al.
(författare)
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LGEM+ : A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction
- 2023
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Ingår i: Discovery Science - 26th International Conference, DS 2023, Proceedings. - : Springer Nature. ; 14276 LNAI, s. 628-643
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Konferensbidrag (refereegranskat)abstract
- Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.
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| 4. |
- Kronström, Filip, 1995, et al.
(författare)
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RIMBO - An Ontology for Model Revision Databases
- 2023
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14276 LNAI, s. 523-534
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Konferensbidrag (refereegranskat)abstract
- The use of computational models is growing throughout most scientific domains. The increased complexity of such models, as well as the increased automation of scientific research, imply that model revisions need to be systematically recorded. We present RIMBO (Revisions for Improvements of Models in Biology Ontology), which describes the changes made to computational biology models. The ontology is intended as the foundation of a database containing and describing iterative improvements to models. By recording high level information, such as modelled phenomena, and model type, using controlled vocabularies from widely used ontologies, the same database can be used for different model types. The database aims to describe the evolution of models by recording chains of changes to them. To make this evolution transparent, emphasise has been put on recording the reasons, and descriptions, of the changes. We demonstrate the usefulness of a database based on this ontology by modelling the update from version 8.4.1 to 8.4.2 of the genome-scale metabolic model Yeast8, a modification proposed by an abduction algorithm, as well as thousands of simulated revisions. This results in a database demonstrating that revisions can successfully be modelled in a semantically meaningful and storage efficient way. We believe such a database is necessary for performing automated model improvement at scale in systems biology, as well as being a useful tool to increase the openness and traceability for model development. With minor modifications the ontology can also be used in other scientific domains. The ontology is made available at https://github.com/filipkro/rimbo and will be continually updated.
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| 5. |
- Kronström, Filip, et al.
(författare)
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RIMBO - An Ontology for Model Revision Databases
- 2023
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Ingår i: Discovery Science - 26th International Conference, DS 2023, Proceedings. - : Springer Nature. ; 14276 LNAI, s. 523-534
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Konferensbidrag (refereegranskat)abstract
- The use of computational models is growing throughout most scientific domains. The increased complexity of such models, as well as the increased automation of scientific research, imply that model revisions need to be systematically recorded. We present RIMBO (Revisions for Improvements of Models in Biology Ontology), which describes the changes made to computational biology models. The ontology is intended as the foundation of a database containing and describing iterative improvements to models. By recording high level information, such as modelled phenomena, and model type, using controlled vocabularies from widely used ontologies, the same database can be used for different model types. The database aims to describe the evolution of models by recording chains of changes to them. To make this evolution transparent, emphasise has been put on recording the reasons, and descriptions, of the changes. We demonstrate the usefulness of a database based on this ontology by modelling the update from version 8.4.1 to 8.4.2 of the genome-scale metabolic model Yeast8, a modification proposed by an abduction algorithm, as well as thousands of simulated revisions. This results in a database demonstrating that revisions can successfully be modelled in a semantically meaningful and storage efficient way. We believe such a database is necessary for performing automated model improvement at scale in systems biology, as well as being a useful tool to increase the openness and traceability for model development. With minor modifications the ontology can also be used in other scientific domains. The ontology is made available at https://github.com/filipkro/rimbo and will be continually updated.
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| 6. |
- Orhobor, Oghenejokpeme I., et al.
(författare)
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Imbalanced regression using regressor-classifier ensembles
- 2023
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Ingår i: Machine Learning. - : Springer Science and Business Media LLC. - 0885-6125 .- 1573-0565. ; 112:4, s. 1365-1387
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Tidskriftsartikel (refereegranskat)abstract
- We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains. Additionally, we compared our algorithm to two resampling methods aimed at addressing imbalanced datasets. Our results show that the proposed extension is highly unlikely to perform worse than the base case, and on average outperforms the two resampling methods with significant differences in performance.
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| 7. |
- Reder, Gabriel, 1992, et al.
(författare)
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Genesis-DB: a database for autonomous laboratory systems
- 2023
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Ingår i: Bioinformatics Advances. - 2635-0041. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI)-driven laboratory automation - combining robotic labware and autonomous software agents - is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.
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| 8. |
- Wang, Yuxuan, et al.
(författare)
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Extrapolation is Not the Same as Interpolation
- 2023
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14276 LNAI, s. 277-292
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Konferensbidrag (refereegranskat)abstract
- We propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): F(drug) → activity. The PubMed server lists around twenty thousand papers doing this. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based around learning a bivariate function that predicts the difference in activities of two drugs: F(drug1, drug2) → signed difference in activity. This formulation is general and potentially suitable for problems to find samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines (SVMs), random forests (RFs), and Gradient Boosting Machines (XGBs). We compared the formulation with standard regression on thousands of drug design datasets, and hundreds of gene expression datasets. The test set extrapolation metrics use the concept of classification metrics to count the identification of extraordinary examples (with greater values than the training set), and top-performing examples (within the top 10% of the whole dataset). On these metrics our pairwise formulation vastly outperformed standard regression for SVMs, RFs, and XGBs. We expect this success to extrapolate to other extrapolation problems.
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| 9. |
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