Microscopic energy consumption modelling of electric buses: model development, calibration, and validation

in Transportation Research. Part D, Transport and Environment (2021)

Diversity Preserving Genetic Algorithms - Application to the Inverted Folding Problem and Analogous Formulated Benchmarks

Doctoral thesis (2016)

Protein structure prediction is an essential step in understanding the molecular mechanisms of living cells with widespread applications in biotechnology and health. Among the open problems in the field ... [more ▼]

Protein structure prediction is an essential step in understanding the molecular mechanisms of living cells with widespread applications in biotechnology and health. Among the open problems in the field, the Inverse Folding Problem (IFP) that consists in finding sequences that fold into a defined structure is, in itself, an important research problem at the heart of most rational protein design approaches. In brief, solutions to the IFP are protein sequences that will fold into a given protein structure, contrary to conventional structure prediction where the solution consists of the structure into which a given sequence folds. This inverse approach is viewed as a simplification due to the fact that the near infinite number of structure conformations of a protein can be disregarded, and only sequence to structure compatibility needs to be determined. Additional emphasis has been put on the generation of many sequences dissimilar from the known reference sequence instead of finding only one solution. To solve the IFP computationally, a novel formulation of the problem was proposed in which possible problem solutions are evaluated in terms of their predicted secondary structure match. In addition, two specialised Genetic Algorithms (GAs) were developed specifically for solving the IFP problem and compared with existing algorithms in terms of performance. Experimental results outlined the superior performance of the developed algorithms, both in terms of model score and diversity of the generated sets of problem solutions, i.e. new protein sequences. A number of landscape analysis experiments were conducted on the IFP model, enabling the development of an original benchmark suite of analogous problems. These benchmarks were shown to share many characteristics with their IFP model counterparts, but are executable in a fraction of the time. To validate the IFP model and the algorithm output, a subset of the generated solutions were selected for further inspection through full tertiary structure prediction and comparison to the original protein structure. Congruence was then assessed by super-positioning and secondary structure annotation statistics. The results demonstrated that an optimisation process relying on a fast secondary structure approximation, such as the IFP model, permits to obtain meaningful sequences. [less ▲]

Preference-Based Genetic Algorithm for Solving the Bio-Inspired NK Landscape Benchmark

in 7th European Symposium on Computational Intelligence and Mathematics (ESCIM) (2015, October)

DISEASES: text mining and data integration of disease-gene associations.

in Methods (2015), 74

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The ... [more ▼]

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. [less ▲]

A Novel Multi-objectivisation Approach for Optimising the Protein Inverse Folding Problem

in Applications of Evolutionary Computation: 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings (2015)

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding ... [more ▼]

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding Problem (IFP) is in itself an important research problem, but also at the heart of most rational protein design approaches. In brief, the IFP consists in finding sequences that will fold into a given structure, rather than determining the structure for a given sequence - as in conventional structure prediction. In this work we present a Multi Objective Genetic Algorithm (MOGA) using the diversity-as-objective (DAO) variant of multi-objectivisation, to optimise secondary structure similarity and sequence diversity at the same time, hence pushing the search farther into wide-spread areas of the sequence solution-space. To control the high diversity generated by the DAO approach, we add a novel Quantile Constraint (QC) mechanism to discard an adjustable worst quantile of the population. This DAO-QC approach can efficiently emphasise exploitation rather than exploration to a selectable degree achieving a trade-off producing both better and more diverse sequences than the standard Genetic Algorithm (GA). To validate the final results, a subset of the best sequences was selected for tertiary structure prediction. The super-positioning with the original protein structure demonstrated that meaningful sequences are generated underlining the potential of this work. [less ▲]

Evolutionary multi objective optimisation with diversity as objective for the protein structure similarity problem

Scientific Conference (2014)

COMPARTMENTS: unification and visualization of protein subcellular localization evidence.

in Database: the Journal of Biological Databases and Curation (2014), 2014

Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high ... [more ▼]

Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high-throughput microscopy-based screens and predicted from primary sequence. To get a comprehensive view of the localization of a protein, it is thus necessary to consult multiple databases and prediction tools. To address this, we present the COMPARTMENTS resource, which integrates all sources listed above as well as the results of automatic text mining. The resource is automatically kept up to date with source databases, and all localization evidence is mapped onto common protein identifiers and Gene Ontology terms. We further assign confidence scores to the localization evidence to facilitate comparison of different types and sources of evidence. To further improve the comparability, we assign confidence scores based on the type and source of the localization evidence. Finally, we visualize the unified localization evidence for a protein on a schematic cell to provide a simple overview. Database URL: http://compartments.jensenlab.org. [less ▲]

Novel efficient asynchronous cooperative co-evolutionary multi-objective algorithms

in Congress on Evolutionary Computation (CEC) (2012)