Ketil Maldes bilde

Ketil Malde

  • E-postketil.malde@uib.no
  • Besøksadresse
    HIB - Thormøhlens gate 55
    5006 Bergen
  • Postadresse
    Postboks 7803
    5020 Bergen


  • bioinformatikk
  • funksjonelle språk
  • maskinlæring/deep learning

Jeg har også en stilling som forsker ved Havforskningsinstituttet i gruppen for populasjonsgenetikk.

Vitenskapelig artikkel
  • Vis forfatter(e) (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. Proceedings of the Northern Lights Deep Learning Workshop. 8 sider.
  • Vis forfatter(e) (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science. 1829-1853.
  • Vis forfatter(e) (2023). Annotating otoliths with a deep generative model. ICES Journal of Marine Science. 55-65.
  • Vis forfatter(e) (2023). Age interpretation of cod otoliths using deep learning. Ecological Informatics. 11 sider.
  • Vis forfatter(e) (2023). A contrastive learning approach for individual re-identification in a wild fish population. Proceedings of the Northern Lights Deep Learning Workshop. 8 sider.
  • Vis forfatter(e) (2022). The salmon louse genome may be much larger than sequencing suggests. Scientific Reports. 1-14.
  • Vis forfatter(e) (2022). DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images. Fishes. 11 sider.
  • Vis forfatter(e) (2021). The salmon louse genome: Copepod features and parasitic adaptations. Genomics. 3666-3680.
  • Vis forfatter(e) (2021). Automatic interpretation of salmon scales using deep learning. Ecological Informatics. 10 sider.
  • Vis forfatter(e) (2021). A real-world dataset and data simulation algorithm for automated fish species identification. Geoscience Data Journal.
  • Vis forfatter(e) (2021). A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images. ICES Journal of Marine Science. 3780-3792.
  • Vis forfatter(e) (2020). Microbial communities associated with the parasitic copepod Lepeophtheirus salmonis. . Marine Genomics. 4 sider.
  • Vis forfatter(e) (2020). Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science. 12 sider.
  • Vis forfatter(e) (2020). Genome wide analysis reveals genetic divergence between Goldsinny wrasse populations. BMC Genetics. 1-15.
  • Vis forfatter(e) (2020). Acoustic classification in multifrequency echosounder data using deep convolutional neural networks. ICES Journal of Marine Science. 1391-1400.
  • Vis forfatter(e) (2019). Levels and temporal trends of persistent organic pollutants (POPs) in Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) from the southern Barents Sea. Environmental Research. 89-97.
  • Vis forfatter(e) (2019). An efficient protocol and data set for automated otolith image analysis. Geoscience Data Journal. 1-9.
  • Vis forfatter(e) (2019). Airgun blasts used in marine seismic surveys have limited effects on mortality, and no sublethal effects on behaviour or gene expression, in the copepod Calanus finmarchicus. ICES Journal of Marine Science. 2033-2044.
  • Vis forfatter(e) (2018). Judging a salmon by its spots: Environmental variation is the primary determinant of spot patterns in Salmo salar. BMC Ecology. 1-13.
  • Vis forfatter(e) (2018). Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science. 342-349.
  • Vis forfatter(e) (2018). Automatic interpretation of otoliths using deep learning. PLOS ONE. 1-14.
  • Vis forfatter(e) (2017). Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization between minke whale species. BMC Genomics. 1-11.
  • Vis forfatter(e) (2015). Characterization of a novel RXR receptor in the salmon louse (Lepeophtheirus salmonis, Copepoda) regulating growth and female reproduction. BMC Genomics. 22 sider.
  • Vis forfatter(e) (2014). Simulating a population genomics data set using FlowSim. BMC Research Notes.
  • Vis forfatter(e) (2014). Human-induced evolution caught in action: SNP-array reveals rapid amphi-atlantic spread of pesticide resistance in the salmon ecotoparasite Lepeophtheirus salmonis. BMC Genomics. 18 sider.
  • Vis forfatter(e) (2014). Gene expression in five salmon louse (Lepeophtheirus salmonis, Krøyer 1837) tissues. Marine Genomics. 39-44.
  • Vis forfatter(e) (2014). Estimating the information value of polymorphic sites using pooled sequences. BMC Genomics. 11 sider.
  • Vis forfatter(e) (2013). Increasing Sequence Search Sensitivity with Transitive Alignments. PLOS ONE. 7 sider.
  • Vis forfatter(e) (2013). How does sequence variability affect de novo assembly quality? Journal of Natural History. 901-910.
  • Vis forfatter(e) (2013). Filtering duplicate reads from 454 pyrosequencing data. Bioinformatics. 830-836.
  • Vis forfatter(e) (2012). Maternal 3 ' UTRs: from egg to onset of zygotic transcription in Atlantic cod. BMC Genomics. 14 sider.
  • Vis forfatter(e) (2011). The genome sequence of Atlantic cod reveals a unique immune system. Nature. 207-210.
  • Vis forfatter(e) (2011). Systematic exploration of error sources in pyrosequencing flowgram data. Bioinformatics. I304-I309.
  • Vis forfatter(e) (2011). Identification of vimentin- and elastin-like transcripts specifically expressed in developing notochord of Atlantic salmon (Salmo salar L.). Cell and Tissue Research. 191-202.
  • Vis forfatter(e) (2011). EST resources and establishment and validation of a 16 k cDNA microarray from Atlantic cod (Gadus morhua). Comparative Biochemistry and Physiology - Part D:Genomics and Proteomics. 23-30.
  • Vis forfatter(e) (2010). Characteristics of 454 pyrosequencing data-enabling realistic simulation with flowsim. Bioinformatics. i420-i425.
  • Vis forfatter(e) (2010). Calcium from salmon and cod bone is well absorbed in young healthy men: a double-blinded randomised crossover design. Nutrition & Metabolism. 9 sider.
  • Vis forfatter(e) (2009). Identification of immune related genes in Atlantic halibut (Hippoglossus hippoglossus L.) following in vivo antigenic and in vitro mitogenic stimulation. Fish and Shellfish Immunology. 729-738.
  • Vis forfatter(e) (2008). The effect of sequence quality on sequence alignment. Bioinformatics. 897-900.
  • Vis forfatter(e) (2008). Repeats and EST analysis for new organisms. BMC Genomics. 7 sider.
  • Vis forfatter(e) (2006). RBR: library-less repeat detection for ESTs. Bioinformatics. 2232-2236.
  • Vis forfatter(e) (2006). Calculating PSSM probabilities with lazy dynamic programming. Journal of functional programming. 75-81.
  • Vis forfatter(e) (2005). Masking repeats while clustering ESTs. Nucleic Acids Research (NAR). 2176-2180.
  • Vis forfatter(e) (2005). A graph based algorithm for generating EST consensus sequences. Bioinformatics. 1371-1375.
  • Vis forfatter(e) (2003). Fast Sequence Clustering Using a Suffix Array Algorithm. Bioinformatics. 1221-1226.
  • Vis forfatter(e) (2003). A Fast Algorithm for EST Clustering using Suffix Arrays. Bioinformatics.
  • Vis forfatter(e) (2021). Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics. 2021 - 25. 2021 - 25. .
  • Vis forfatter(e) (2020). Big Data in Marine Science. 6. 6. .
  • Vis forfatter(e) (2019). Machine learning to improve marine science for the sustainability of living ocean resources: Report from the 2019 Norway - U.S. Workshop. .
  • Vis forfatter(e) (2018). Report of the Workshop on Machine Learning in Marine Science (WKMLEARN). .
Faglig foredrag
  • Vis forfatter(e) (2022). Artificial Intelligence methods for fisheries management.
  • Vis forfatter(e) (2019). Using a CNN trained on synthetic data for fish species identification.
  • Vis forfatter(e) (2019). Using a CNN trained on synthetic data for fish species identification.
  • Vis forfatter(e) (2016). Big Data og Big Analysis - hvordan drikke fra brannslangen.
  • Vis forfatter(e) (2015). Insitute of Marine Research - a research and advisory institute.
Vitenskapelig foredrag
  • Vis forfatter(e) (2024). Unraveling Acoustic Signal Patterns in Fisheries Through DINO-Based Self-Supervised Learning.
  • Vis forfatter(e) (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary.
  • Vis forfatter(e) (2023). A story about data extraction and deep learning applied to fishery acoustic data.
  • Vis forfatter(e) (2022). Using deep learning models to count and identify fish species from in-trawl images.
  • Vis forfatter(e) (2022). Selecting maximally informative frequency subsets for acoustic surveys.
  • Vis forfatter(e) (2022). Exploring imaging protocols and neural network architectures for automated otolith analysis.
  • Vis forfatter(e) (2022). An online otolith age reader using deep neural networks: Perspectives and challenges.
  • Vis forfatter(e) (2022). A deep learning approach for individual re-identification (re-ID) of fish in the wild.
  • Vis forfatter(e) (2021). Combined trawl-mounted optic and acoustic methods to study the mesopelagic ecosystem.
  • Vis forfatter(e) (2019). The COGMAR project.
  • Vis forfatter(e) (2018). Drowning in data: Can deep learning approaches be the solution?
  • Vis forfatter(e) (2013). Identifying diagnostic SNPs in the presence of sequencing errors.
  • Vis forfatter(e) (2013). Can Software Transactional Memory make concurrent programs simple and safe?
  • Vis forfatter(e) (2012). Transcriptomic analysis of the salmon louse.
  • Vis forfatter(e) (2012). The Salmon Louse Genome Project.
  • Vis forfatter(e) (2012). Attempts to induce sterility in Atlantic salmon by morfolino and zink finger techniques.
  • Vis forfatter(e) (2011). Maternal 3'UTRs: from egg to onset of zygotic transcription in Atlantic cod.
  • Vis forfatter(e) (2009). Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell.
  • Vis forfatter(e) (2002). A Fast Algorithm for EST Clustering using Suffix Trees.
Short communication
  • Vis forfatter(e) (2011). Flower: extracting information from pyrosequencing data. Bioinformatics. 1041-1042.
  • Vis forfatter(e) (2023). Object Tracking Approach for Catch Estimation on Trawl Surveys.
  • Vis forfatter(e) (2022). Selecting Maximally Informative Frequency Subsets for Acoustic Surveys.
  • Vis forfatter(e) (2023). On the Significance of Distance in Machine Learning.
  • Vis forfatter(e) (2005). Algorithms for the Analysis of Expressed Sequence Tags.
  • Vis forfatter(e) (2023). Escaping the black box: explicit annotation of otolith growth rings with deep learning.
  • Vis forfatter(e) (2023). Annotating Otoliths with a Deep Generative Model.
  • Vis forfatter(e) (2020). Automatic interpretation of salmon scales using deep learning.
  • Vis forfatter(e) (2019). Otoliths as life history indicators.
  • Vis forfatter(e) (2017). Automatisk klassifisering av svømming i not .
  • Vis forfatter(e) (2013). A data storage strategy for generic, heterogenous scientific data.
  • Vis forfatter(e) (2021). Machine Learning + Marine Science: Critical Role of Partnerships in Norway. Journal of Ocean Technology. 1-9.

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