Author

Anni S. Halkola

University of Turku

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Approach:benchmark study· 1 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Assay:electronic health records· 1

Frequent Co-Authors

Teemu D. LaajalaiD· 2 shared Alex C. SoupiriD· 1 shared Brooke L. FridleyiD· 1 shared Christelle ColiniD· 1 shared Federico C. F. CalboliiD· 1 shared James C. CostelloiD· 1 shared Jordan H. CreediD· 1 shared Kaisa JokiiD· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Teemu D. LaajalaORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Teemu D. Laajala, Varsha Sreekanth, Alex C. Soupir, Jordan H. Creed, Anni S. Halkola, Federico C. F. Calboli, Kalaimathy Singaravelu, Michael V. Orman, Christelle Colin, Travis A. Gerke, Brooke L. Fridley, Svitlana Tyekucheva, James C. Costello

curatedPCaData harmonizes prostate cancer datasets to enable meta-analysis and clinical insights across multiple cohorts.

Scientific Data · Jul 5, 2023Peer-reviewedBioinformatics

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

Anni S. Halkola, Kaisa Joki, Tuomas K. Mirtti, Marko M. Mäkelä, Tero A. Aittokallio, Teemu D. Laajala

OSCAR uses an L0-pseudonorm approach to select sparse prognostic features in prostate cancer, balancing model accuracy and clinical implementation costs.

PLoS Computational Biology · Mar 10, 2023Peer-reviewedBioinformatics