Research output per year
Research output per year
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
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Lu Cheng
- Department of Computer Science - Visitor (Faculty)
- Professorship Vehtari Aki - Visitor (Faculty), Academy Research Fellow
Person: Postdoctoral researchers, Visiting Scholar
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Markus Heinonen
- Department of Computer Science - Academy Research Fellow
- Probabilistic Machine Learning - Academy Research Fellow
- Professorship Lähdesmäki Harri - Academy Research Fellow
Person: Postdoctoral researchers
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Harri Lähdesmäki
- Department of Computer Science - Professor (Associate Professor)
- Helsinki Institute for Information Technology (HIIT)
- Computer Science Professors - Professor (Associate Professor)
- Computer Science - Artificial Intelligence and Machine Learning (AIML) - Research area - Professor (Associate Professor)
- Computer Science - Computational Life Sciences (CSLife) - Research area - Professor (Associate Professor)
- Professorship Lähdesmäki Harri - Professor (Associate Professor)
Person: Professors, tenure track
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Balancing Imbalanced Toxicity Models : Using MolBERT with Focal Loss
Masood, M. A., Kaski, S., Ceulemans, H., Herman, D. & Heinonen, M., 2025, AI in Drug Discovery - 1st International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Proceedings. Clevert, D.-A., Wand, M., Schmidhuber, J., Malinovská, K. & Tetko, I. V. (eds.). Springer, p. 82-97 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14894 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Open AccessFile38 Downloads (Pure) -
Computational Intelligence Methods for Bioinformatics and Biostatistics : 19th International Meeting, CIBB 2024, Benevento, Italy, September 4–6, 2024, Revised Selected Papers
Cerulo, L. (Editor), Napolitano, F. (Editor), Bardozzo, F. (Editor), Cheng, L. (Editor), Annalisa, A. (Editor) & Pagnotta, S. M. (Editor), 2025, Springer. 300 p. (Lecture Notes in Computer Science; vol. 15276)Research output: Book/Report › Anthology › Scientific › peer-review
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Driver gene detection via causal inference on single cell embeddings
Fu, C. & Cheng, L., 2025, Computational Intelligence Methods for Bioinformatics and Biostatistics : 19th International Meeting, CIBB 2024, Benevento, Italy, September 4–6, 2024, Revised Selected Papers. Springer, 6 p. (Lecture Notes in Computer Science; vol. 15276).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Datasets
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Additional file 2 of Modeling binding specificities of transcription factor pairs with random forests
Antikainen, A. A. (Creator), Heinonen, M. (Creator) & Lähdesmäki, H. (Creator), figshare, 6 Jun 2022
DOI: 10.6084/m9.figshare.20003421, https://springernature.figshare.com/articles/dataset/Additional_file_2_of_Modeling_binding_specificities_of_transcription_factor_pairs_with_random_forests/20003421
Dataset
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Additional file 4 of Probabilistic modeling methods for cell-free DNA methylation based cancer classification
Halla-aho, V. (Creator) & Lähdesmäki, H. (Creator), figshare, 2022
DOI: 10.6084/m9.figshare.19518516, https://springernature.figshare.com/articles/dataset/Additional_file_4_of_Probabilistic_modeling_methods_for_cell-free_DNA_methylation_based_cancer_classification/19518516
Dataset
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A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways
Äijö, T. (Creator), Huang, Y.-H. (Contributor), Mannerström, H. (Creator), Chavez, L. (Contributor), Tsagaratou, A. (Contributor), Rao, A. (Contributor) & Lähdesmäki, H. (Creator), figshare, 1 Jan 2016
DOI: 10.6084/m9.figshare.c.3604346.v1, https://figshare.com/collections/A_probabilistic_generative_model_for_quantification_of_DNA_modifications_enables_analysis_of_demethylation_pathways/3604346/1
Dataset
Activities
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A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
Ögretir, M. (Speaker)
14 Dec 2022Activity: Talk or presentation types › Conference presentation
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Faster Bayesian inference in Joint Species Distribution Models
Tikhonov, G. (Speaker), Vanhatalo, J. (Contributor), Ovaskainen, O. (Contributor) & Lähdesmäki, H. (Contributor)
22 Jun 2020 → 26 Jun 2020Activity: Talk or presentation types › Conference presentation
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IEEE Access (Journal)
Timonen, J. (Reviewer)
28 Sept 2020Activity: Publication peer-review and editorial work types › Reviewer of scientific journal
Press/Media
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Research Findings from Aalto University Update Understanding of Machine Learning (EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings)
27/12/2023
1 item of Media coverage
Press/Media: Media appearance
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University of Turku and Abo Akademi University Researchers Target Type 1 Diabetes (Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes)
10/10/2023
1 item of Media coverage
Press/Media: Media appearance
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Research Data from University of Turku and Abo Akademi University Update Understanding of Data Analytics (Permutation-based significance analysis reduces the type 1 error rate in bisulphite sequencing data analysis of human umbilical cord blood ...)
05/10/2023
1 item of Media coverage
Press/Media: Media appearance