Antti Honkela

Publications

Research output per year 2007 2011 2015 2016 2021 2023

• 31 Article
• 9 Conference contribution
• 5 Chapter
• 1 Poster
• 2 More
  • 1 Meeting Abstract
  • 1 Conference article

Research output per year

Research output per year

Search results

• 2023

  Differentially private partitioned variational inference

  Heikkilä, M., Ashman, M., Swaroop, S., Turner, R. E. & Honkela, A., 2023, In: Transactions on machine learning research. 2023, 4, 33 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Digital public health leadership in the global fight for health security

  AlKnawy, B., Kozlakidis, Z., Tarkoma, S., Bates, D., Honkela, A., Crooks, G., Rhee, K. & McKillop, M., Feb 2023, In: BMJ global health. 8, 2, 10 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Individual Privacy Accounting with Gaussian Differential Privacy

  Koskela, A., Tobaben, M. & Honkela, A., 2023. 25 p.

  Research output: Conference materials › Poster › peer-review

  Open Access

  File
• 2022

  d3p - A Python Package for Differentially-Private Probabilistic Programming

  Prediger, L., Loppi, N., Kaski, S. & Honkela, A., 2022, In: Proceedings on Privacy Enhancing Technologies. 2022, 2, p. 407-425

  Research output: Contribution to journal › Conference article › Scientific › peer-review

  Open Access

  File

• Strong pathogen competition in neonatal gut colonisation

  Mäklin, T., Thorpe, H. A., Pöntinen, A. K., Gladstone, R. A., Shao, Y., Pesonen, M., McNally, A., Johnsen, P. J., Samuelsen, Ø., Lawley, T. D., Honkela, A. & Corander, J., 1 Dec 2022, In: Nature Communications. 13, 7417.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Tekoälystä tietoturvallista

  Honkela, A., 2022, Tekoälyratkaisut tänään ja tulevaisuudessa. Eduskunnan tulevaisuusvaliokunta, p. 121-127 (Eduskunnan tulevaisuusvaliokunnan julkaisuja).

  Research output: Chapter in Book/Report/Conference proceeding › Chapter › General public

  Open Access

  File
• 2021

  A LONGITUDINAL GENOMIC AND FUNCTIONAL ANALYSIS OF CLOSTRIDIUM BOTULINUM ISOLATED FROM AN INFANT BOTULISM CASE

  Douillard, F. P., Derman, Y., Woudstra, C., Selby, K., Mäklin, T., Honkela, A., Korkeala, H. & Lindstrom, M., Jan 2021, In: Toxicon. 190, p. S17-S17 1 p.

  Research output: Contribution to journal › Meeting Abstract › peer-review

• Bacterial Genomic Epidemiology with Mixed Samples

  Mäklin, T., Kallonen, T., Alanko, J., Samuelsen, Ø., Hegstad, K., Mäkinen, V., Corander, J., Heinz, E. & Honkela, A., 15 Nov 2021, In: Microbial Genomics. 7, 11, 18 p., 000691.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Closing Clostridium botulinum Group III Genomes Using Long-Read Sequencing

  Woudstra, C., Mäklin, T., Derman, Y., Bano, L., Skarin, H., Mazuet, C., Honkela, A. & Lindström, M., Jun 2021, In: Microbiology Resource Announcements. 10, 22, 4 p., e01364-20.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Differentially Private Bayesian Inference for Generalized Linear Models

  Kulkarni, T., Jälkö, J., Koskela, A., Kaski, S. & Honkela, A., 2021, Proceedings of the 38th International Conference on Machine Learning, PMLR. Meila, M. & Zhang, T. (eds.). Journal of Machine Learning Research, p. 5838-5849 12 p. (Proceedings of Machine Learning Research; vol. 139).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File

• High-resolution sweep metagenomics using fast probabilistic inference

  Mäklin, T., Kallonen, T., David, S., Boinett, C. J., Pascoe, B., Méric, G., Aanensen, D. M., Feil, E. J., Baker, S., Parkhill, J., Sheppard, S. K., Corander, J. & Honkela, A., 8 Oct 2021, In: Wellcome open research. 5, 14, 28 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Privacy-preserving data sharing via probabilistic modeling

  Jalko, J., Lagerspetz, E., Haukka, J., Tarkoma, S., Honkela, A. & Kaski, S., 9 Jul 2021, In: Patterns. 2, 7, 10 p., 100271.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

  Koskela, A., Jalko, J., Prediger, L. & Honkela, A., 2021, 24th International Conference on Artificial Intelligence and Statistics (AISTATS). Banerjee, A. & Fukumizu, K. (eds.). Microtome Publishing, 10 p. (Proceedings of Machine Learning Research; vol. 130).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File
• 2020

  Computing Tight Differential Privacy Guarantees Using FFT

  Koskela, A., Jälkö, J. & Honkela, A., Jun 2020, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. AISTATS, p. 2560-2569 10 p. (Proceedings of Machine Learning Research; vol. 108).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File

• Learning Rate Adaptation for Differentially Private Learning

  Koskela, A. & Honkela, A., Jun 2020, Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics. AISTATS, p. 2465-2475 10 p. (Proceedings of Machine Learning Research; vol. 108).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File
• 2019

  Differentially private Markov chain Monte Carlo

  Heikkilä, M., Jälkö, J., Dikmen, O. & Honkela, A., 2019, Advances in Neural Information Processing Systems 32. Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E. & Garnett, R. (eds.). Maryland Heights, MO: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), 11 p. (Advances in Neural Information Processing Systems; vol. 32).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File

• Representation transfer for differentially private drug sensitivity prediction

  Niinimäki, T., Heikkilä, M. A., Honkela, A. & Kaski, S., 5 Jul 2019, In: Bioinformatics. 35, 14, p. i218-i224 7 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Seasonal Variation in Genome-Wide DNA Methylation Patterns and the Onset of Seasonal Timing of Reproduction in Great Tits

  Viitaniemi, H. M., Verhagen, I., Visser, M. E., Honkela, A., van Oers, K. & Husby, A., Mar 2019, In: Genome Biology and Evolution. 11, 3, p. 970-983 14 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2018

  Efficient differentially private learning improves drug sensitivity prediction

  Honkela, A., Das, M., Nieminen, A., Dikmen, O. & Kaski, S., 6 Feb 2018, In: Biology Direct. 13, 12 p., 1.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• GPrank: an R package for detecting dynamic elements from genome-wide time series

  Topa, H. & Honkela, A., 4 Oct 2018, In: BMC Bioinformatics. 19, 6 p., 367.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Identifying Bacterial Strains from Sequencing Data

  Mäklin, T., Corander, J. & Honkela, A., 2018, Data Mining for Systems Biology: Methods and Protocols. Mamitsuka, H. (ed.). 2 ed. New York, NY: Humana press, p. 1-7 7 p. (Methods in Molecular Biology; vol. 1807).

  Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review
• 2017

  Differentially private Bayesian learning on distributed data

  Heikkila, M., Lagerspetz, E., Kaski, S., Shimizu, K., Tarkoma, S. & Honkela, A., 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R. (eds.). NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), Vol. 30. 10 p. (Advances in Neural Information Processing Systems; vol. 30).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File

• Differentially Private Variational Inference for Non-conjugate Models

  Jälkö, J., Dikmen, O. & Honkela, A., 2017, Uncertainty in Artificial Intelligence 2017: Proceedings of the 33rd Conference, UAI 2017 . Elidan, G. & Kersting, K. (eds.). The Association for Uncertainty in Artificial Intelligence, 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

  Open Access

  File

• Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

  Dzida, T., Iqbal, M., Charapitsa, I., Reid, G., Stunnenberg, H., Matarese, F., Grote, K., Honkela, A. & Rattray, M., 28 Sep 2017, In: PeerJ. 5, 23 p., 3742.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2016

  Analysis of differential splicing suggests different modes of short-term splicing regulation

  Topa, H. & Honkela, A., 15 Jun 2016, In: Bioinformatics. 32, 12, p. 147-155 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Bayesian identification of bacterial strains from sequencing data

  Sankar, A., Malone, B. M., Bayliss, S. C., Pascoe, B., Méric, G., Hitchings, M. D., Sheppard, S. K., Feil, E. J., Corander, J. I. & Honkela, A. J. H., 25 Aug 2016, In: Microbial Genomics. 2, 8, 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• On the inconsistency of ℓ1-penalised sparse precision matrix estimation

  Heinävaara, O., Leppä-Aho, J., Corander, J. & Honkela, A., 13 Dec 2016, In: BMC Bioinformatics. 17, Suppl 16, p. 99-107 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• RNA Production Delays Shape Transcriptional Response Dynamics

  Honkela, A. J. H. & Rattray, M., 25 May 2016, In: Cell Systems. 2, 5, p. 291 1 p.

  Research output: Contribution to journal › Article › Scientific

  Open Access

• Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

  Lees, J. A., Vehkala, M., Välimäki, N., Harris, S. R., Chewapreecha, C., Croucher, N. J., Marttinen, P., Davies, M. R., Steer, A. C., Tong, S. Y. C., Honkela, A., Parkhill, J., Bentley, S. & Corander, J., 16 Sep 2016, In: Nature Communications. 7, 8 p., 12797.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2015

  Fast and accurate approximate inference of transcript expression from RNA-seq data

  Hensman, J., Papastamoulis, P., Glaus, P., Honkela, A. J. H. & Rattray, M., 15 Dec 2015, In: Bioinformatics. 31, 24, p. 3881-3889 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Gaussian process modelling of multiple short time series

  Topa, H. & Honkela, A. J. H., 2015, ESANN 2015: Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, p. 83-88 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

• Gaussian process test for high-throughput sequencing time series: application to experimental evolution

  Topa, H., Jonas, A., Kofler, R., Kosiol, C. & Honkela, A., 1 Jun 2015, In: Bioinformatics. 31, 11, p. 1762-1770 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

  Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, H. G., Reid, G., Lawrence, N. D. & Rattray, M., 20 Oct 2015, In: Proceedings of the National Academy of Sciences of the United States of America. 112, 42, p. 13115-13120 6 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

  Uziela, K. & Honkela, A., 12 May 2015, In: PLoS One. 10, 5, 18 p., 0126545.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2014

  A community effort to assess and improve drug sensitivity prediction algorithms

  Costello, J. C., Heiser, L. M., Georgii, E., Gonen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., Khan, S. A., Mpindi, J-P., Kallioniemi, O., Honkela, A., Aittokallio, T., Wennerberg, K., Collins, J. J., Gallahan, D., Singer, D., Saez-Rodriguez, J., Kaski, S., & 3 othersGray, J. W., Stolovitzky, G. & NCI DREAM Community, Dec 2014, In: Nature Biotechnology. 32, 12, p. 1202-U57 13 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

• Exploration and retrieval of whole-metagenome sequencing samples

  Seth, S., Valimaki, N., Kaski, S. & Honkela, A., 1 Sept 2014, In: Bioinformatics. 30, 17, p. 2471-2479 9 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

• Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data

  wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, H. G., Reid, G., Lawrence, N. D. & Rattray, M., May 2014, In: PLoS Computational Biology. 10, 5, 17 p., e1003598.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2013

  Mining regulatory network connections by ranking transcription factor target genes using time series expression data

  Honkela, A., Rattray, M. & Lawrence, N. D., 2013, Data Mining for Systems Biology: Methods and Protocols. Mamitsuka, H., DeLisi, C. & Kanehisa, M. (eds.). Humana press, p. 59-67 9 p. (Methods in Molecular Biology; vol. 939).

  Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review
• 2012

  Identifying differentially expressed transcripts from RNA-seq data with biological variation

  Glaus, P., Honkela, A. & Rattray, M., 2012, In: Bioinformatics. 28, 13, p. 1721-1728 8 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File

• Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison

  Titsias, M. K., Honkela, A., Lawrence, N. D. & Rattray, M., 30 May 2012, In: BMC Systems Biology. 6, 21 p., 53.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access

  File
• 2011

  A Generative Approach for Image-Based Modeling of Tumor Growth

  Menze, B. H., Van Leemput, K., Honkela, A., Konukoglu, E., Weber, M-A., Ayache, N. & Golland, P., 2011, Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. Székely, G. & Hahn, H. K. (eds.). Springer-Verlag, p. 735-747 13 p. (Lecture Notes in Computer Science; vol. 6801).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

• Enemmän vai laadukkaammin

  Translated title of the contribution: More quality or quantityHonkela, A., 2011, Kirjeitä nuorelle tutkijalle: kannanottoja tutkijan arjesta. Paso, M. (ed.). Helsinki: Suomalainen Tiedeakatemia, p. 38-42 5 p. (Suomalaisen Tiedeakatemian kannanottoja; vol. 3).

  Research output: Chapter in Book/Report/Conference proceeding › Chapter › General public

• Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation

  Lawrence, N., Rattray, M., Honkela, A. & Titsias, M., 2011, Handbook of Statistical Systems Biology. Stumpf, M. P. H., Balding, D. J. & Girolami, M. (eds.). John Wiley & Sons Ltd. , p. 376-394 19 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review

• Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model

  Remes, U., Palomaki, K. J., Raiko, T., Honkela, A. & Kurimo, M., 2011, In: IEEE Signal Processing Letters. 18, 10, p. 563-566 4 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

• tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor

  Honkela, A., Gao, P., Ropponen, J., Rattray, M. & Lawrence, N. D., 2011, In: Bioinformatics. 27, 7, p. 1026-1027 2 p.

  Research output: Contribution to journal › Article › Scientific › peer-review
• 2010

  Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes

  Honkela, A., Raiko, T., Kuusela, M., Tornio, M. & Karhunen, J., 2010, In: Journal of Machine Learning Research. 11, p. 3235-3268 34 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

• Model-based method for transcription factor target identification with limited data

  Honkela, A., Girardot, C., Gustafson, E. H., Liu, Y-H., Furlong, E. E. M., Lawrence, N. D. & Rattray, M., 2010, In: Proceedings of the National Academy of Sciences of the United States of America. 107, p. 7793-7798 6 p.

  Research output: Contribution to journal › Article › Scientific › peer-review
• 2007

  Compact modeling of data using independent variable group analysis

  Alhoniemi, E., Honkela, A., Lagus, K., Seppä, S., Wagner, P. & Valpola, H., 5 Nov 2007, In: IEEE Transactions on Neural Networks. 18, 6, p. 1762-1776 15 p.

  Research output: Contribution to journal › Article › Scientific › peer-review

  Open Access