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Markus Heinonen

Showing results (1-10 of 22) with videos related to

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Bioinformatics (Oxford, England)|June 29, 2018
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusionEmmi Jokinen, Markus Heinonen, Harri Lähdesmäki
BMC Bioinformatics|June 6, 2022
Modeling binding specificities of transcription factor pairs with random forestsAnni A Antikainen, Markus Heinonen, Harri Lähdesmäki
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|January 8, 2011
Computing atom mappings for biochemical reactions without subgraph isomorphismMarkus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, et al.
Metabolites|June 25, 2014
Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerIDHuibin Shen, Nicola Zamboni, Markus Heinonen, et al.
Bioinformatics (Oxford, England)|July 21, 2012
Metabolite identification and molecular fingerprint prediction through machine learningMarkus Heinonen, Huibin Shen, Nicola Zamboni, et al.
Plos Computational Biology|March 25, 2021
Predicting recognition between T cell receptors and epitopes with TCRGPEmmi Jokinen, Jani Huuhtanen, Satu Mustjoki, et al.
Biophysical Journal|December 18, 2024
Probabilistic analysis of spatial viscoelastic cues in 3D cell culture using magnetic microrheometryOssi Arasalo, Arttu J Lehtonen, Mari Kielosto, et al.
Bioinformatics (Oxford, England)|September 13, 2019
Bayesian metabolic flux analysis reveals intracellular flux couplingsMarkus Heinonen, Maria Osmala, Henrik Mannerström, et al.
Rapid Communications in Mass Spectrometry : RCM|September 3, 2008
FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric dataMarkus Heinonen, Ari Rantanen, Taneli Mielikäinen, et al.
Biotechnology for Biofuels|June 30, 2016
Genome wide analysis of protein production load in Trichoderma reeseiTiina M Pakula, Heli Nygren, Dorothee Barth, et al.
Pageof 3

Showing results (1-10 of 22) with videos related to

Sort By:
Pageof 3
Bioinformatics (Oxford, England)|June 29, 2018
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusionEmmi Jokinen, Markus Heinonen, Harri Lähdesmäki
BMC Bioinformatics|June 6, 2022
Modeling binding specificities of transcription factor pairs with random forestsAnni A Antikainen, Markus Heinonen, Harri Lähdesmäki
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|January 8, 2011
Computing atom mappings for biochemical reactions without subgraph isomorphismMarkus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, et al.
Metabolites|June 25, 2014
Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerIDHuibin Shen, Nicola Zamboni, Markus Heinonen, et al.
Bioinformatics (Oxford, England)|July 21, 2012
Metabolite identification and molecular fingerprint prediction through machine learningMarkus Heinonen, Huibin Shen, Nicola Zamboni, et al.
Plos Computational Biology|March 25, 2021
Predicting recognition between T cell receptors and epitopes with TCRGPEmmi Jokinen, Jani Huuhtanen, Satu Mustjoki, et al.
Biophysical Journal|December 18, 2024
Probabilistic analysis of spatial viscoelastic cues in 3D cell culture using magnetic microrheometryOssi Arasalo, Arttu J Lehtonen, Mari Kielosto, et al.
Bioinformatics (Oxford, England)|September 13, 2019
Bayesian metabolic flux analysis reveals intracellular flux couplingsMarkus Heinonen, Maria Osmala, Henrik Mannerström, et al.
Rapid Communications in Mass Spectrometry : RCM|September 3, 2008
FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric dataMarkus Heinonen, Ari Rantanen, Taneli Mielikäinen, et al.
Biotechnology for Biofuels|June 30, 2016
Genome wide analysis of protein production load in Trichoderma reeseiTiina M Pakula, Heli Nygren, Dorothee Barth, et al.
Pageof 3