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Interpreting biolog...
Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
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- Hartman, Erik (författare)
- Lund University
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- Scott, Aaron M (författare)
- Lund University,Lunds universitet,Infection Medicine Proteomics,Forskargrupper vid Lunds universitet,epIgG,Lund University Research Groups
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- Karlsson, Christofer (författare)
- Lund University,Lunds universitet,Infection Medicine Proteomics,Forskargrupper vid Lunds universitet,Lund University Research Groups
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- Mohanty, Tirthankar (författare)
- Lund University,Lunds universitet,Translationell Sepsisforskning,Forskargrupper vid Lunds universitet,Translational Sepsis research,Lund University Research Groups
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- Vaara, Suvi T (författare)
- University of Helsinki
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- Linder, Adam (författare)
- Lund University,Lunds universitet,Translationell Sepsisforskning,Forskargrupper vid Lunds universitet,SEBRA Sepsis and Bacterial Resistance Alliance,Heparinbindande protein inom thoraxkirurgi,Translational Sepsis research,Lund University Research Groups,Heparin bindning protein in cardiothoracic surgery
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- Malmström, Lars (författare)
- Lund University,Lunds universitet,epIgG,Forskargrupper vid Lunds universitet,Lund University Research Groups
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- Malmström, Johan (författare)
- Lund University,Lunds universitet,Infection Medicine Proteomics,Forskargrupper vid Lunds universitet,SEBRA Sepsis and Bacterial Resistance Alliance,epIgG,BioMS,LTH profilområde: Teknik för hälsa,LTH profilområden,Lunds Tekniska Högskola,Lund University Research Groups,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Nature Communications. - 2041-1723. ; 14, s. 1-13
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Humans
- COVID-19
- Proteomics
- Neural Networks, Computer
- Algorithms
- Acute Kidney Injury
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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