Browsing Øvrige samlinger by Title

Now showing items 18240-18259 of 34529

  • Machiavelli på indeks: Ti strategier for å omgå bokforbudet 
    Nordbø, Thorbjørn (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2023)
  • Machine Excellence Tradeoffs to Ethical and Legal Perspectives 
    Tørresen, Jim; Saplacan, Diana; Baselizadeh, Adel; Mahler, Tobias (Chapter / Bokkapittel / AcceptedVersion; Peer reviewed, 2023)
    Abstract—We appreciate well-functioning technology being able to also personalize its services. However, to protect privacy and avoid a potential misuse of personal data, we are encouraged to limit the amount of personal ...
  • Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury 
    Gravesteijn, BY; Nieboer, Daan; Ercole, Ari; Lingsma, Hester F; Nelson, David; Van Calster, Ben; Steyerberg, Ewout W; Andelic, Nada; Anke, Audny; Frisvold, Shirin; Helseth, Eirik; Røe, Cecilie; Røise, Olav; Skandsen, Toril; Vik, Anne (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2020)
    Objective We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting We performed logistic ...
  • Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas 
    van IJzendoorn, David G.P.; Szuhai, Karoly; Briaire-De Bruijn, Inge H; Kostine, Marie; Kuijjer, Marieke Lydia; Bovée, Judith V.M.G. (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2019)
    Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine ...
  • Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls 
    Shemesh, Or; Polak, Pazit; Lundin, Knut Erik Aslaksen; Sollid, Ludvig M.; Yaari, Gur (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2021)
    Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated ...
  • Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database 
    Al-Khdhairawi, Ahmad; Sanuri, Danish; Akbar, Rahmad; Lam, Su Datt; Sugumar, Shobana; Ibrahim, Nazlina; Chieng, Sylvia; Sairi, Fareed (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2023)
    Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to ...
  • Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex 
    Friederich, Pascal; Dos Passos Gomes, Gabriel; De Bin, Riccardo; Aspuru-Guzik, Alán; Balcells, David (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2020)
    Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying ...
  • Machine Learning for Fatigue Detection using Fitbit Fitness Trackers 
    Husom, Erik Johannes; Dautov, Rustem; Nedisan Videsjorden, Adela; Gonidis, Fotis; Papatzelos, Spyridon; Malamas, Nikolaos (Chapter / Bokkapittel / AcceptedVersion; Peer reviewed, 2022)
    Fatigue can be a pre-cursor to many illnesses and injuries, and cause fatal work-related incidents. Fatigue detection has been traditionally performed in lab conditions with stationary medical-grade diagnostics equipment ...
  • Machine learning for intraoperative prediction of viability in ischemic small intestine 
    Strand-Amundsen, Runar James; Tronstad, Christian; Reims, Henrik Mikael; Reinholt, Finn P.; Høgetveit, Jan Olav; Tønnessen, Tor Inge (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2018)
    Objective: Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30-93%) of patients with acute ...
  • Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects 
    Henghes, Ben; Lahav, Ofer; Gerdes, David W.; Lin, H. W.; Morgan, R.; Abbott, T. M. C.; Aguena, M.; Allam, S.; Annis, J.; Avila, S.; Bertin, E.; Brooks, D.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Conselice, C.; Costanzi, M.; Da Costa, L. N.; De Vicente, J.; Desai, S.; Diehl, H. T.; Doel, P.; Everett, S.; Ferrero, Ismael; Frieman, J.; García-Bellido, Juan; Gaztanaga, E.; Gruen, D.; Gruendl, Robert A.; Gschwend, J.; Gutierrez, G.; Hartley, W. G.; Hinton, S. R.; Honscheid, K.; Hoyle, B.; James, D. J.; Kuehn, K.; Kuropatkin, N.; Marshall, Jennifer L.; Melchior, P.; Menanteau, F.; Miquel, R.; Ogando, R. L. C.; Palmese, Antonella; Paz-Chinchón, Francisco; Plazas, Andrés A.; Romer, A. K.; Sánchez, C.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Serrano, S.; Smith, M.; Soares-Santos, M; Suchyta, E.; Tarle, G.; To, C.; Wilkinson, R. D. (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2020)
    In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The ...
  • Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy 
    Cunha, Andrè Bastos da; Hou, Jie; Schuelke, Christin (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2019)
    Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and ...
  • Machine learning initialization to accelerate Stokes profile inversions 
    Gafeira, Ricardo; Orozco Suarez, D.; Milić, Ivan; Quintero Noda, Carlos; Ruiz Cobo, Basilio; Uitenbroek, Han (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2021)
    Context. At present, an exponential growth in scientific data from current and upcoming solar observatories is expected. Most of the data consist of high spatial and temporal resolution cubes of Stokes profiles taken in ...
  • Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq 
    Miller, Jason R.; Adjeroh, Donald A. (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2024)
    Background Parent-of-origin allele-specific gene expression (ASE) can be detected in interspecies hybrids by virtue of RNA sequence variants between the parental haplotypes. ASE is detectable by differential ...
  • Machine learning prediction of quartz forming-environments 
    Wang, Y.; Qiu, K.-F.; Müller, Axel; Hou, Z.-L.; Zhu, Z.-H.; Yu, H.-C. (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2021)
    Trace elements of quartz document the physical-chemical evolutions of quartz growth, which has been a great and most applied tool in the study of geological settings in quartz-forming environments. A classic method is using ...
  • Machine Learning with Domain Knowledge for Predictive Quality Monitoring in Resistance Spot Welding 
    Zhou, Baifan; Pychynski, Tim; Reischl, Markus; Kharlamov, Evgeny; Mikut, Ralf (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2022)
    Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing ...
  • Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data 
    Maros, Máté E.; Capper, David; Jones, David T.; Hovestadt, Volker; von Deimling, Andreas; Pfister, Stefan M; Benner, Axel; Zucknick, Karola Manuela; Sill, Martin (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2020)
    DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates ...
  • Machine Learning, Cognitive Sovereignty and Data Protection Rights with Respect to Automated Decisions 
    Bygrave, Lee Andrew (Chapter / Bokkapittel / PublishedVersion; Peer reviewed, 2022)
    Human behaviour is increasingly governed by automated decisional systems based on machine learning (ML) and ‘Big Data’. While these systems promise a range of benefits, they also throw up a congeries of challenges, not ...
  • Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial 
    Rakaee, Mehrdad; Andersen, S.; Giannikou, K.; Paulsen, Erna-Elise; Kilvær, Thomas Karsten; Rasmussen Busund, Lill-Tove; Berg, Thomas; Richardsen, Elin; Lombardi, Ana Paola; Adib, E.; Pedersen, Mona Irene; Tafavvoghi, Masoud; Wahl, Sissel Gyrid Freim; Petersen, R.H.; Bondgaard, A.L.; Yde, C.W.; Baudet, C.; Licht, P.; Lund-Iversen, Marius; Grønberg, Bjørn Henning; Fjellbirkeland, Lars; Helland, Åslaug; Pøhl, M.; Kwiatkowski, D.J.; Dønnem, Tom (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2023)
    Background We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478 ). Molecular and genomic features associated with immune ...
  • Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study 
    Shakibfar, Saeed; Zhao, Jing; Li, Huiqi; Nordeng, Hedvig Marie Egeland; Lupattelli, Angela; Pavlović, Milena; Sandve, Geir Kjetil Ferkingstad; Nyberg, Fredrik; Wettermark, Björn; Hajiebrahimi, Mohammadhossein; Andersen, Morten; Sessa, Maurizio (Journal article / Tidsskriftartikkel / PublishedVersion; Peer reviewed, 2023)
    Aims To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway. Method We employed linked data from the national health registries of Sweden and ...
  • Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching 
    He, Yuan; Chen, Jiaoyan; Dong, Hang; Jimenez-Ruiz, Ernesto; Horrocks, Ian (Journal article / Tidsskriftartikkel / AcceptedVersion; Peer reviewed, 2022)
    Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) ...