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Den digitale revolution:fortællinger fra datalogiens verden

• Authors : Naur, Peter; Vinter, Brian; Hansen, Klaus ...

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfScience; Det Natur- og Biovidenskabelige Fakultet; Datalogisk Institut

• Source: Naur , P , Vinter , B , Hansen , K , Mogensen , T Æ , Erleben , K , Pisinger , D , Nielsen , M , Kringelbach , M , Pedersen , E W , Blume , P , Helles , R , Andersen , T O (red.) , Bansler , J P

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Greedy vs. L1 convex optimization in sparse coding:comparative study in abnormal event detection

• Authors : Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfScience; Faculty of Science; Machine learning

• Source: Ren , H , Pan , H , Olsen , S I & Moeslund , T B 2015 , ' Greedy vs. L1 convex optimization in sparse coding : comparative study in abnormal event detection ' , Journal of

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Bridging a translational gap:Using machine learning to improve the prediction of PTSD

Subjects: Early prediction; Machine learning; Markov boundary feature selection

• Source: and For members of the Jerusalem Trauma Outreach and Prevention Study (J-TOPS) group 2015 , ' Bridging a translational gap : Using machine learning to improve the

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Unsupervised behaviour-specific dictionary learning for abnormal event detection

• Authors : Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor ...

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfScience; Faculty of Science; Image analysis

• Source: Ren , H , Liu , W , Olsen , S I , Escalera , S & Moeslund , T B 2015 , Unsupervised behaviour-specific dictionary learning for abnormal event detection . in X Xie , M W

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A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

• Authors : Joshi, Neha; Baumann, Matthias; Ehammer, Andrea

Subjects: optical; synthetic aperture radar; meta-analysis

• Source: Joshi , N , Baumann , M , Ehammer , A , Fensholt , R , Grogan , K , Hostert , P , Jepsen , M R , Kuemmerle , T , Meyfroidt , P , Mitchard , E T A , Reiche , J , Ryan , C M & Waske , B 2016 , ' A

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Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

• Authors : Bak, N; Ebdrup, B H; Oranje, B

Subjects: Adult; Algorithms; Antipsychotic Agents

• Source: Bak , N , Ebdrup , B H , Oranje , B , Fagerlund , B , Jensen , M H , Düring , S W , Nielsen , M Ø , Glenthøj , B Y & Hansen , L K 2017 , ' Two subgroups of antipsychotic-naive, first-episode

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Big universe, big data:machine learning and image analysis for astronomy

• Authors : Kremer, Jan; Stensbo-Smidt, Kristoffer; Gieseke, Fabian Cristian

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfScience; Faculty of Science; Big Data

• Source: Kremer , J , Stensbo-Smidt , K , Gieseke , F C , Pedersen , K S & Igel , C 2017 , ' Big universe, big data : machine learning and image analysis for astronomy ' , IEEE

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DotAligner:Identification and clustering of RNA structure motifs

• Authors : Smith, Martin A.; Seemann, Stefan E.; Quek, Xiu Cheng

Subjects: Functional genome annotation; Functions of RNA structures; Machine learning

• Source: Smith , M A , Seemann , S E , Quek , X C & Mattick , J S 2017 , ' DotAligner : Identification and clustering of RNA structure motifs ' , Genome Biology , vol. 18 , no. 1 , 244 .

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RED-ML:a novel, effective RNA editing detection method based on machine learning

• Authors : Xiong, Heng; Liu, Dongbing; Li, Qiye

Subjects: A-to-I editing; machine learning; posttranscriptional modification

• Source: Xiong , H , Liu , D , Li , Q , Lei , M , Xu , L , Wu , L , Wang , Z , Ren , S , Li , W , Xia , M , Lu , L , Lu , H , Hou , Y , Zhu , S , Liu , X , Sun , Y , Wang , J , Yang , H , Wu , K , Xu , X & Lee

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Footprints of antigen processing boost MHC class II natural ligand predictions

• Authors : Barra, Carolina; Alvarez, Bruno; Paul, Sinu

Subjects: Antigen processing; Binding predictions; Eluted ligands

• Source: Barra , C , Alvarez , B , Paul , S , Sette , A , Peters , B , Andreatta , M , Buus , S & Nielsen , M 2018 , ' Footprints of antigen processing boost MHC class II natural ligand predictions ' , Genome

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Pseudomonas aeruginosa transcriptome during human infection

• Authors : Cornforth, Daniel M.; Dees, Justine L.; Ibberson, Carolyn B.

Subjects: Chronic wounds; Cystic fibrosis; Human transcriptome

• Source: Cornforth , D M , Dees , J L , Ibberson , C B , Huse , H K , Mathiesen , I H , Kirketerp-Møller , K , Wolcott , R D , Rumbaugh , K P , Bjarnsholt , T & Whiteley , M 2018 , ' Pseudomonas aeruginosa

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Population specific biomarkers of human aging:A big data study using South Korean, Canadian, and Eastern European patient populations

• Authors : Mamoshina, Polina; Kochetov, Kirill; Putin, Evgeny

Subjects: Biochemistry aging clocks; Biological age; Deep Learning

• Source: Mamoshina , P , Kochetov , K , Putin , E , Cortese , F , Aliper , A , Lee , W S , Ahn , S M , Uhn , L , Skjodt , N , Kovalchuk , O , Scheibye-Knudsen , M & Zhavoronkov , A 2018 , ' Population specific

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Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

• Authors : Blomberg, Stig Nikolaj; Folke, Fredrik; Ersbøll, Annette Kjær

Subjects: Artificial intelligence; Cardiopulmonary resuscitation; Detection time

• Source: Blomberg , S N , Folke , F , Ersbøll , A K , Christensen , H C , Torp-Pedersen , C , Sayre , M R , Counts , C R & Lippert , F K 2019 , ' Machine learning as a

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Enlarged perivascular spaces in brain MRI:Automated quantification in four regions

• Authors : Dubost, Florian; Yilmaz, Pinar; Adams, Hieab

Subjects: Deep learning; Dementia; Enlarged perivascular spaces

• Source: Dubost , F , Yilmaz , P , Adams , H , Bortsova , G , Ikram , M A , Niessen , W , Vernooij , M & de Bruijne , M 2019 , ' Enlarged perivascular spaces in brain MRI : Automated quantification in four

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Block Forests:random forests for blocks of clinical and omics covariate data

• Authors : Hornung, Roman; Wright, Marvin N.

Subjects: Multi-omics data; Prediction; Random forest

• Source: Hornung , R & Wright , M N 2019 , ' Block Forests : random forests for blocks of clinical and omics covariate data ' , BMC Bioinformatics , vol. 20 , 358 . https://doi.org/10.1186/s12859-019-2942-y

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Adaptivity in Bayesian inverse finite element problems:Learning and simultaneous control of discretisation and sampling errors

• Authors : Kerfriden, Pierre; Kundu, Abhishek; Claus, Susanne

Subjects: Bayesian statistics; Data-driven modelling; Error estimation

• Source: Kerfriden , P , Kundu , A & Claus , S 2019 , ' Adaptivity in Bayesian inverse finite element problems : Learning and simultaneous control of discretisation and sampling

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BioReader:a text mining tool for performing classification of biomedical literature

• Authors : Simon, Christian; Davidsen, Kristian; Hansen, Christina

Subjects: Database curation; Text mining; Machine learning

• Source: Simon , C , Davidsen , K , Hansen , C , Seymour , E , Barnkob , M B & Olsen , L R 2019 , ' BioReader : a text mining tool for performing classification of biomedical literature ' , BMC Bioinformatics

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Neuroimaging markers of global cognition in early Alzheimer's disease:A magnetic resonance imaging-electroencephalography study

• Authors : Waser, Markus; Benke, Thomas; Dal-Bianco, Peter

Subjects: Aged; 80 and over; Alzheimer Disease/diagnostic imaging

• Source: Waser , M , Benke , T , Dal-Bianco , P , Garn , H , Mosbacher , J A , Ransmayr , G , Schmidt , R , Seiler , S , Sorensen , H B D & Jennum , P J 2019 , ' Neuroimaging markers of global cognition in

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Maskinlæring som politologisk værktøj ; Machine learning as a political science tool

• Authors : Hjorth, Frederik Georg; Bach, Alexander; Svejgaard, Jesper

Subjects: /dk/atira/pure/core/keywords/FacultyOfSocialSciences; Det Samfundsvidenskabelige Fakultet; Machine learning

• Source: Hjorth , F G , Bach , A & Svejgaard , J 2019 , ' Maskinlæring som politologisk værktøj ' , Politica , bind 51 , nr. 2 , s. 168-186 . <

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AI in the Health & Life Sciences & the Medicus L(ex) Machina:Setterwalls Life Sciences Report (2019)

• Authors : Minssen, Timo

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfLaw; Faculty of Law; law

• Source: Minssen , T 2019 , AI in the Health & Life Sciences & the Medicus L(ex) Machina : Setterwalls Life Sciences Report (2019) . Setterwalls (Law Firm) . <

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Battling Antibiotic Resistance:Can Machine Learning Improve Prescribing?

• Authors : Ribers, Michael A.; Ullrich, Hannes

Subjects: /dk/atira/pure/core/keywords/FacultyOfSocialSciences; Faculty of Social Sciences; antibiotic prescribing

• Source: Ribers , M A & Ullrich , H 2019 ' Battling Antibiotic Resistance : Can Machine Learning Improve Prescribing? ' . https://doi.org/10.2139/ssrn.3392196

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Peers know you:A feasibility study of the predictive value of peEr's observations to estimate human states

• Authors : Berrocal, Allan; Wac, Katarzyna

Subjects: Ecological momentary assessment; Machine learning; Peerceived momentary assessment

• Source: Berrocal , A & Wac , K 2020 , ' Peers know you : A feasibility study of the predictive value of peEr's observations to estimate human states ' , Procedia Computer Science , vol. 175 , pp. 205-213 .

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Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments:Hierarchical Bayesian Approach

• Authors : Busk, Jonas; Faurholt-Jepsen, Maria; Frost, Mads

Subjects: bipolar disorder; mood; early medical intervention

• Source: Busk , J , Faurholt-Jepsen , M , Frost , M , Bardram , J E , Kessing , L V & Winther , O 2020 , ' Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments : Hierarchical Bayesian Approach

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Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

• Authors : Gravesteijn, Benjamin Y.; Nieboer, Daan; Ercole, Ari

Subjects: Cohort study; Data science; Machine learning

• Source: Gravesteijn , B Y , Nieboer , D , Ercole , A , Lingsma , H F , Nelson , D , van Calster , B , Steyerberg , E W , Åkerlund , C , Amrein , K , Andelic , N , Andreassen , L , Anke , A , Antoni , A ,

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Empathetic application of machine learning may address appropriate utilization of ART

• Authors : Jenkins, Julian; van der Poel, Sheryl; Krüssel, Jan

Subjects: ART utilization; Artificial intelligence; IVF drop-outs

• Source: Jenkins , J , van der Poel , S , Krüssel , J , Bosch , E , Nelson , S M , Pinborg , A & Yao , M M W 2020 , ' Empathetic application of machine learning may address

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Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

• Authors : Johnsson, Jesper; Björnsson, Ola; Andersson, Peder

Subjects: Artificial intelligence; Artificial neural networks; Cerebral performance category

• Source: Johnsson , J , Björnsson , O , Andersson , P , Jakobsson , A , Cronberg , T , Lilja , G , Friberg , H , Hassager , C , Kjaergard , J , Wise , M , Nielsen , N & Frigyesi , A 2020 , ' Artificial neural

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Applicability of an automated model and parameter selection in the prediction of screening-level PTSD in Danish soldiers following deployment:Development study of transferable predictive models using automated machine learning

• Authors : Karstoft, Karen Inge; Tsamardinos, Ioannis; Eskelund, Kasper

Subjects: Decision support; Machine learning; Mental health

• Source: Karstoft , K I , Tsamardinos , I , Eskelund , K , Andersen , S B & Nissen , L R 2020 , ' Applicability of an automated model and parameter selection in the prediction of screening-level PTSD in Danish

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Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women

• Authors : Liang, Liang; Rasmussen, Marie Louise Hee; Piening, Brian

Subjects: delivery prediction; gestational age; human pregnancy

• Source: Liang , L , Rasmussen , M L H , Piening , B , Shen , X , Chen , S , Röst , H , Snyder , J K , Tibshirani , R , Skotte , L , Lee , N CY , Contrepois , K , Feenstra , B , Zackriah , H , Snyder , M &

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Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

• Authors : Manchia, Mirko; Vieta, Eduard; Smeland, Olav B.

Subjects: Machine learning; Open science; Philanthropy

• Source: Manchia , M , Vieta , E , Smeland , O B , Altimus , C , Bechdolf , A , Bellivier , F , Bergink , V , Fagiolini , A , Geddes , J R , Hajek , T , Henry , C , Kupka , R , Lagerberg , T V , Licht , R W ,

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New methodologies in ageing research

• Authors : Osborne, Brenna; Bakula, Daniela; Ben Ezra, Michael

Subjects: Ageing; High-throughput methods; Machine learning

• Source: Osborne , B , Bakula , D , Ben Ezra , M , Dresen , C , Hartmann , E , Kristensen , S M , Mkrtchyan , G V , Nielsen , M H , Petr , M A & Scheibye-Knudsen , M 2020 , ' New methodologies in ageing

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Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson's disease using electrophysiological measurements of visual processing

• Authors : Østergaard, Freja Gam; Himmelberg, Marc M; Laursen, Bettina

Subjects: Animals; Biomarkers; Dependovirus/genetics

• Source: Østergaard , F G , Himmelberg , M M , Laursen , B , Siebner , H R , Wade , A R & Christensen , K V 2020 , ' Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of

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Classification of renal tumour using convolutional neural networks to detect oncocytoma

• Authors : Pedersen, Mikkel; Andersen, Michael Brun; Christiansen, Henning

Subjects: Deep learning; Machine learning; Oncocytoma

• Source: Pedersen , M , Andersen , M B , Christiansen , H & Azawi , N H 2020 , ' Classification of renal tumour using convolutional neural networks to detect oncocytoma ' , European Journal of Radiology , vol.

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Artificial Intelligence in Pharmacoepidemiology:A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence

• Authors : Sessa, Maurizio; Khan, Abdul Rauf; Liang, David

Subjects: artificial intelligence; deep learning; machine learning

• Source: Sessa , M , Khan , A R , Liang , D , Andersen , M & Kulahci , M 2020 , ' Artificial Intelligence in Pharmacoepidemiology : A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in

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Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

• Authors : Ukkonen, Peter; Pincus, Robert; Hogan, Robin J.

Subjects: machine learning; radiation; atmospheric model

• Source: Ukkonen , P , Pincus , R , Hogan , R J , Pagh Nielsen , K & Kaas , E 2020 , ' Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and

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Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

• Authors : Wendorff, Mareike; Garcia Alvarez, Heli M.; Østerbye, Thomas

Subjects: antigen presentation; high-throughput; HLA

• Source: Wendorff , M , Garcia Alvarez , H M , Østerbye , T , ElAbd , H , Rosati , E , Degenhardt , F , Buus , S , Franke , A & Nielsen , M 2020 , ' Unbiased Characterization of Peptide-HLA Class II

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Predicting Fine Particulate Matter (PM2.5) in the Greater London Area:An Ensemble Approach using Machine Learning Methods

• Authors : Yazdi, Mahdieh Danesh; Kuang, Zheng; Dimakopoulou, Konstantina

Subjects: air pollution; particulate matter; machine learning

• Source: Yazdi , M D , Kuang , Z , Dimakopoulou , K , Barratt , B , Suel , E , Amini , H , Lyapustin , A , Katsouyanni , K & Schwartz , J 2020 , ' Predicting Fine Particulate Matter (PM2.5) in the Greater

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Wheel rutting: preliminary investigations of soil redox potential and automated monitoring of their presence using machine learning and highresolution LiDAR data

• Authors : Callesen, Ingeborg; Brockmann, Bo; Fischer, Lene

Subjects: /dk/atira/pure/core/keywords/TheFacultyOfScience; Faculty of Science; forest operations

• Source: Callesen , I , Brockmann , B , Fischer , L , Magnussen , A & Dam , E B 2020 , Wheel rutting: preliminary investigations of soil redox potential and automated monitoring of their presence using

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Bandit-based relay selection in cooperative networks over unknown stationary channels

• Authors : Nomikos, Nikolaos; Talebi, Sadegh; Wichman, Risto

Subjects: Machine learning; Multi-armed bandits; Relay selection

• Source: Nomikos , N , Talebi , S , Wichman , R & Charalambous , T 2020 , Bandit-based relay selection in cooperative networks over unknown stationary channels . in Proceedings of the 2020 IEEE 30th

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Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

• Authors : Andersson, Peder; Johnsson, Jesper; Björnsson, Ola

Subjects: Artificial intelligence; Artificial neural networks; Cardiac arrest

• Source: Andersson , P , Johnsson , J , Björnsson , O , Cronberg , T , Hassager , C , Zetterberg , H , Stammet , P , Undén , J , Kjaergaard , J , Friberg , H , Blennow , K , Lilja , G , Wise , M P ,

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Classification of nemoral forests with fusion of multi-temporal sentinel-1 and 2 data

• Authors : Bjerreskov, Kristian Skau; Nord-Larsen, Thomas; Fensholt, Rasmus

Subjects: Forest and tree species distribution; Forest resources; Machine learning

• Source: Bjerreskov , K S , Nord-Larsen , T & Fensholt , R 2021 , ' Classification of nemoral forests with fusion of multi-temporal sentinel-1 and 2 data ' , Remote Sensing , vol. 13 , no. 5 , 950 , pp. 1-19 .

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Detecting adaptive introgression in human evolution using convolutional neural networks

• Authors : Gower, Graham; Iáñez Picazo, Pablo; Fumagalli, Matteo

Subjects: adaptive introgression; computational biology; genetics

• Source: Gower , G , Iáñez Picazo , P , Fumagalli , M & Racimo , F 2021 , ' Detecting adaptive introgression in human evolution using convolutional neural networks ' , eLife , vol. 10 , e64669 .

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MultiPep:a hierarchical deep learning approach for multi-label classification of peptide bioactivities

• Authors : Gronning, Alexander G. B.; Kacprowski, Tim; Scheele, Camilla

Subjects: peptide bioactivity prediction; peptide therapeutics; deep learning

• Source: Gronning , A G B , Kacprowski , T & Scheele , C 2021 , ' MultiPep : a hierarchical deep learning approach for multi-label classification of peptide bioactivities ' ,

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FOWD:A Free Ocean Wave Dataset for Data Mining and Machine Learning

• Authors : Hafner, Dion; Gemmrich, Johannes; Jochum, Markus

Subjects: Wave properties; Waves; oceanic

• Source: Hafner , D , Gemmrich , J & Jochum , M 2021 , ' FOWD : A Free Ocean Wave Dataset for Data Mining and Machine Learning ' , Journal of Atmospheric and Oceanic Technology ,

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Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer

• Authors : He, Liye; Bulanova, Daria; Oikkonen, Jaana

Subjects: combination synergy; drug combinations; machine learning

• Source: He , L , Bulanova , D , Oikkonen , J , Häkkinen , A , Zhang , K , Zheng , S , Wang , W , Erkan , E P , Carpén , O , Joutsiniemi , T , Hietanen , S , Hynninen , J , Huhtinen , K , Hautaniemi , S ,

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Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

• Authors : Hollenstein, Nora; Renggli, Cedric; Glaus, Benjamin

Subjects: brain activity; EEG; frequency bands

• Source: Hollenstein , N , Renggli , C , Glaus , B , Barrett , M , Troendle , M , Langer , N & Zhang , C 2021 , ' Decoding EEG Brain Activity for Multi-Modal Natural Language Processing ' , Frontiers in Human

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Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation

• Authors : Ingham, Anna Cäcilia; Kielsen, Katrine; Mordhorst, Hanne

Subjects: Acute GvHD; Amplicon sequence variants; Antibiotics

• Source: Ingham , A C , Kielsen , K , Mordhorst , H , Ifversen , M , Aarestrup , F M , Müller , K G & Pamp , S J 2021 , ' Microbiota long-term dynamics and prediction of acute graft-versus-host disease in

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A Review of Domain Adaptation without Target Labels

• Authors : Kouw, Wouter M.; Loog, Marco

Subjects: covariate shift; domain adaptation; Machine learning

• Source: Kouw , W M & Loog , M 2021 , ' A Review of Domain Adaptation without Target Labels ' , IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43 , no. 3 ,

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On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

• Authors : Lindorff-Larsen, Kresten; Kragelund, Birthe B.

Subjects: condensate; intrinsically disordered protein; machine learning

• Source: Lindorff-Larsen , K & Kragelund , B B 2021 , ' On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of

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Should We Embed in Chemistry?:A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

• Authors : Lovric, Mario; Duricic, Tomislav; Tran, Han T. N.

Subjects: manifold learning; machine learning; rdkit

• Source: Lovric , M , Duricic , T , Tran , H T N , Hussain , H , Lacic , E , Rasmussen , M A & Kern , R 2021 , ' Should We Embed in Chemistry? A Comparison of Unsupervised Transfer

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Clinician preimplementation perspectives of a decision-support tool for the prediction of cardiac arrhythmia based on machine learning:near-live feasibility and qualitative study

• Authors : Matthiesen, Stina; Diederichsen, Søren Zöga; Hansen, Mikkel Klitzing Hartmann

Subjects: Artificial intelligence; Cardiac arrhythmia; Clinical decision support systems

• Source: Matthiesen , S , Diederichsen , S Z , Hansen , M K H , Villumsen , C , Lassen , M C H , Jacobsen , P K , Risum , N , Winkel , B G , Philbert , B T , Svendsen , J H & Andersen , T O 2021 , ' Clinician

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