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1Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries
C. Espinosa, W. Khan, R. Khanam, S. Das, J. Khalid, J. Pervin, M. Kasaro, K. Contrepois, A. Chang, T. Phongpreecha и 47 other автор(ы). Science Advances, 9 (21): eade7692 (2023)
год назад , @becker
income
myown
country
middle
low
pregnancy
birth
lmic
multiomics
maternal
preterm
ehr
incomemyowncountrymiddlelowpregnancybirthlmicmultiomicsmaternalpretermehr
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1Evidence for human milk as a biological system and recommendations for study design—a report from “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Working Group 4
S. Donovan, N. Aghaeepour, A. Andres, M. Azad, M. Becker, S. Carlson, K. Järvinen, W. Lin, B. Lönnerdal, C. Slupsky и 1 other автор(ы). The American Journal of Clinical Nutrition, (2023)
год назад , @becker
myown
citedby:scholar:timestamp:2023-5-14
citedby:scholar:count:4
milk
human
nutrition
infant
myowncitedby:scholar:timestamp:2023-5-14citedby:scholar:count:4milkhumannutritioninfant
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2Machine learning techniques for accountability
B. Kim, и F. Doshi-Velez. AI Magazine, 42 (1): 47--52 (2021)
год назад , @becker
artificial
responsible
machine
ai
citedby:scholar:count:15
learning
intelligence
ml
citedby:scholar:timestamp:2023-4-10
accountability
artificialresponsiblemachineaicitedby:scholar:count:15learningintelligencemlcitedby:scholar:timestamp:2023-4-10accountability
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1Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, и S. Ji. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2022)
год назад , @becker
artificial
neural
citedby:scholar:timestamp:2023-4-9
gnn
ai
explainable
citedby:scholar:count:203
networks
graph
xai
intelligence
artificialneuralcitedby:scholar:timestamp:2023-4-9gnnaiexplainablecitedby:scholar:count:203networksgraphxaiintelligence
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1Data-driven longitudinal characterization of neonatal health and morbidity
D. Francesco, J. Reiss, J. Roger, A. Tang, A. Chang, M. Becker, T. Phongpreecha, C. Espinosa, S. Morin, E. Berson и 23 other автор(ы). Science Translational Medicine, 15 (683): eadc9854 (2023)
2 лет назад , @becker
myown
project:2023-tracking
data
health
neonatal
paper:fastcor
longitudinal
myownproject:2023-trackingdatahealthneonatalpaper:fastcorlongitudinal
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1Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning
M. Becker, J. Dai, A. Chang, D. Feyaerts, I. Stelzer, M. Zhang, E. Berson, G. Saarunya, D. De Francesco, C. Espinosa и 1 other автор(ы). Frontiers in Pediatrics, (2022)
2 лет назад , @becker
multitask
myown
immune
stress
pregnancy
learning
life
stressors
cell
system
outcomes
machine
adverse
immunology
single
multitaskmyownimmunestresspregnancylearninglifestressorscellsystemoutcomesmachineadverseimmunologysingle
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1Machine Learning Techniques
J. Choi, N. Aghaeepour, и M. Becker. стр. 147--164. Springer International Publishing, Cham, (2022)
2 лет назад , @becker
chapter
myown
book
machine
learning
surgery
chaptermyownbookmachinelearningsurgery
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1Early prediction and longitudinal modeling of preeclampsia from multiomics
I. Marić, K. Contrepois, M. Moufarrej, I. Stelzer, D. Feyaerts, X. Han, A. Tang, N. Stanley, R. Wong, G. Traber и 1 other автор(ы). Patterns, 3 (12): 100655 (2022)
2 лет назад , @becker
myown
modeling
omics
pregnancy
multiomics
preeclampsia
prediction
multi
longitudinal
myownmodelingomicspregnancymultiomicspreeclampsiapredictionmultilongitudinal
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1In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network
R. Fallahzadeh, N. Bidoki, I. Stelzer, M. Becker, I. Marić, A. Chang, A. Culos, T. Phongpreecha, M. Xenochristou, D. De Francesco и 1 other автор(ы). Cytometry Part A, (2022)
2 лет назад , @becker
high
myown
dimensional
neural
encoder
network
single
cell
highmyowndimensionalneuralencodernetworksinglecell
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1Shorter maternal leukocyte telomere length following cesarean birth: Implications for future research
D. Panelli, J. Mayo, R. Wong, M. Becker, I. Maric, E. Wu, I. Gotlib, N. Aghaeepour, M. Druzin, D. Stevenson и 1 other автор(ы). American Journal of Obstetrics & Gynecology, 228 (1): S456--S457 (2023)
2 лет назад , @becker
myown
pregnancy
telomere
birth
length
cesarean
myownpregnancytelomerebirthlengthcesarean
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3Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, и A. Ho. arXiv preprint arXiv:2211.04325, (2022)
2 лет назад , @becker
set
models
deep
large
sets
data
learning
requirement
datasets
dataset
machine
limit
model
themis
setmodelsdeeplargesetsdatalearningrequirementdatasetsdatasetmachinelimitmodelthemis
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2Image segmentation using deep learning: A survey
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, и D. Terzopoulos. IEEE transactions on pattern analysis and machine intelligence, (2021)
2 лет назад , @becker
image
citedby:scholar:timestamp:2023-1-5
hecodex
review
segmentation
survey
citedby:scholar:count:1330
imagecitedby:scholar:timestamp:2023-1-5hecodexreviewsegmentationsurveycitedby:scholar:count:1330
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3Subgroup generalization and fairness of graph neural networks
J. Ma, J. Deng, и Q. Mei. Advances in Neural Information Processing Systems, (2021)
2 лет назад , @becker
fairness
subgroup
subgroups
neural
citedby:scholar:count:22
gnn
neurips
citedby:scholar:timestamp:2022-12-13
network
graph
themis
nn
fairnesssubgroupsubgroupsneuralcitedby:scholar:count:22gnnneuripscitedby:scholar:timestamp:2022-12-13networkgraphthemisnn
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2The Deskilling of Domain Expertise in AI Development
N. Sambasivan, и R. Veeraraghavan. CHI Conference on Human Factors in Computing Systems, стр. 1--14. (2022)
2 лет назад , @becker
domain
artificial
deskilling
machine
citedby:scholar:count:13
ai
learning
knowledge
expertise
themis
intelligence
citedby:scholar:timestamp:2022-12-8
domainartificialdeskillingmachinecitedby:scholar:count:13ailearningknowledgeexpertisethemisintelligencecitedby:scholar:timestamp:2022-12-8
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1CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images
M. Lee, J. Bedia, S. Bhate, G. Barlow, D. Phillips, W. Fantl, G. Nolan, и C. Schürch. BMC bioinformatics, 23 (1): 1--17 (2022)
2 лет назад , @becker
image
citedby:scholar:count:10
citedby:scholar:timestamp:2022-12-2
fluorescence
staining
nucleus
segmentation
yuqi
he
cell
imagecitedby:scholar:count:10citedby:scholar:timestamp:2022-12-2fluorescencestainingnucleussegmentationyuqihecell
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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, и G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
2 лет назад , @becker
comparison
forest
decision
neural
data
tabular
learning
tree
trees
network
performance
random
citedby:scholar:count:5
evaluation
citedby:scholar:timestamp:2022-12-1
machine
ml
nn
comparisonforestdecisionneuraldatatabularlearningtreetreesnetworkperformancerandomcitedby:scholar:count:5evaluationcitedby:scholar:timestamp:2022-12-1machinemlnn
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1Interpretable machine learning prediction of all-cause mortality
W. Qiu, H. Chen, A. Dincer, S. Lundberg, M. Kaeberlein, и S. Lee. medRxiv, (2022)
2 лет назад , @becker
supervised
distance
citedby:scholar:count:3
machine
collaboration
fuelln
learning
interpretable
citedby:scholar:timestamp:2022-11-30
ml
superviseddistancecitedby:scholar:count:3machinecollaborationfuellnlearninginterpretablecitedby:scholar:timestamp:2022-11-30ml
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2A gesture system for graph visualization in virtual reality environments
Y. Huang, T. Fujiwara, Y. Lin, W. Lin, и K. Ma. 2017 ieee pacific visualization symposium (pacificvis), стр. 41--45. IEEE, (2017)
2 лет назад , @becker
citedby:scholar:count:40
virtual
visualization
reality
citedby:scholar:timestamp:2022-11-25
immermind
m:
network
graph
gesture
m:id:immermind:coordinate:x:-515.505973815918
m:id:immermind:coordinate:y:-42.4267578125
m:id:immermind:node
citedby:scholar:count:40virtualvisualizationrealitycitedby:scholar:timestamp:2022-11-25immermindm:networkgraphgesturem:id:immermind:coordinate:x:-515.505973815918m:id:immermind:coordinate:y:-42.4267578125m:id:immermind:node
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1Predicting cell populations in single cell mass cytometry data
T. Abdelaal, V. van Unen, T. Höllt, F. Koning, M. Reinders, и A. Mahfouz. Cytometry Part A, 95 (7): 769--781 (2019)
2 лет назад , @becker
cytometry
mass
citedby:scholar:count:35
prediction
yuqi
citedby:scholar:timestamp:2022-11-22
single
cell
type
cytof
cytometrymasscitedby:scholar:count:35predictionyuqicitedby:scholar:timestamp:2022-11-22singlecelltypecytof
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1Graph representation learning in biomedicine and healthcare
M. Li, K. Huang, и M. Zitnik. Nature Biomedical Engineering, (2022)
2 лет назад , @becker
toread
neural
learning
biomedicine
medicine
representation
networks
graph
health
themis
healthcare
toreadneurallearningbiomedicinemedicinerepresentationnetworksgraphhealththemishealthcare
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1Map: Project - BMBF: AI 3 Simulation

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1Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

1Evidence for human milk as a biological system and recommendations for study design—a report from “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Working Group 4

2Machine learning techniques for accountability

1Explainability in graph neural networks: A taxonomic survey

1Data-driven longitudinal characterization of neonatal health and morbidity

1Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

1Machine Learning Techniques

1Early prediction and longitudinal modeling of preeclampsia from multiomics

1In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network

1Shorter maternal leukocyte telomere length following cesarean birth: Implications for future research

3Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

2Image segmentation using deep learning: A survey

3Subgroup generalization and fairness of graph neural networks

2The Deskilling of Domain Expertise in AI Development

1CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

2Why do tree-based models still outperform deep learning on typical tabular data?

1Interpretable machine learning prediction of all-cause mortality

2A gesture system for graph visualization in virtual reality environments

1Predicting cell populations in single cell mass cytometry data

1Graph representation learning in biomedicine and healthcare

Uni Würzburg - LS6 - WS 2011 - Seminar - Twitter Analyse

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Uni Würzburg - LS6 - WS 2011 - Seminar - Twitter Analyse

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