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2Evolution-Informed Neural Networks for Microbiome Data Analysis
M. Ito, Y. Glaser, и P. Sadowski. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), стр. 3386--3391. IEEE, (2021)
2 лет назад , @becker
background
bioinformatics
deep
information
knowledge
learning
micribiome
network
neutral
nn
themis
backgroundbioinformaticsdeepinformationknowledgelearningmicribiomenetworkneutralnnthemis
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1A biomedical open knowledge network harnesses the power of AI to understand deep human biology
S. Baranzini, K. Börner, J. Morris, C. Nelson, K. Soman, E. Schleimer, M. Keiser, M. Musen, R. Pearce, T. Reza и 1 other автор(ы). Ai Magazine, 43 (1): 46--58 (2022)
2 лет назад , @becker
ai
background
biology
biomedical
citedby:scholar:count:1
citedby:scholar:timestamp:2022-6-21
graph
human
knowledge
learning
machine
network
open
aibackgroundbiologybiomedicalcitedby:scholar:count:1citedby:scholar:timestamp:2022-6-21graphhumanknowledgelearningmachinenetworkopen
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1Explaining deep neural networks and beyond: A review of methods and applications
W. Samek, G. Montavon, S. Lapuschkin, C. Anders, и K. Müller. Proceedings of the IEEE, 109 (3): 247--278 (2021)
2 лет назад , @becker
explainable
machine
learning
neural
networks
themis
citedby:scholar:count:193
citedby:scholar:timestamp:2022-6-27
explainablemachinelearningneuralnetworksthemiscitedby:scholar:count:193citedby:scholar:timestamp:2022-6-27
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1A guide to machine learning for biologists
J. Greener, S. Kandathil, L. Moffat, и D. Jones. Nature Reviews Molecular Cell Biology, 23 (1): 40--55 (2022)
2 лет назад , @becker
guide
machine
learning
biologists
nature
citedby:scholar:count:72
citedby:scholar:timestamp:2022-6-29
guidemachinelearningbiologistsnaturecitedby:scholar:count:72citedby:scholar:timestamp:2022-6-29
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1Mapping single-cell data to reference atlases by transfer learning
M. Lotfollahi, M. Naghipourfar, M. Luecken, M. Khajavi, M. Büttner, M. Wagenstetter, {. Avsec, A. Gayoso, N. Yosef, M. Interlandi и 1 other автор(ы). Nature Biotechnology, 40 (1): 121--130 (2022)
2 лет назад , @becker
single
cell
mapping
reference
atlas
transfer
learning
combine
datasets
dataset
nature
theiss
citedby:scholar:count:57
citedby:scholar:timestamp:2022-8-30
singlecellmappingreferenceatlastransferlearningcombinedatasetsdatasetnaturetheisscitedby:scholar:count:57citedby:scholar:timestamp:2022-8-30
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2Algorithmic fairness datasets: the story so far
A. Fabris, S. Messina, G. Silvello, и G. Susto. Data Mining and Knowledge Discovery, 36 (6): 2074--2152 (01.11.2022)
2 лет назад , @becker
algorithmic
fairness
learning
machine
themis
toread
algorithmicfairnesslearningmachinethemistoread
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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
graph
representation
learning
neural
networks
toread
themis
biomedicine
medicine
health
healthcare
graphrepresentationlearningneuralnetworkstoreadthemisbiomedicinemedicinehealthhealthcare
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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
fuelln
interpretable
machine
learning
ml
supervised
distance
collaboration
citedby:scholar:count:3
citedby:scholar:timestamp:2022-11-30
fuellninterpretablemachinelearningmlsuperviseddistancecollaborationcitedby:scholar:count:3citedby:scholar:timestamp:2022-11-30
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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
citedby:scholar:count:5
citedby:scholar:timestamp:2022-12-1
comparison
data
decision
evaluation
forest
learning
machine
ml
network
neural
nn
performance
random
tabular
tree
trees
citedby:scholar:count:5citedby:scholar:timestamp:2022-12-1comparisondatadecisionevaluationforestlearningmachinemlnetworkneuralnnperformancerandomtabulartreetrees
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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
knowledge
themis
domain
expertise
ai
artificial
intelligence
machine
learning
deskilling
citedby:scholar:count:13
citedby:scholar:timestamp:2022-12-8
knowledgethemisdomainexpertiseaiartificialintelligencemachinelearningdeskillingcitedby:scholar:count:13citedby:scholar:timestamp:2022-12-8
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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
themis
large
data
machine
learning
requirement
deep
models
model
dataset
datasets
set
sets
limit
themislargedatamachinelearningrequirementdeepmodelsmodeldatasetdatasetssetsetslimit
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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
book
chapter
learning
machine
myown
surgery
bookchapterlearningmachinemyownsurgery
(0)
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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
adverse
cell
immune
immunology
learning
life
machine
multitask
myown
outcomes
pregnancy
single
stress
stressors
system
adversecellimmuneimmunologylearninglifemachinemultitaskmyownoutcomespregnancysinglestressstressorssystem
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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
ai
artificial
intelligence
machine
learning
ml
accountability
responsible
citedby:scholar:count:15
citedby:scholar:timestamp:2023-4-10
aiartificialintelligencemachinelearningmlaccountabilityresponsiblecitedby:scholar:count:15citedby:scholar:timestamp:2023-4-10
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1Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L. Oakden-Rayner, J. Dunnmon, G. Carneiro, и C. Ré. Proceedings of the ACM conference on health, inference, and learning, стр. 151--159. (2020)
год назад , @becker
hidden
stratification
subgroup
machine
learning
failure
pattern
mining
citedby:scholar:count:243
citedby:scholar:timestamp:2023-5-31
hiddenstratificationsubgroupmachinelearningfailurepatternminingcitedby:scholar:count:243citedby:scholar:timestamp:2023-5-31
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1Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature
E. Berson, A. Sreenivas, T. Phongpreecha, A. Perna, F. Grandi, L. Xue, N. Ravindra, N. Payrovnaziri, S. Mataraso, Y. Kim и 9 other автор(ы). Nature Communications, 14 (1): 4947 (2023)
10 месяцев назад , @becker
alzheimer
deconvolution
epigenetics
genome
learning
machine
myown
alzheimerdeconvolutionepigeneticsgenomelearningmachinemyown
(0)
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1Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity
N. Ravindra, C. Espinosa, E. Berson, T. Phongpreecha, P. Zhao, M. Becker, A. Chang, S. Shome, I. Marić, D. De Francesco и 1 other автор(ы). npj Digital Medicine, 6 (1): 171 (2023)
10 месяцев назад , @becker
accelerometer
activity
deep
learning
myown
pregnancy
prematurity
representation
sleep
accelerometeractivitydeeplearningmyownpregnancyprematurityrepresentationsleep
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2Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer
H. Chereda, A. Bleckmann, F. Kramer, A. Leha, и T. Beissbarth. Studies in health technology and informatics, (сентября 2019)
6 месяцев назад , @becker
background
bki
gnn
graph
informed
integration
knowledge
learning
machine
ml
networks
neural
backgroundbkignngraphinformedintegrationknowledgelearningmachinemlnetworksneural
(0)
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1Feature-weighted elastic net: using "features of features" for better prediction
J. Tay, N. Aghaeepour, T. Hastie, и R. Tibshirani. Statistica Sinica, 33 (1): 259--279 (января 2023)
6 месяцев назад , @becker
background
bki
ien
informed
integration
knowledge
learning
linear
machine
ml
regression
backgroundbkiieninformedintegrationknowledgelearninglinearmachinemlregression
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1Computational Approaches for Connecting Maternal Stress to Preterm Birth
A. Mirzaei, B. Hiller, I. Stelzer, K. Thiele, Y. Tan, и M. Becker. Clinics in Perinatology, (2024)
4 месяцев назад , @becker
computational
learning
machine
myown
pregnancy
stress
computationallearningmachinemyownpregnancystress
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BibSonomy

публикации (спрятать)123
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2Evolution-Informed Neural Networks for Microbiome Data Analysis

1A biomedical open knowledge network harnesses the power of AI to understand deep human biology

1Explaining deep neural networks and beyond: A review of methods and applications

1A guide to machine learning for biologists

1Mapping single-cell data to reference atlases by transfer learning

2Algorithmic fairness datasets: the story so far

1Graph representation learning in biomedicine and healthcare

1Interpretable machine learning prediction of all-cause mortality

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

2The Deskilling of Domain Expertise in AI Development

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

1Machine Learning Techniques

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

2Machine learning techniques for accountability

1Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

1Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature

1Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

2Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer

1Feature-weighted elastic net: using "features of features" for better prediction

1Computational Approaches for Connecting Maternal Stress to Preterm Birth

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