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1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.
https://github.com/hurcy/awesome-ehr-deeplearning
vor 4 Jahren von @becker
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deep
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3Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science
https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
vor 4 Jahren von @becker
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transfer
learning
when
to
use
ml
neural
network
nn
machine
dataset
size
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1GFLASSO: Graph-Guided Fused LASSO in R - DataCamp
https://www.datacamp.com/community/tutorials/gflasso-R
vor 4 Jahren von @becker
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graph
guided
lasso
machine
learning
r
multilabel
multi
label
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1mims-harvard/scikit-fusion: scikit-fusion: Data fusion via collective latent factor models
https://github.com/mims-harvard/scikit-fusion
4 лет назад , @becker
data
fusion
mit
multiview
learning
machine
graph
graphs
network
python
sklearn
skfusion
scikit
matrix
factorization
gpu
py
heterogeneous
latent
factor
datafusionmitmultiviewlearningmachinegraphgraphsnetworkpythonsklearnskfusionscikitmatrixfactorizationgpupyheterogeneouslatentfactor
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1MULTI-VIEW LEARNING | Papers With Code
https://paperswithcode.com/task/multi-view-learning/
4 лет назад , @becker
python
multi
view
learning
ml
multiomics
omics
multiview
nalab
butterfly
learn
py
overview
code
list
pythonmultiviewlearningmlmultiomicsomicsmultiviewnalabbutterflylearnpyoverviewcodelist
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1Overview of mvlearn — mvlearn alpha documentation
https://mvlearn.neurodata.io/index.html
4 лет назад , @becker
python
multi
view
learning
ml
multiomics
omics
multiview
nalab
butterfly
learn
py
pythonmultiviewlearningmlmultiomicsomicsmultiviewnalabbutterflylearnpy
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2Statistical Significance Tests for Comparing Machine Learning Algorithms
https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
4 лет назад , @becker
statistical
significance
test
testing
hypothesis
comparison
machine
learning
data
mining
science
cross
validation
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
4 лет назад , @becker
interpretable
machine
learning
ml
xai
ai
artifical
intelligence
explainable
interpretablemachinelearningmlxaiaiartificalintelligenceexplainable
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1How to use Data Scaling Improve Deep Learning Model Stability and Performance
https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
4 лет назад , @becker
deep
learning
scaling
input
output
data
machine
project
butterfly
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1A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
4 лет назад , @becker
transfer
learning
neural
network
networks
nn
project
butterfly
echo
deep
transferlearningneuralnetworknetworksnnprojectbutterflyechodeep
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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
(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 (Januar 2023)
6 месяцев назад , @becker
background
bki
ien
informed
integration
knowledge
learning
linear
machine
ml
regression
backgroundbkiieninformedintegrationknowledgelearninglinearmachinemlregression
(0)
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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, (September 2019)
6 месяцев назад , @becker
background
bki
gnn
graph
informed
integration
knowledge
learning
machine
ml
networks
neural
backgroundbkignngraphinformedintegrationknowledgelearningmachinemlnetworksneural
(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
(0)
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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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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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2Machine learning techniques for accountability
B. Kim, и F. Doshi-Velez. AI Magazine, 42 (1): 47--52 (2021)
a year ago by @becker
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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
(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 and 1 other author(s). Frontiers in Pediatrics, (2022)
2 years ago by @becker
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adverse
cell
immune
immunology
learning
life
machine
multitask
myown
outcomes
pregnancy
single
stress
stressors
system
adversecellimmuneimmunologylearninglifemachinemultitaskmyownoutcomespregnancysinglestressstressorssystem
(0)
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1Machine Learning Techniques
J. Choi, N. Aghaeepour, and M. Becker. page 147--164. Springer International Publishing, Cham, (2022)
2 years ago by @becker
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book
chapter
learning
machine
myown
surgery
bookchapterlearningmachinemyownsurgery
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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, and A. Ho. arXiv preprint arXiv:2211.04325, (2022)
2 years ago by @becker
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themis
large
data
machine
learning
requirement
deep
models
model
dataset
datasets
set
sets
limit
themislargedatamachinelearningrequirementdeepmodelsmodeldatasetdatasetssetsetslimit
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2The Deskilling of Domain Expertise in AI Development
N. Sambasivan, and R. Veeraraghavan. CHI Conference on Human Factors in Computing Systems, page 1--14. (2022)
2 years ago by @becker
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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
(0)
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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
2 years ago by @becker
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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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1Interpretable machine learning prediction of all-cause mortality
W. Qiu, H. Chen, A. Dincer, S. Lundberg, M. Kaeberlein, and S. Lee. medRxiv, (2022)
2 years ago by @becker
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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
(0)
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1Graph representation learning in biomedicine and healthcare
M. Li, K. Huang, and M. Zitnik. Nature Biomedical Engineering, (2022)
2 years ago by @becker
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graph
representation
learning
neural
networks
toread
themis
biomedicine
medicine
health
healthcare
graphrepresentationlearningneuralnetworkstoreadthemisbiomedicinemedicinehealthhealthcare
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2Algorithmic fairness datasets: the story so far
A. Fabris, S. Messina, G. Silvello, and G. Susto. Data Mining and Knowledge Discovery, 36 (6): 2074--2152 (01.11.2022)
2 years ago by @becker
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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 and 1 other author(s). Nature Biotechnology, 40 (1): 121--130 (2022)
2 years ago by @becker
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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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1A guide to machine learning for biologists
J. Greener, S. Kandathil, L. Moffat, and D. Jones. Nature Reviews Molecular Cell Biology, 23 (1): 40--55 (2022)
2 years ago by @becker
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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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1Explaining deep neural networks and beyond: A review of methods and applications
W. Samek, G. Montavon, S. Lapuschkin, C. Anders, and K. Müller. Proceedings of the IEEE, 109 (3): 247--278 (2021)
2 years ago by @becker
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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 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 and 1 other author(s). Ai Magazine, 43 (1): 46--58 (2022)
2 years ago by @becker
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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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2Evolution-Informed Neural Networks for Microbiome Data Analysis
M. Ito, Y. Glaser, and P. Sadowski. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), page 3386--3391. IEEE, (2021)
2 years ago by @becker
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bioinformatics
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information
knowledge
learning
micribiome
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neutral
nn
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1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.

3Transfer learning from pre-trained models | by Pedro Marcelino | Towards Data Science

1GFLASSO: Graph-Guided Fused LASSO in R - DataCamp

1mims-harvard/scikit-fusion: scikit-fusion: Data fusion via collective latent factor models

1MULTI-VIEW LEARNING | Papers With Code

1Overview of mvlearn — mvlearn alpha documentation

2Statistical Significance Tests for Comparing Machine Learning Algorithms

4Interpretable Machine Learning

1How to use Data Scaling Improve Deep Learning Model Stability and Performance

1A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

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1Computational Approaches for Connecting Maternal Stress to Preterm Birth

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

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

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

1Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature

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

2Machine learning techniques for accountability

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

1Machine Learning Techniques

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

2The Deskilling of Domain Expertise in AI Development

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

1Interpretable machine learning prediction of all-cause mortality

1Graph representation learning in biomedicine and healthcare

2Algorithmic fairness datasets: the story so far

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

1A guide to machine learning for biologists

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

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

2Evolution-Informed Neural Networks for Microbiome Data Analysis

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