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1Bayesian regression predictions - Tutorials - Pyro Discussion Forum
https://forum.pyro.ai/t/bayesian-regression-predictions/1115/8
4 years ago by @becker
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1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro
https://github.com/pyro-ppl/pyro/issues/1930
4 years ago by @becker
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1Financial forecasting with probabilistic programming and Pyro
https://medium.com/@alexrachnog/financial-forecasting-with-probabilistic-programming-and-pyro-db68ab1a1dba
4 years ago by @becker
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1machine learning - Bias and variance in leave-one-out vs K-fold cross validation - Cross Validated
https://stats.stackexchange.com/questions/61783/bias-and-variance-in-leave-one-out-vs-k-fold-cross-validation
6 years ago by @becker
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1predictive models - Differences between cross validation and bootstrapping to estimate the prediction error - Cross Validated
https://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio
6 years ago by @becker
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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 and 1 other author(s). Patterns, 3 (12): 100655 (2022)
a year ago by @becker
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longitudinal
modeling
multi
multiomics
myown
omics
prediction
preeclampsia
pregnancy
longitudinalmodelingmultimultiomicsmyownomicspredictionpreeclampsiapregnancy
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1Predicting cell populations in single cell mass cytometry data
T. Abdelaal, V. van Unen, T. Höllt, F. Koning, M. Reinders, and A. Mahfouz. Cytometry Part A, 95 (7): 769--781 (2019)
2 years ago by @becker
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cytof
single
cell
type
prediction
yuqi
mass
cytometry
citedby:scholar:count:35
citedby:scholar:timestamp:2022-11-22
cytofsinglecelltypepredictionyuqimasscytometrycitedby:scholar:count:35citedby:scholar:timestamp:2022-11-22
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2BoostEMM-Transparent Boosting using Exceptional Model Mining.
S. van der Zon, O. Zeev-Ben-Mordehai, T. Vrijdag, W. van Ipenburg, W. Duivesteijn, J. Veldsink, and M. Pechenizkiy. MIDAS@ PKDD/ECML, page 5--16. (2017)
3 years ago by @becker
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subgroup
discovery
emm
boosting
exceptional
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mining
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1VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
N. Stanley, I. Stelzer, A. Tsai, R. Fallahzadeh, E. Ganio, M. Becker, T. Phongpreecha, H. Nassar, S. Ghaemi, I. Maric and 20 other author(s). Nature Communications, 11 (1): 3738-- (2020)
4 years ago by @becker
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heterogeneous
mass
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myown
p21
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project:bmbf
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autocellclusteringcytofcytometrygatingheterogeneousmassmetamyownp21predictionpredictiveproject:bmbfprojectrelatedsingle
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1Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
J. Sugawara, D. Ochi, R. Yamashita, T. Yamauchi, D. Saigusa, M. Wagata, T. Obara, M. Ishikuro, Y. Tsunemoto, Y. Harada and 1 other author(s). BMJ open, 9 (2): e025939 (2019)
4 years ago by @becker
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log
data
maternity
pregnancy
multiomics
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lifelog
monitoring
activity
fetal
complications
outcome
prediction
analysis
citedby:scholar:count:4
citedby:scholar:timestamp:2020-8-11
logdatamaternitypregnancymultiomicslongitudinallifelogmonitoringactivityfetalcomplicationsoutcomepredictionanalysiscitedby:scholar:count:4citedby:scholar:timestamp:2020-8-11
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2Deep learning for inferring gene relationships from single-cell expression data
Y. Yuan, and Z. Bar-Joseph. Proceedings of the National Academy of Sciences, 116 (52): 27151--27158 (2019)
4 years ago by @becker
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cell
cnn
deep
gene
learning
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cellcnndeepgenelearningnetworkneuralnnpredictionsingle
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12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, and A. Hotho. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6 (3): 1--24 (2020)
4 years ago by @becker
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air
cnn
image
map
mapping
myown
network
networks
neural
open
p21
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project:bmbf
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street
aircnnimagemapmappingmyownnetworknetworksneuralopenp21predictionproject:bmbfqualitystreet
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1Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia
X. Han, M. Ghaemi, K. Ando, L. Peterson, E. Ganio, A. Tsai, D. Gaudilliere, J. Einhaus, B. Bertrand, N. Stanley and 1 other author(s). Frontiers in immunology, (2019)
5 years ago by @becker
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correlation
disruption
immune
iti
maternal
nalab
paper:cordis
paper:fastcor
prediction
pree
preeclampsia
system
correlationdisruptionimmuneitimaternalnalabpaper:cordispaper:fastcorpredictionpreepreeclampsiasystem
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1ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI
S. Fazli, M. Danóczy, J. Schelldorfer, and K. Mueller. NeuroImage, (2011)
5 years ago by @becker
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effect
mixed
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nalab
prediction
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effectmixedmodelnalabpredictionsubgroups
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2DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
A. Singh, C. Shannon, B. Gautier, F. Rohart, M. Vacher, S. Tebbutt, and K. Lê Cao. Bioinformatics, (2019)
5 years ago by @becker
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bio
immune
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multi
multiomics
omics
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preterm
system
bioimmuneintegrationmchrimultimultiomicsomicspredictionpretermsystem
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5Understanding Where Your Classifier Does (Not) Work--The SCaPE Model Class for EMM
W. Duivesteijn, and J. Thaele. 2014 IEEE International Conference on Data Mining, page 809--814. IEEE, (2014)
5 years ago by @becker
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classification
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pattern
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project:bmbf
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classificationclassifierdiscoveryminingpatternpredictionproject:bmbfsubgroupsubgroupsunderstandworkwouter
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2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, and Z. He. Sustainability, 8 (8): 786 (2016)
6 years ago by @becker
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regression
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street
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air
quality
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citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
6 years ago by @becker
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3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
6 years ago by @becker
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lur
land
use
regression
air
quality
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2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, and P. Kanaroglou. Journal of environmental management, (2016)
6 years ago by @becker
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lur
land
use
regression
air
pollution
quality
eva
p2map
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network
nn
mobile
stationary
prediction
citedby:scholar:count:31
citedby:scholar:timestamp:2018-2-21
lurlanduseregressionairpollutionqualityevap2mapneuralnetworknnmobilestationarypredictioncitedby:scholar:count:31citedby:scholar:timestamp:2018-2-21
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2b+ WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring—Preliminary results and analysis
F. Murphy, M. Magno, P. Whelan, and E. Vici. Sensors Applications Symposium (SAS), 2015 IEEE, page 1--6. IEEE, (2015)
7 years ago by @becker
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hobos
bee
honey
bees
machine
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ml
weather
prediction
citedby:scholar:count:11
citedby:scholar:timestamp:2017-7-20
hobosbeehoneybeesmachinelearningmlweatherpredictioncitedby:scholar:count:11citedby:scholar:timestamp:2017-7-20
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1b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring
F. Edwards-Murphy, M. Magno, P. Whelan, J. O’Halloran, and E. Popovici. Computers and Electronics in Agriculture, (2016)
7 years ago by @becker
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citedby:scholar:timestamp:2017-7-20
hobosbeehoneybeesmachinelearningmlweatherpredictioncitedby:scholar:count:4citedby:scholar:timestamp:2017-7-20
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2Using markov chains for link prediction in adaptive web sites
J. Zhu, J. Hong, and J. Hughes. Soft-Ware 2002: Computing in an Imperfect World, Springer, (2002)
7 years ago by @becker
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inthesis
diss
link
prediction
citedby:scholar:count:174
citedby:scholar:timestamp:2017-7-17
inthesisdisslinkpredictioncitedby:scholar:count:174citedby:scholar:timestamp:2017-7-17
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1To explain or to predict?
G. Shmueli, and others. Statistical Science, 25 (3): 289--310 (2010)
7 years ago by @becker
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analysiscitedby:scholar:count:513citedby:scholar:timestamp:2017-7-10datadissexplainexplanationinthesispredictprediction
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4TribeFlow: Mining & Predicting User Trajectories
F. Figueiredo, B. Ribeiro, J. Almeida, and C. Faloutsos. International Conference on World Wide Web, page 695--706. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2016)
7 years ago by @becker
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1Bayesian regression predictions - Tutorials - Pyro Discussion Forum

1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro

1Financial forecasting with probabilistic programming and Pyro

1machine learning - Bias and variance in leave-one-out vs K-fold cross validation - Cross Validated

1predictive models - Differences between cross validation and bootstrapping to estimate the prediction error - Cross Validated

publications (hide)51
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1Early prediction and longitudinal modeling of preeclampsia from multiomics

1Predicting cell populations in single cell mass cytometry data

2BoostEMM-Transparent Boosting using Exceptional Model Mining.

1VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

1Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy

2Deep learning for inferring gene relationships from single-cell expression data

12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

1Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia

1ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

2DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

5Understanding Where Your Classifier Does (Not) Work--The SCaPE Model Class for EMM

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING

2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models

2b+ WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring—Preliminary results and analysis

1b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring

2Using markov chains for link prediction in adaptive web sites

1To explain or to predict?

4TribeFlow: Mining & Predicting User Trajectories

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