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1Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated
https://www.nature.com/articles/s41598-022-14395-4#citeas
a year ago by @becker
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analysis
component
critique
evaluation
pca
principle
analysiscomponentcritiqueevaluationpcaprinciple
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1Propensity Score Analysis | Columbia Public Health
https://www.publichealth.columbia.edu/research/population-health-methods/propensity-score-analysis
a year ago by @becker
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propensity
score
matching
simple
tutorial
explanation
analysis
biomedicine
propensityscorematchingsimpletutorialexplanationanalysisbiomedicine
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1scverse - single cell methods
https://scverse.org/
2 years ago by @becker
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analysis
cell
github
methods
single
software
analysiscellgithubmethodssinglesoftware
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1shafferm/SCNIC: Sparse Cooccurence Network Investigation for Compositional data
https://github.com/shafferm/SCNIC
2 years ago by @becker
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project:fastcor
compositional
data
correlation
positive
co-occurrence
coocurrence
network
analysis
project:fastcorcompositionaldatacorrelationpositiveco-occurrencecoocurrencenetworkanalysis
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1Varimax PCA
https://twitter.com/karlrohe/status/1513528399201021965?t=UhJUk71F5_eYfusM7qY-YA&s=19
2 years ago by @becker
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analysis
factor
inference
pca
toread
varimax
analysisfactorinferencepcatoreadvarimax
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1Mummichog
https://shuzhao-li.github.io/mummichog.org/
2 years ago by @becker
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metabolomics
metabolites
python
py
pathway
network
analysis
metabolomicsmetabolitespythonpypathwaynetworkanalysis
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2zqfang/GSEApy: Gene Set Enrichment Analysis in Python
https://github.com/zqfang/GSEApy
2 years ago by @becker
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data
base
database
api
py
python
gene
enrichment
analysis
set
databasedatabaseapipypythongeneenrichmentanalysisset
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1ryananeff/DGCA: DGCA 2.0: Highly Parallel and Memory-efficient Differential Correlation analysis
https://github.com/ryananeff/DGCA
3 years ago by @becker
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dgca
parallel
fast
differential
gene
analysis
correlation
paper:fastcor
dgcaparallelfastdifferentialgeneanalysiscorrelationpaper:fastcor
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1ICA, Nonlinear Independent Component Analysis, Unsupervised Learning, Redundancy Reduction
http://people.idsia.ch/~juergen/ica.html
6 years ago by @becker
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non
linear
pca
ica
machine
learning
from:hotho
nonlinear
independent
component
analysis
nonlinearpcaicamachinelearningfrom:hothononlinearindependentcomponentanalysis
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1http://i.stanford.edu/~jure/pub/talks2/netalgs-bignet-apr17.pdf
http://i.stanford.edu/~jure/pub/talks2/netalgs-bignet-apr17.pdf
7 years ago by @becker
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network
analysis
jure
snap
algorithms
algorithm
analytics
networkanalysisjuresnapalgorithmsalgorithmanalytics
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1Sequential Data Analysis Introduction
http://www.orgdna.net/traminer/material/slides/vbm_sequential_day1.pdf
7 years ago by @becker
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diss
sequence
analysis
sequential
overview
disssequenceanalysissequentialoverview
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1Inductive or Deductive? Two Different Approaches
http://2012books.lardbucket.org/books/sociological-inquiry-principles-qualitative-and-quantitative-methods/s05-03-inductive-or-deductive-two-dif.html
7 years ago by @becker
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inductive
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research
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1Deduction & Induction
http://www.socialresearchmethods.net/kb/dedind.php
7 years ago by @becker
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analysis
deduction
deductive
induction
inductive
reasoning
research
analysisdeductiondeductiveinductioninductivereasoningresearch
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1Inductive and deductive approaches to research | Dr Deborah Gabriel
http://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/
7 years ago by @becker
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analysis
deduction
deductive
induction
inductive
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research
analysisdeductiondeductiveinductioninductivereasoningresearch
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1SVD & PCA using R
SVD & PCA and realtion in R, but does not calculate PCA from SVD
10 years ago by @becker
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analysis
component
decomposition
pca
principal
singular
svd
value
analysiscomponentdecompositionpcaprincipalsingularsvdvalue
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2PCA and SVD
An introduction to SVD and PCA and its comparison.
10 years ago by @becker
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analysis
component
decomposition
pca
principal
singular
svd
value
analysiscomponentdecompositionpcaprincipalsingularsvdvalue
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6Stanford Large Network Dataset Collection
http://snap.stanford.edu/data/
10 years ago by @becker
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analysis
dataset
network
analysisdatasetnetwork
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4R: Time Series Analysis
http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/zeitreihen/sommer03/ts_r_intro.pdf
11 years ago by @becker
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R
analysis
calibration
eva
series
time
Ranalysiscalibrationevaseriestime
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1Weka: Time Series Analysis and Forecasting with Weka
http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka
11 years ago by @becker
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analysis
calibration
eva
forecasting
series
time
weka
analysiscalibrationevaforecastingseriestimeweka
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1Alex Davies | A word list for sentiment analysis of Twitter
http://alexdavies.net/2011/10/word-lists-for-sentiment-analysis-of-twitter/
12 years ago by @becker
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analysis
sentiment
twitter
analysissentimenttwitter
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1DiffCorr: an R package to analyze and visualize differential correlations in biological networks
A. Fukushima. Gene 518 (1): 209--214 (2013)
a year ago by @becker
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paper:fastcor
differential
correlation
analysis
citedby:scholar:count:142
citedby:scholar:timestamp:2022-9-18
paper:fastcordifferentialcorrelationanalysiscitedby:scholar:count:142citedby:scholar:timestamp:2022-9-18
(0)
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2The discordant method: a novel approach for differential correlation
C. Siska, R. Bowler, and K. Kechris. Bioinformatics 32 (5): 690--696 (2016)
a year ago by @becker
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paper:fastcor
differential
correlation
analysis
citedby:scholar:count:28
citedby:scholar:timestamp:2022-9-18
paper:fastcordifferentialcorrelationanalysiscitedby:scholar:count:28citedby:scholar:timestamp:2022-9-18
(0)
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2DCARS: differential correlation across ranked samples
S. Ghazanfar, D. Strbenac, J. Ormerod, J. Yang, and E. Patrick. Bioinformatics 35 (5): 823--829 (2019)
a year ago by @becker
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paper:fastcor
differential
correlation
analysis
citedby:scholar:count:5
citedby:scholar:timestamp:2022-9-18
paper:fastcordifferentialcorrelationanalysiscitedby:scholar:count:5citedby:scholar:timestamp:2022-9-18
(0)
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2DINGO: differential network analysis in genomics
M. Ha, V. Baladandayuthapani, and K. Do. Bioinformatics 31 (21): 3413--3420 (2015)
2 years ago by @becker
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paper:fastcor
differential
network
analysis
citedby:scholar:count:106
citedby:scholar:timestamp:2022-4-27
m:
m:id:test2:node
m:id:test2:coordinate:x:177.40279006958008
m:id:test2:coordinate:y:-49.76913261413574
paper:fastcordifferentialnetworkanalysiscitedby:scholar:count:106citedby:scholar:timestamp:2022-4-27m:m:id:test2:nodem:id:test2:coordinate:x:177.40279006958008m:id:test2:coordinate:y:-49.76913261413574
(0)
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1Differential network analysis by simultaneously considering changes in gene interactions and gene expression
J. Tu, L. Ou-Yang, Y. Zhu, H. Yan, H. Qin, and X. Zhang. Bioinformatics 37 (23): 4414--4423 (2021)
2 years ago by @becker
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paper:fastcor
differential
network
analysis
citedby:scholar:count:3
citedby:scholar:timestamp:2022-4-27
paper:fastcordifferentialnetworkanalysiscitedby:scholar:count:3citedby:scholar:timestamp:2022-4-27
(0)
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1BioNetStat: a tool for biological networks differential analysis
V. Jardim, S. Santos, A. Fujita, and M. Buckeridge. Frontiers in genetics (2019)
2 years ago by @becker
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paper:fastcor
differential
network
analysis
citedby:scholar:count:13
citedby:scholar:timestamp:2022-4-27
paper:fastcordifferentialnetworkanalysiscitedby:scholar:count:13citedby:scholar:timestamp:2022-4-27
(0)
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1Estimating batch effect in Microarray data with Principal Variance Component Analysis (PVCA) method
P. Bushel, and J. Li. (2015)
2 years ago by @becker
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citedby:scholar:timestamp:2022-3-18
principal
component
citedby:scholar:count:1
variance
pvca
test
analysis
pca
citedby:scholar:timestamp:2022-3-18principalcomponentcitedby:scholar:count:1variancepvcatestanalysispca
(0)
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1PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data
R. Hernández-de Diego, S. Tarazona, C. Mart\'ınez-Mira, L. Balzano-Nogueira, P. Furió-Tar\'ı, G. Pappas Jr, and A. Conesa. Nucleic acids research 46 (W1): W503--W509 (2018)
2 years ago by @becker
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pathway
analysis
um1
nalab
citedby:scholar:count:98
citedby:scholar:timestamp:2022-1-27
pathwayanalysisum1nalabcitedby:scholar:count:98citedby:scholar:timestamp:2022-1-27
(0)
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1Integrative pathway enrichment analysis of multivariate omics data
M. Paczkowska, J. Barenboim, N. Sintupisut, N. Fox, H. Zhu, D. Abd-Rabbo, M. Mee, P. Boutros, and J. Reimand. Nature communications 11 (1): 1--16 (2020)
2 years ago by @becker
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pathway
analysis
um1
nalab
citedby:scholar:count:53
citedby:scholar:timestamp:2022-1-27
pathwayanalysisum1nalabcitedby:scholar:count:53citedby:scholar:timestamp:2022-1-27
(0)
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1Exploring Relations in Neuroscientific Literature using Augmented Reality: A Design Study
I. Troost, G. Tanhaei, L. Hardman, and W. Hürst. Designing Interactive Systems Conference 2021 , page 266--274. (2021)
2 years ago by @becker
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literature
exploration
virtual
reality
vr
analysis
immermind
m:
m:id:immermind:node
m:id:immermind:coordinate:x:52.87889862060547
m:id:immermind:coordinate:y:-250.97742080688477
literatureexplorationvirtualrealityvranalysisimmermindm:m:id:immermind:nodem:id:immermind:coordinate:x:52.87889862060547m:id:immermind:coordinate:y:-250.97742080688477
(0)
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3Imaxes: Immersive axes as embodied affordances for interactive multivariate data visualisation
M. Cordeil, A. Cunningham, T. Dwyer, B. Thomas, and K. Marriott. Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology , page 71--83. (2017)
2 years ago by @becker
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virtual
citedby:scholar:count:140
reality
data
vr
analysis
immermind
plots
citedby:scholar:timestamp:2022-1-11
m:
m:id:immermind:node
m:id:immermind:coordinate:x:-378.8338394165039
m:id:immermind:coordinate:y:134.9595603942871
virtualcitedby:scholar:count:140realitydatavranalysisimmermindplotscitedby:scholar:timestamp:2022-1-11m:m:id:immermind:nodem:id:immermind:coordinate:x:-378.8338394165039m:id:immermind:coordinate:y:134.9595603942871
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1Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors
Y. Wang, M. Anderson, J. Cohen, A. Heinecke, K. Li, N. Satish, N. Sundaram, N. Turk-Browne, and T. Willke. SC'15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , page 1--12. IEEE, (2015)
3 years ago by @becker
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paper:fastcor
fmri
parallel
distributed
fcma
full
correlation
matrix
analysis
brain
connectivity
functional
citedby:scholar:count:15
citedby:scholar:timestamp:2021-1-7
paper:fastcorfmriparalleldistributedfcmafullcorrelationmatrixanalysisbrainconnectivityfunctionalcitedby:scholar:count:15citedby:scholar:timestamp:2021-1-7
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2The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
D. Shuman, S. Narang, P. Frossard, A. Ortega, and P. Vandergheynst. IEEE signal processing magazine 30 (3): 83--98 (2013)
3 years ago by @becker
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graph
signal
processing
analysis
cordis
dis
functional
citedby:scholar:count:2114
citedby:scholar:timestamp:2020-9-4
graphsignalprocessinganalysiscordisdisfunctionalcitedby:scholar:count:2114citedby:scholar:timestamp:2020-9-4
(0)
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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 others. BMJ open 9 (2): e025939 (2019)
3 years ago by @becker
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log
data
maternity
pregnancy
multiomics
longitudinal
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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2DGCA: a comprehensive R package for differential gene correlation analysis
A. McKenzie, I. Katsyv, W. Song, M. Wang, and B. Zhang. BMC systems biology 10 (1): 106 (2016)
3 years ago by @becker
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analysis
citedby:scholar:count:55
citedby:scholar:timestamp:2020-8-2
correlation
dgca
differential
disruption
gene
paper:cordis
paper:fastcor
analysiscitedby:scholar:count:55citedby:scholar:timestamp:2020-8-2correlationdgcadifferentialdisruptiongenepaper:cordispaper:fastcor
(0)
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1Fouille de données médicales: Analyse et prédiction de données sur les maladies cardio-vasculaires
N. Barret, V. Acier, J. Reisser, and N. Medjedel. (20xx)
4 years ago by @becker
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analysis
application
pysubgroup
subgroup
analysisapplicationpysubgroupsubgroup
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2Complex time series analysis of PM10 and PM2. 5 for a coastal site using artificial neural network modelling and k-means clustering
M. Elangasinghe, N. Singhal, K. Dirks, J. Salmond, and S. Samarasinghe. Atmospheric Environment (2014)
6 years ago by @becker
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analysis
citedby:scholar:count:44
citedby:scholar:timestamp:2018-3-13
clustering
k
k-means
kmeans
means
network
neural
nn
p2map
pm10
pm2.5
pm25
timeseries
analysiscitedby:scholar:count:44citedby:scholar:timestamp:2018-3-13clusteringkk-meanskmeansmeansnetworkneuralnnp2mappm10pm2.5pm25timeseries
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3Stylometric analysis of scientific articles
S. Bergsma, M. Post, and D. Yarowsky. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , page 327--337. Association for Computational Linguistics, (2012)
6 years ago by @becker
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analysis
articles
author
citedby:scholar:count:63
citedby:scholar:timestamp:2018-1-10
gender
native
non
nonnative
paper:country
paper:country:cite
profiling
research
scientific
stylometric
writing
analysisarticlesauthorcitedby:scholar:count:63citedby:scholar:timestamp:2018-1-10gendernativenonnonnativepaper:countrypaper:country:citeprofilingresearchscientificstylometricwriting
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7Towards Sentiment Analysis on German Literature
A. Zehe, M. Becker, F. Jannidis, and A. Hotho. Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz) , page 387--394. Springer, (2017)
6 years ago by @becker
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myown
sentiment
analysis
german
literature
myownsentimentanalysisgermanliterature
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3On the lack of typical behavior in the global Web traffic network
M. Meiss, F. Menczer, and A. Vespignani. Proceedings of the 14th international conference on World Wide Web , page 510--518. ACM, (2005)
6 years ago by @becker
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inthesis
diss
web
analysis
related
work
modeling
citedby:scholar:count:35
citedby:scholar:timestamp:2017-8-9
inthesisdisswebanalysisrelatedworkmodelingcitedby:scholar:count:35citedby:scholar:timestamp:2017-8-9
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