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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
2 years ago by @becker
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component
principle
evaluation
critique
analysis
pca
componentprincipleevaluationcritiqueanalysispca
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1Propensity Score Analysis | Columbia Public Health
https://www.publichealth.columbia.edu/research/population-health-methods/propensity-score-analysis
2 years ago by @becker
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score
propensity
tutorial
simple
biomedicine
analysis
explanation
matching
scorepropensitytutorialsimplebiomedicineanalysisexplanationmatching
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1scverse - single cell methods
https://scverse.org/
2 years ago by @becker
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github
software
methods
analysis
single
cell
githubsoftwaremethodsanalysissinglecell
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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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co-occurrence
data
correlation
compositional
positive
analysis
project:fastcor
network
coocurrence
co-occurrencedatacorrelationcompositionalpositiveanalysisproject:fastcornetworkcoocurrence
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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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toread
inference
analysis
pca
varimax
factor
toreadinferenceanalysispcavarimaxfactor
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1Mummichog
https://shuzhao-li.github.io/mummichog.org/
2 years ago by @becker
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metabolomics
python
py
pathway
analysis
metabolites
network
metabolomicspythonpypathwayanalysismetabolitesnetwork
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1zqfang/GSEApy: Gene Set Enrichment Analysis in Python
https://github.com/zqfang/GSEApy
2 years ago by @becker
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enrichment
set
database
gene
python
data
py
api
analysis
base
enrichmentsetdatabasegenepythondatapyapianalysisbase
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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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fast
parallel
gene
correlation
analysis
differential
paper:fastcor
dgca
fastparallelgenecorrelationanalysisdifferentialpaper:fastcordgca
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2Zeppelin
https://zeppelin.apache.org/
5 years ago by @nosebrain
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spark
data
zeppelin
analysis
sql
sparkdatazeppelinanalysissql
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2We analyzed 16,625 papers to figure out where AI is headed next - MIT Technology Review
Our study of 25 years of artificial-intelligence research suggests the era of deep learning may come to an end.
5 years ago by @hotho
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paper
ai
analysis
paperaianalysis
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1Monitoring of swarming sounds in bee hives for early detection of the swarming period - ScienceDirect
https://www.sciencedirect.com/science/article/pii/S0168169908001385
6 years ago by @hotho
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bee
sound
analysis
Bienen
beesoundanalysisBienen
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1Text Analysis Conference (TAC) KBP 2017 Tracks
https://tac.nist.gov/2017/KBP/
6 years ago by @hotho
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2018
relation
text
analysis
track
2018relationtextanalysistrack
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1SANSA-Stack
https://github.com/SANSA-Stack
7 years ago by @thoni
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semantic
spark
analysis
stack
semanticsparkanalysisstack
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1NLP datasets
Data sets I have developed and used in my research.
7 years ago by @schwemmlein
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sentiment
hyponym
semantic
dataset
twitter
data
relation
analysis
extraction
sentimenthyponymsemanticdatasettwitterdatarelationanalysisextraction
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1ICA, Nonlinear Independent Component Analysis, Unsupervised Learning, Redundancy Reduction
http://people.idsia.ch/~juergen/ica.html
7 years ago by @becker
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linear
component
independent
ica
machine
learning
analysis
pca
non
from:hotho
nonlinear
linearcomponentindependenticamachinelearninganalysispcanonfrom:hothononlinear
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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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analytics
algorithms
analysis
network
jure
snap
algorithm
analyticsalgorithmsanalysisnetworkjuresnapalgorithm
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1Sequential Data Analysis Introduction
http://www.orgdna.net/traminer/material/slides/vbm_sequential_day1.pdf
8 years ago by @becker
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sequence
overview
sequential
diss
analysis
sequenceoverviewsequentialdissanalysis
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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
8 years ago by @becker
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induction
inductive
citable
reasoning
deduction
deductive
analysis
research
inductioninductivecitablereasoningdeductiondeductiveanalysisresearch
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1Deduction & Induction
http://www.socialresearchmethods.net/kb/dedind.php
8 years ago by @becker
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induction
inductive
reasoning
deduction
deductive
analysis
research
inductioninductivereasoningdeductiondeductiveanalysisresearch
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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/
8 years ago by @becker
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induction
inductive
reasoning
deduction
deductive
analysis
research
inductioninductivereasoningdeductiondeductiveanalysisresearch
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publications (hide)217
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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)
2 years ago by @becker
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correlation
citedby:scholar:count:142
analysis
citedby:scholar:timestamp:2022-9-18
differential
paper:fastcor
correlationcitedby:scholar:count:142analysiscitedby:scholar:timestamp:2022-9-18differentialpaper:fastcor
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1The discordant method: a novel approach for differential correlation
C. Siska, R. Bowler, and K. Kechris. Bioinformatics, 32 (5): 690--696 (2016)
2 years ago by @becker
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correlation
citedby:scholar:count:28
analysis
citedby:scholar:timestamp:2022-9-18
differential
paper:fastcor
correlationcitedby:scholar:count:28analysiscitedby:scholar:timestamp:2022-9-18differentialpaper:fastcor
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1DCARS: differential correlation across ranked samples
S. Ghazanfar, D. Strbenac, J. Ormerod, J. Yang, and E. Patrick. Bioinformatics, 35 (5): 823--829 (2019)
2 years ago by @becker
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correlation
citedby:scholar:count:5
analysis
citedby:scholar:timestamp:2022-9-18
differential
paper:fastcor
correlationcitedby:scholar:count:5analysiscitedby:scholar:timestamp:2022-9-18differentialpaper:fastcor
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1DINGO: 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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citedby:scholar:count:106
m:id:test2:node
citedby:scholar:timestamp:2022-4-27
analysis
m:id:test2:coordinate:y:-49.76913261413574
m:
m:id:test2:coordinate:x:177.40279006958008
network
differential
paper:fastcor
citedby:scholar:count:106m:id:test2:nodecitedby:scholar:timestamp:2022-4-27analysism:id:test2:coordinate:y:-49.76913261413574m:m:id:test2:coordinate:x:177.40279006958008networkdifferentialpaper:fastcor
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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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citedby:scholar:count:3
citedby:scholar:timestamp:2022-4-27
analysis
network
differential
paper:fastcor
citedby:scholar:count:3citedby:scholar:timestamp:2022-4-27analysisnetworkdifferentialpaper:fastcor
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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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citedby:scholar:count:13
citedby:scholar:timestamp:2022-4-27
analysis
network
differential
paper:fastcor
citedby:scholar:count:13citedby:scholar:timestamp:2022-4-27analysisnetworkdifferentialpaper:fastcor
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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
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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)
3 years ago by @becker
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nalab
citedby:scholar:count:98
citedby:scholar:timestamp:2022-1-27
um1
pathway
analysis
nalabcitedby:scholar:count:98citedby:scholar:timestamp:2022-1-27um1pathwayanalysis
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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)
3 years ago by @becker
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nalab
citedby:scholar:timestamp:2022-1-27
citedby:scholar:count:53
um1
pathway
analysis
nalabcitedby:scholar:timestamp:2022-1-27citedby:scholar:count:53um1pathwayanalysis
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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)
3 years ago by @becker
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literature
exploration
virtual
m:id:immermind:coordinate:x:52.87889862060547
reality
m:id:immermind:coordinate:y:-250.97742080688477
m:id:immermind:node
vr
analysis
immermind
m:
literatureexplorationvirtualm:id:immermind:coordinate:x:52.87889862060547realitym:id:immermind:coordinate:y:-250.97742080688477m:id:immermind:nodevranalysisimmermindm:
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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)
3 years ago by @becker
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virtual
citedby:scholar:count:140
reality
data
analysis
immermind
m:
plots
citedby:scholar:timestamp:2022-1-11
m:id:immermind:coordinate:y:134.9595603942871
m:id:immermind:node
vr
m:id:immermind:coordinate:x:-378.8338394165039
virtualcitedby:scholar:count:140realitydataanalysisimmermindm:plotscitedby:scholar:timestamp:2022-1-11m:id:immermind:coordinate:y:134.9595603942871m:id:immermind:nodevrm:id:immermind:coordinate:x:-378.8338394165039
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2A Survey on Sentiment and Emotion Analysis for Computational Literary Studies
E. Kim, and R. Klinger. Zeitschrift für digitale Geisteswissenschaften, (2016)
3 years ago by @schwemmlein
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sentiment
nlp
emotion
analysis
digital_humanities
sentimentnlpemotionanalysisdigital_humanities
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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)
4 years ago by @becker
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functional
distributed
citedby:scholar:count:15
analysis
matrix
fmri
paper:fastcor
brain
parallel
connectivity
citedby:scholar:timestamp:2021-1-7
correlation
full
fcma
functionaldistributedcitedby:scholar:count:15analysismatrixfmripaper:fastcorbrainparallelconnectivitycitedby:scholar:timestamp:2021-1-7correlationfullfcma
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5Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study
P. Davidson, P. Düking, C. Zinner, B. Sperlich, and A. Hotho. Sensors, (May 2020)
4 years ago by @hotho
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myown
smartwatch
timeseries
rpe
analysis
2020
myownsmartwatchtimeseriesrpeanalysis2020
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1What they do in the shadows: examining the far-right networks on Telegram
A. Urman, and S. Katz. Information, Communication & Society, 0 (0): 1-20 (2020)
4 years ago by @schwemmlein
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telegram
antrag
deconspire
far-right
analysis
network
graph
telegramantragdeconspirefar-rightanalysisnetworkgraph
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2Analysis of Telegram, An Instant Messaging Service
A. Dargahi Nobari, N. Reshadatmand, and M. Neshati. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, page 2035–2038. New York, NY, USA, Association for Computing Machinery, (2017)
4 years ago by @schwemmlein
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detection
telegram
messaging
antrag
deconspire
analysis
network
pagerank
spam
graph
instant
classification
detectiontelegrammessagingantragdeconspireanalysisnetworkpagerankspamgraphinstantclassification
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1The 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)
4 years ago by @becker
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signal
functional
cordis
processing
analysis
citedby:scholar:count:2114
citedby:scholar:timestamp:2020-9-4
dis
graph
signalfunctionalcordisprocessinganalysiscitedby:scholar:count:2114citedby:scholar:timestamp:2020-9-4disgraph
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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
monitoring
activity
complications
pregnancy
data
analysis
fetal
citedby:scholar:count:4
maternity
multiomics
prediction
citedby:scholar:timestamp:2020-8-11
lifelog
outcome
longitudinal
logmonitoringactivitycomplicationspregnancydataanalysisfetalcitedby:scholar:count:4maternitymultiomicspredictioncitedby:scholar:timestamp:2020-8-11lifelogoutcomelongitudinal
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1DGCA: 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)
4 years ago by @becker
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disruption
gene
citedby:scholar:count:55
correlation
paper:cordis
citedby:scholar:timestamp:2020-8-2
analysis
differential
paper:fastcor
dgca
disruptiongenecitedby:scholar:count:55correlationpaper:cordiscitedby:scholar:timestamp:2020-8-2analysisdifferentialpaper:fastcordgca
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2Beyond canonical texts: A computational analysis of fanfiction
S. Milli, and D. Bamman. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, page 2048--2053. (2016)
4 years ago by @schwemmlein
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literature
nlp
fiction
character
fan
analysis
network
literaturenlpfictioncharacterfananalysisnetwork
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