iPAS statistics

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Citations per year

Number of citations per year for the bioinformatics software tool iPAS

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iPAS specifications


Unique identifier OMICS_25551
Name iPAS
Alternative name individualized Pathway Aberrance Score
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Taesung Park
  • person_outline Nam Huh
  • person_outline TaeJin Ahn

Publication for individualized Pathway Aberrance Score

iPAS citations


Personalized analysis of pathway aberrance induced by sevoflurane and propofol

PMCID: 5647075
PMID: 28849088
DOI: 10.3892/mmr.2017.7305

[…] Pathway statistics of anesthetic-treated samples and normal samples were obtained by using the method of iPAS. The pathway statistics of the pathways in which most DEGs were participating were displayed in and to illustrate the difference between the two groups. presents pathway statistics for each of […]


Personalized identification of differentially expressed pathways in pediatric sepsis

PMCID: 5647041
PMID: 28849000
DOI: 10.3892/mmr.2017.7217

[…] the number of correlated pathways in the individual pathway analysis.The majority of current pathway analyses have been developed to investigate deregulated pathways between two phenotype groups. The individualized pathway aberrance score (iPAS) method is straightforward to compare the expression profiles of an individual disease and normal cells to identify molecular changes that are specific to […]


Identification of altered pathways in breast cancer based on individualized pathway aberrance score

PMCID: 5529805
PMID: 28789343
DOI: 10.3892/ol.2017.6292

[…] d pathways, making special use of accumulated normal data in cases when a patient's matched normal data were unavailable ().The present study identified altered pathways in breast cancer based on the individualized pathway aberrance score (iPAS) method which included data preprocessing, gene-level statistics, pathway-level statistics and a significant test. The altered pathways were validated by c […]


Personalized Drug Analysis in B Cell Chronic Lymphocytic Leukemia Patients

PMCID: 5432060
PMID: 28477439
DOI: 10.12659/MSM.900738

[…] Pathway level statistics in individual pathway of all samples were obtained using iPAS. Normal distribution analysis was performed in pathway level statistics of disease samples after combining a disease case with all the normal samples. P-values of pathways in each disease sample […]


Pathway based detection of idiopathic pulmonary fibrosis at an early stage

PMCID: 5364974
PMID: 28260097
DOI: 10.3892/mmr.2017.6274

[…] seful in identifying cancer from unknown samples. A number of methods have been proposed to identify differential pathways, including the attract method (), personal pathway deregulation score () and individualized pathway aberrance score (). Personalized identification of differential pathways provides pathway interpretation in a single sample with accumulated normal data.Support vector machines […]


Personalized Analysis by Validation of Monte Carlo for Application of Pathways in Cardioembolic Stroke

PMCID: 5338568
PMID: 28232661
DOI: 10.12659/MSM.899690

[…] ication of custom therapeutic decisions. Existing pathway analysis methods are not suitable for identifying the pathway aberrance that may occur in an individual sample []. Therefore, we employed the iPAS to analyze the personalized identification of networks, taking advantage of a vast number of normal samples.A key innovation of the method is iPAS using ANS in CES. Ahn et al. [] proved that the […]

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iPAS institution(s)
Samsung Advanced Institute of Technology, Suwon, South Korea; Samsung Genome Institute, Seoul, South Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea; Department of Statistics, Seoul National University, Seoul, South Korea
iPAS funding source(s)
Supported by the National Research Foundation of Korea (NRF) grant (2012R1A3A2026438) and by the Bio & Medical Technology Development Program of the NRF grant (2013M3A9C4078158).

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