The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Integrated Analysis of Transcriptomic and Proteomic Data
Open accessAbstract
Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
AFLP: a new technique for DNA fingerprinting
Vos, P., Hogers, R., Bleeker, M., Reijans, M., Lee, T. V. D., Hornes, M., Friters, A., Pot, J., Paleman, J., Kuiper, M., et al. (1995) · Nucleic Acids Research · doi:10.1093/nar/23.21.4407 · Cited by 12,401
Perkins, D. N., Pappin, D. J. C., Creasy, D. M., & Cottrell, J. S. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19991201)20:18<3551::aid-elps3551>3.0.co;2-2 · Cited by 8,203
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Cited by
Understanding nanoparticle endocytosis to improve targeting strategies in nanomedicine
Sousa de Almeida, M., Susnik, E., Drasler, B., Taladriz-Blanco, P., Petri-Fink, A., & Rothen-Rutishauser, B. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01127d · Cited by 1,079
A deep learning model to predict RNA-Seq expression of tumours from whole-slide images
Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17678-4 · Cited by 533
Paving the way to single-molecule protein sequencing
Restrepo-Pérez, L., Joo, C., & Dekker, C. (2018) · Nature Nanotechnology · doi:10.1038/s41565-018-0236-6 · Cited by 396
Perna, F., Berman, S. H., Soni, R. K., Mansilla-Soto, J., Eyquem, J., Hamieh, M., Hendrickson, R. C., Brennan, C. W., & Sadelain, M. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.09.004 · Cited by 346
Kumar, G., Shekh, A., Jakhu, S., Sharma, Y., Kapoor, R., & Sharma, T. R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00914 · Cited by 315
Metadata
- Authors
- Haider, S., & Pal, R.
- Venue
- Current Genomics
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2013-02-01
- Volume
- 14
- Issue
- 2
- Pages
- 91-110
- ISSN
- 1389-2029 (ISSN-L)
- OpenAlex
- W2032964150
- Cited by
- 431
- References
- 126
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Integrated Analysis of Transcriptomic and Proteomic Data. https://app.cochat.ai/papers/10.2174/1389202911314020003
- The paper
- Haider, S., & Pal, R. (2013). Integrated Analysis of Transcriptomic and Proteomic Data. Current Genomics. https://doi.org/10.2174/1389202911314020003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
