Literature DB >> 34236669

Integration of Proteomics and Other Omics Data.

Mengyun Wu1, Yu Jiang2, Shuangge Ma3.   

Abstract

In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.

Entities:  

Keywords:  Data integration; High-dimensional statistics; Unsupervised and supervised analysis

Mesh:

Year:  2021        PMID: 34236669     DOI: 10.1007/978-1-0716-1641-3_18

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  59 in total

Review 1.  Integrative methods for analyzing big data in precision medicine.

Authors:  Vladimir Gligorijević; Noël Malod-Dognin; Nataša Pržulj
Journal:  Proteomics       Date:  2016-03       Impact factor: 3.984

2.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.

Authors:  Daniela M Witten; Robert Tibshirani; Trevor Hastie
Journal:  Biostatistics       Date:  2009-04-17       Impact factor: 5.899

3.  Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

Authors:  Siegfried Gessulat; Tobias Schmidt; Daniel Paul Zolg; Patroklos Samaras; Karsten Schnatbaum; Johannes Zerweck; Tobias Knaute; Julia Rechenberger; Bernard Delanghe; Andreas Huhmer; Ulf Reimer; Hans-Christian Ehrlich; Stephan Aiche; Bernhard Kuster; Mathias Wilhelm
Journal:  Nat Methods       Date:  2019-05-27       Impact factor: 28.547

4.  ProHits-viz: a suite of web tools for visualizing interaction proteomics data.

Authors:  James D R Knight; Hyungwon Choi; Gagan D Gupta; Laurence Pelletier; Brian Raught; Alexey I Nesvizhskii; Anne-Claude Gingras
Journal:  Nat Methods       Date:  2017-06-29       Impact factor: 28.547

5.  A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival.

Authors:  Ling Zhou; Lu Tang; Angela T Song; Diane M Cibrik; Peter X-K Song
Journal:  Stat Biosci       Date:  2016-10-03

Review 6.  A census of human cancer genes.

Authors:  P Andrew Futreal; Lachlan Coin; Mhairi Marshall; Thomas Down; Timothy Hubbard; Richard Wooster; Nazneen Rahman; Michael R Stratton
Journal:  Nat Rev Cancer       Date:  2004-03       Impact factor: 60.716

7.  Log-ratio lasso: Scalable, sparse estimation for log-ratio models.

Authors:  Stephen Bates; Robert Tibshirani
Journal:  Biometrics       Date:  2019-03-29       Impact factor: 1.701

8.  Proteomic maps of breast cancer subtypes.

Authors:  Stefka Tyanova; Reidar Albrechtsen; Pauliina Kronqvist; Juergen Cox; Matthias Mann; Tamar Geiger
Journal:  Nat Commun       Date:  2016-01-04       Impact factor: 14.919

9.  A multivariate approach to the integration of multi-omics datasets.

Authors:  Chen Meng; Bernhard Kuster; Aedín C Culhane; Amin Moghaddas Gholami
Journal:  BMC Bioinformatics       Date:  2014-05-29       Impact factor: 3.169

10.  Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets.

Authors:  Kevin Drew; Christian L Müller; Richard Bonneau; Edward M Marcotte
Journal:  PLoS Comput Biol       Date:  2017-10-12       Impact factor: 4.475

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