Extracting human protein interactions from MEDLINE using a full-sentence parser

Daraselia, Nikolai; Yuryev, Anton; Egorov, Sergei; Novichkova, Svetalana; Nikitin, Alexander P.; Mazo, Ilya

doi:10.1093/bioinformatics/btg452

Cited by 203 publications

(149 citation statements)

References 12 publications

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“…For this specific purpose we use the information contained in the ResNet mammalian database from Ariadne Genomics (http://www.ariadnegenomics.com/) (Novichkova et al, 2003;Daraselia et al, 2004). We selected only the interactions included in the category of Promoter Binding and Direct Regulation.…”

Section: Gene Regulatory Network Reconstructionmentioning

confidence: 99%

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

Liu

Plongthongkum³

et al. 2013

Protein Cell

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With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT + ) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types.

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Section: Gene Regulatory Network Reconstructionmentioning

confidence: 99%

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

Liu

Plongthongkum³

et al. 2013

Protein Cell

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“…For instance, Ramani et al developed a text mining pipeline of machine learning and natural language processing methods to predict human protein-protein interactions from MedLine abstracts, and recovered a network with comparable accuracy to existing PPI networks [90]. The MedScan information extraction system is a similar predictor that involves a natural language parser for full sentences [91].…”

Section: Text Miningmentioning

confidence: 99%

Domain‐mediated protein interaction prediction: From genome to network

et al. 2012

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a b s t r a c tProtein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence-and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.

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“…26 were obtained from the human protein interaction databases of BIND, HPRD and ResNet. [27][28][29] The most highly connected proteins were chosen as targets for further analysis.…”

Section: Patient Selectionmentioning

confidence: 99%

Proof concept for clinical justification of network mapping for personalized cancer therapeutics

et al. 2007

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To identify signature targets associated with patient-specific cancer lesions based on tumor versus normal tissue differential protein and mRNA coexpression patterns for the purpose of synthesizing cancer-specific customized RNA interference knockdown therapeutics. Analysis of biopsied tissue involved two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis coupled with MALDI-TOF/TOF mass spectrometry for proteomic assessment. Standard microarray techniques were utilized for mRNA analysis. Priority was assigned to overexpressed protein targets with co-overexpressed genes with a high likelihood of functional nodal centrality in the cancer network as defined by the interactive databases BIND, HPRD and ResNet. HPLC-grade small interfering RNA (siRNA) duplexes were utilized to assess knockdown of target proteins in expressive cell lines as measured by western blot. Seven patients with metastatic cancer underwent biopsy. One patient (RW001) had biopsies from two disease sites 10 months apart. Seven priority proteins were identified, one for each patient (RACK 1, Ras related nuclear protein, heat-shock 27 kDa protein 1, superoxide dismutase, enolase1, stathmin1 and cofilin1). Prioritized proteins in RW001 from the two disease sites over time were the same. We demonstrated 480% siRNA inhibition of RACK 1 and stathmin1 of inexpressive malignant cell lines with correlated cell kill. Identification of functionally relevant target gene fingerprints, unique to an individual's cancer, is feasible 'at the bedside' and can be utilized to synthesize siRNA knockdown therapeutics. Further animal safety testing followed by clinical study is recommended.

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Extracting human protein interactions from MEDLINE using a full-sentence parser

Abstract: MedScan is available for commercial licensing from Ariadne Genomics, Inc.

Cited by 203 publications

References 12 publications

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

Domain‐mediated protein interaction prediction: From genome to network

Proof concept for clinical justification of network mapping for personalized cancer therapeutics

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