Gene expression analysis identifies global gene dosage sensitivity in cancer

Fehrmann, Rudolf S.N.; Karjalainen, Juha; Krajewska, Małgorzata; Westra, Harm-Jan; Maloney, David J.; Simeonov, Anton; Pers, Tune H.; Hirschhorn, Joel N.; Jansen, Ritsert C.; Schultes, Erik; Haagenl, Herman H. H. B. M. Van; Vries, Elisabeth G.E. de; Meerman, Gerard J. te; Wijmenga, Cisca; Vugt, Marcel A.T.M. van; Franke, Lude

doi:10.1038/ng.3173

Cited by 306 publications

(331 citation statements)

References 35 publications

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“…Biomedical text mining is an important tool to support large-scale biomedical data analysis, such as biomedical network construction [Zhou et al, 2014], gene prioritization [Aerts et al, 2006, Liu et al, 2015, drug repositioning [Wang and Zhang, 2013], finding literature support of experimental findings [Morris et al, 2012, Willer et al, 2013, Fehrmann et al, 2015, generating hypothesis [Zhou et al, 2014, Al-Aamri et al, 2017, Rastegar-Mojarad et al, 2015 and database curation [Li et al, 2015]. The most fundamental task in biomedical text mining is biomedical named entity recognition (BioNER) that automatically recognizes and extracts biomedical entities (e.g., genes, proteins, chemicals and diseases) from text [Jensen et al, 2006, Rebholz-Schuhmann et al, 2012.…”

Section: Introductionmentioning

confidence: 99%

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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Motivation: Biomedical named entity recognition (BioNER) is the most fundamental task in biomedical text mining. State-of-the-art BioNER systems often require handcrafted features specifically designed for each type of biomedical entities. This feature generation process requires intensive labors from biomedical and linguistic experts, and makes it difficult to adapt these systems to new biomedical entity types. Although recent studies explored using neural network models for BioNER to free experts from manual feature generation, these models still require substantial human efforts to annotate massive training data. Results: We propose a multi-task learning framework for BioNER that is based on neural network models to save human efforts. We build a global model by collectively training multiple models that share parameters, each model capturing the characteristics of a different biomedical entity type. In experiments on five BioNER benchmark datasets covering four major biomedical entity types, our model outperforms state-ofthe-art systems and other neural network models by a large margin, even when only limited training data are available. Further analysis shows that the large performance gains come from sharing character-and word-level information between different biomedical entities. The approach creates new opportunities for text-mining approaches to help biomedical scientists better exploit knowledge in biomedical literature. Availability: The source code for our models is available at https://github.com/yuzhimanhua/lm-lstm-crf, and the corpora are available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016.

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Section: Introductionmentioning

confidence: 99%

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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“…For a detailed description of FGmRNAprofiling, we refer to Fehrmann et al (4). In short, we analyzed 77,840 expression profiles of publicly available samples with principal component analysis and found that a limited number of transcriptional components capture the major regulators of the messenger RNA transcriptome.…”

Section: Methodsmentioning

confidence: 99%

“…To this end, we used the recently developed method of functional genomic messenger RNA (FGmRNA) profiling to predict overexpression of target antigens on the protein level (4). FGmRNA profiling is capable of correcting a gene expression profile of an individual tumor for physiologic and experimental factors, which are considered not to be relevant for the observed tumor phenotype and characteristics.…”

mentioning

confidence: 99%

Data-Driven Prioritization and Review of Targets for Molecular-Based Theranostic Approaches in Pancreatic Cancer

et al. 2017

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Molecularly targeted therapeutic and imaging strategies directed at aberrant signaling pathways in pancreatic tumor cells may improve the poor outcome of pancreatic ductal adenocarcinoma (PDA). Therefore, relevant molecular targets need to be identified. Methods: We collected publicly available expression profiles of patient-derived normal pancreatic tissue (n 5 77) and PDA samples (n 5 103). Functional genomic messenger RNA profiling was applied to predict target upregulation on the protein level. We prioritized these targets based on current status of preclinical therapeutic and imaging evaluation in PDA. Results: We identified 213 significantly upregulated proteins in PDA compared with normal pancreatic tissue. We prioritized mucin-1, mesothelin, g-glutamyltransferase 5, and cathepsin-E as the most interesting targets, because studies already demonstrated their potential for both therapeutic and imaging strategies in literature. Conclusion: This study can assist clinicians and drug developers in deciding which theranostic targets should be taken for further clinical evaluation in PDA.

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“…Many genes and proteins are diferentially expressed in tumor tissue compared to nontumor tissue [74][75][76][77]. Thus, it is intuitive that faty acid proiles are likely to be altered in tumors compared to nontumor tissue and this has indeed been demonstrated in breast and prostate cancer [78,79].…”

Section: Cancer Associations With Faty Acidsmentioning

confidence: 99%

Fatty Acids in Veterinary Medicine and Research

Simpson¹,

Mostyn²

2017

Fatty Acids

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Faty acid regulation is an essential process for all animals. A number of studies have shown that diet afects the levels/availability of faty acids in the body but increasingly an evidence shows that disease states can alter the amounts within the body too. Faty acid levels and availability have been altered by a number of diseases, disorders and reactions including inlammatory responses, heart disease and heart failure and wound repair. They are also essential during the growth and development stages of animals. The amount of research into the consequences of diferent faty acid intake and levels in various disease states and during development has increased in both humans and animals. This review presents an overview of the research undertaken to date and highlights the importance, uses and beneits of understanding the roles of faty acids in both the healthy animals and animals under difering disorders and diseases.

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Gene expression analysis identifies global gene dosage sensitivity in cancer

Cited by 306 publications

References 35 publications

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Data-Driven Prioritization and Review of Targets for Molecular-Based Theranostic Approaches in Pancreatic Cancer

Fatty Acids in Veterinary Medicine and Research

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