Precision annotation of digital samples in NCBI’s gene expression omnibus

Hadley, Dexter; Pan, James; El-Sayed, Osama; Aljabban, Jihad; Aljabban, Imad; Azad, Tej D.; Hadied, Mohamad Omar; Raza, S. Mahmood; Rayikanti, Benjamin Abhishek; Chen, Bin; Paik, Harikrishna; Aran, Dvir; Spatz, Jordan; Himmelstein, Daniel S.; Panahiazar, Maryam; Bhattacharya, Sanchita; Sirota, Marina; Musen, Mark A.; Butte, Atul J.

doi:10.1038/sdata.2017.125

Cited by 45 publications

(53 citation statements)

References 51 publications

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“…STARGEO is a nascent platform for annotating and meta-analyzing differential gene expression experiments ( Hadley et al, 2017 ). The STAR acronym stands for Search-Tag-Analyze Resources, while GEO refers to the Gene Expression Omnibus ( Edgar et al, 2002 ; Barrett et al, 20122013 ).…”

Section: Materials and Methodsmentioning

confidence: 99%

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Himmelstein

Lizée

Hessler

et al. 2017

eLife

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390

387

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The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

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Section: Materials and Methodsmentioning

confidence: 99%

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Himmelstein

Lizée

Hessler

et al. 2017

eLife

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390

387

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“…As a first step, we extracted a group of genes for each skin conditions from Gene expression Omnibus (GEO). We found over 1000s of genes with the Search Tag Analyze Resource for GEO (STARGEO) platform [16]. STARGEO has been explained in our previous work by details [7].…”

Section: Targeting Drugmentioning

confidence: 99%

“…The National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) is an open database of more than 2 million samples of functional genomics experiments. The Search Tag Analyze Resource for GEO (STARGEO) platform allows for meta-analysis of genomic signatures of disease and tissue through tagging of biological samples from multiple experiments [16]. We extracted over 1000s of genes for skin conditions based on studies and samples from GEO with STARGEO platform.…”

Section: Targeting Drugmentioning

confidence: 99%

Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype

Panahiazar¹,

Fadavi²,

Aljabban³

et al. 2018

Informatics

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A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful.

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“…age, sex, and sometimes even tissue of sample origin is missing (i.e GEO [3]). This leads to missing and/or inaccurate annotations, and requires revision and correction by an expert [6]. While state-of-the-art expression databases such as the small RNA Expression Atlas (SEA, http://sea.ims.bio) [10] provide well-structured, ontology-based annotations of publicly available small RNA-seq (sRNA-seq) data, this is achieved by curation of annotations, and missing information is still a problem in many experimental databases.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles

Fiošina

Fiošins

Bonn

2019

Bioinformatics Research and Applications

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The lack of well-structured annotations in a growing amount of RNA expression data complicates data interoperability and reusability. Commonly -used text mining methods extract annotations from existing unstructured data descriptions and often provide inaccurate output that requires manual curation. Automatic data-based augmentation (generation of annotations on the base of expression data) can considerably improve the annotation quality and has not been well-studied. We formulate an automatic augmentation of small RNA-seq expression data as a classification problem and investigate deep learning (DL) and random forest (RF) approaches to solve it. We generate tissue and sex annotations from small RNA-seq expression data for tissues and cell lines of homo sapiens. We validate our approach on 4243 annotated small RNAseq samples from the Small RNA Expression Atlas (SEA) database. The average prediction accuracy for tissue groups is 98% (DL), for tissues -96.5% (DL), and for sex -77% (DL). The "one dataset out" average accuracy for tissue group prediction is 83% (DL) and 59% (RF). On average, DL provides better results as compared to RF, and considerably improves classification performance for 'unseen' datasets.

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Precision annotation of digital samples in NCBI’s gene expression omnibus

Cited by 45 publications

References 51 publications

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype

Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles

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