You are here

Home

Text Mining

Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. [Source: Wikipedia ]

miRTex

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations.

Rating: 
Average: 5 (1 vote)

tfmirloop

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods.

Rating: 
Average: 5 (1 vote)

miRiaD (Text Mining Tool)

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field.

Rating: 
4
Average: 4 (2 votes)

ODIN-bc2015-miRNA

Submitted by ChenLiang on Sun, 09/10/2017 - 20:02

MicroRNAs (miRNAs) are small and non-coding RNA molecules that inhibit gene expression posttranscriptionally. They play important roles in several biological processes, and in recent years there has been an interest in studying how they are related to the pathogenesis of diseases. Although there are already some databases that contain information for miRNAs and their relation with illnesses, their curation represents a significant challenge due to the amount of information that is being generated every day.

Rating: 
Average: 5 (1 vote)

T2DiACoD

Submitted by ChenLiang on Sun, 09/10/2017 - 20:16

We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications.

Rating: 
Average: 5 (1 vote)

COGERE

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components.

Rating: 
Average: 5 (1 vote)

miCancerna

Submitted by ChenLiang on Tue, 01/09/2018 - 19:19

Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers.

Rating: 
5
Average: 5 (2 votes)

Structuring osteosarcoma knowledge

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers.

Rating: 
Average: 5 (1 vote)

miRLiN

Submitted by ChenLiang on Mon, 01/09/2017 - 10:21

BACKGROUND: The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. RESULTS: For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs.

Rating: 
Average: 5 (1 vote)

GEAR

Submitted by ChenLiang on Thu, 04/06/2017 - 17:49

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance.

Rating: 
Average: 5 (1 vote)

Pages

Subscribe to Text Mining