Increased Time and Cost of mining PubMed everyday

www.xtractor.in/premium

Researchers are benefited to the maximal when accurate content from Biomedical Literature is delivered on a real time basis. We estimated the amount of time, effort & cost that is involved in mining PubMed for the relevant Biomedical facts every single day.

We performed a search on PubMed for Breast Neoplasm over different time spans:

We then estimated the time that is required by a normal human annotator to pick & categorize the relevant sentences from each of these abstracts and annotate the sentences for Protein, Diseases, Drugs or Biological Processes.

We found to annotate 30 days of data on breast neoplasm it would take at least 1man day (taking into account from our past estimates that it would at least take 10 min to annotate one abstract). So extrapolating the amount of time required to annotate data on a single Search “ Breast Neoplasm” for 3 months or 90 days happens to be 11 man-days of effort and considerable cost.

The same would imply if you were to read and analyze the facts yourself.

So in order to annotate 50000 abstracts (it would amount to 50000 abstracts x 10 mins) = 500000 mins or 1041 man-days or ~ 3 years to annotate 1 month of literature findings from PubMed.

Solution:

The major bottleneck with manual curation as demonstrated above involves considerable time and cost.  So in XTractor we have reduced the time involved in the manual annotation effort- by significantly cutting down the process steps to boost our internal productivity and turnaround time. So that in almost real-time basis we are able to serve you with the latest manually annotated scientific facts every single day.

ROI:

At XTractor we do manual annotation of more than 700 abstracts everyday that too at < $1 per day.

XTractor delivers you the handpicked, manually annotated facts within 10 days from the date of publication from PubMed.

FREE Trial at  http://www.xtractor.in/premium/trial.do

text mining, manual annotation, data alerts, colloborations, pubmed, curation, genes, drugs, processes, diseases, free, data mining, tag, annotations, drug discovery, web 2.0, biomedical literature, publishing, abstracts, natural language processing, NLP, data analysis, visualization, concept linking, abstraction, categorization, precision, recall, data accuracy, proteins, interactions, molecules, text gathering, indexing, index, query, MeSH, biological process, protein function, NLM, accuracy, accurate data, manual curation, curate, annotate, annotations, colloborations, curation, data alerts, data mining, diseases, drug discovery, drugs, free, genes, manual annotation, processes, pubmed, tag, text mining,text mining, manual annotation, data alerts, pubmed, genes, drugs, processes, diseases, free, data mining, tag, drug discovery, web 2.0, natural language processing, data analysis, visualization, concept linking, abstraction, precision, recall, data accuracy, proteins, interactions, index, query, MeSH, NLM, manual curation, protein interactions, abstraction, abstracts, accuracy, accurate, data, annotations, biological process, biomedical literature, categorization, colloborations, concept linking, curate, curation, data accuracy, data alerts, literature, categorization, colloborations, concept linking curate curation data accuracy data alerts da, abstracts, annotations, biological process, categorization, colloborations, data accuracy, data analysis, data mining, drug discovery, MeSH, molecules, natural language processing, NLM, NLP, precision, processes, protein function, protein interactions, publishing, pubmed, query, recall, tag, text gathering, visualization, web 2.0