MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

Abstract

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

“…in the discussion section of the paper or whether there is a results figure, which gives firm proof of the interaction occurring according to experimental data. For such tasks, our tool ( 122 ) can ideally be suited, as it readily distinguishes and mines separately text from the main article and distinguishes it from figure legends and concrete results. Data mining tools and databases, which strongly profit from such data extraction tools to separate and distinguish among images, legends and text, include, for instance the i-HOP, where a separation between information ‘extracted from a text part’ and ‘extracted from a image part/so from original data’ is powerful and meaningful.…”

“…a database storing and linking molecular data with images would be highly desirable. This can only be achieved and established if first the mixture of text, protocols and omics data is properly separated from images, figures and figure legends—again a task for which our tool ( 122 ) is perfectly suited. As, for the different use-cases and databases for which such approaches can be applied to illustrate that there are a number of situations where such tools are very useful.…”

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

“… Raw dataset is attached to this manuscript, which categorically provides all images and text in XML format, extracted from manuscripts (from different publishers (included in file names)) using MSL 37 . …”

“…F1000Research : Dataset 1. Extracted Images and Text from Papers tested using MSL, 10.5256/f1000research.7329.d108739 37 …”