Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. One of the critical problems in mining tabular data is how to understand their. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. This change shows both interesting qualitative properties that follow our. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. The solution has an intuitive visual project design, it is easy-to-use and makes data mining a quick and hassle-free process. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. About FMiner FMiner is a data extraction tool that helps to collect variable data from multiple web sources. It is an easy to use web data extraction tool that combines best-in-class features with an intuitive visual project design tool, to make your next data mining project a breeze. # If document has instances of \xa0 replace them with spaces.Defective complement activation has been associated with various types of kidney disease. FMiner is a software for web scraping, web data extraction, screen scraping, web harvesting, web crawling and web macro support for windows and Mac OS X. Request = (url, data=None, headers=headers) I solved it as follows: from io import StringIO, BytesIOįrom pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManagerĭef extract_text_from_pdf_url(url, user_agent=None): I have found other questions on this, but nothing I can make work - possibly because they tend to be quite old.)Īny help would be greatly appreciated! Thank you! I'm new to Python, so please bear that in mind (P.s.
I have no idea how I need to amend the "with open" logic to call from a remote url, nor am I sure which request library I would be best using for the latest version of Python (requests, urllib, urllib2, etc.?)
This works (yay!), but what I really want to do is request the pdf directly, via its url, rather than open a pdf that has been pre-saved to a local drive. Page_interpreter = PDFPageInterpreter(resource_manager, converter) Using the information found here: Exporting Data from PDFs with Python, I have the following code: import ioįrom nverter import TextConverterįrom pdfminer.pdfinterp import PDFPageInterpreterįrom pdfminer.pdfinterp import PDFResourceManagerĬonverter = TextConverter(resource_manager, fake_file_handle)
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