Supplemental Book Resources for:

• Author Information
  
  • Contact Jules J. Berman by email.
    
  • Long list of all publications, and short list of PubMed citations for Jules J. Berman.
    
  • Jules J. Berman's blog on data specifications
    
    
• Corrections site for Ruby Programming for Medicine and Biology
  
  
• Corrections site for Perl Programming for Medicine and Biology
  
  
• Corrections site for Biomedical Informatics
  
  
• Corrections site for The Ruby Programming Language
  
  
• Corrections site for The Perl Programming Language
  
  
• Corrections site for Neoplasms: Principles of Development and Diversity
  
  
• Approximately 240 lists from Biomedical Informatics
  
  
• Phylogeny (class hierarchy) construction for any of over 400,000 organisms.
  
  
• White paper: Jules J. Berman and G. William Moore. Implementing an RDF Schema for Pathology Images
  September 10, 2007
  
  
• Developmental Lineage Neoplasms Classfication (now in RDF ontology format).
  
  The RDF file is about 10 Megabytes and is so large that some browsers cannot parse the entire file, with all of its child nodes. I am providing the file as a simple text file. This way your browser will not try parse the nodal hierarchy. Whenever you want to parse the file as an RDF document, you can just rename the file "neordf.xml".
  http://www.julesberman.info/neordf.txt
  
  The file was validated using the W3C validator service at http://www.w3.org/rdf/validator/, with a caveat. The full ontology file (10+ Mbytes) was too large for the validator, so I truncated the ontology, validated the truncated file (that contained all of the classes, subclasses, properties), and left out the repetitive list of terms. Then I took the entire file and validated it with an XML parser to verify that the file was well-formed). That really covers everything (RDF semantics and XML structure).
  
  The gzipped version of the RDF file (under 1 Megabyte)
  http://www.julesberman.info/neorxml.gz
  
  The flat file version, listing each term followed by its lineage (gzipped file).
  http://www.julesberman.info/neoself.gz
  
  The plain old XML version, with no RDF semantics (compressed gzip file).
  http://www.julesberman.info/neoclxml.gz
  
  The plain old XML version, with no RDF semantics (compressed zip file).
  http://www.julesberman.info/neoclxml.zip
  
  The ontology contains several parts, including the neoplasms classification proper (illustrated in the schematic below).
  
  
  
  In this version (October 27, 2007), there are 5841 classified types of neoplasms and 130,503 terms representing the 5,841 types of neoplasms.
  
  This version of the neoplasms classification represents the largest nomenclature of neoplasms and, with today's publication, the largest formal ontology (in RDF syntax) of neoplasm names.
  
  
• Parsable Doublets List
  
  Word doublets are two-word phrases that appear in text (i.e., they are not randomly chosen two-word sequences.)
  
  Doublets can be used in a variety of informatics projects: indexing, data scrubbing, nomenclature curation, etc.
  
  The available public domain doublet list was generated from a large narrative pathology text. Thus the doublets included here would be particularly suitable for informatics projects involving surgical pathology reports, autopsy reports, pathology papers and books, and so on.
  
  
• De-identifier for confidential medical records.
  
  A scrubber (deidentification) use-case for the doublets list is available. It consists of scrubbed output for over 15000 citations of pathology papers (from PubMed)
  
  A slightly more complex scrubber (deidentification) that preserves punctuation in the original narrative textuse.
  
  A scrubber (deidentification) that parses a public domain book.
  
  The scrubber, which is distributed under a GNU license, uses the doublet method (described in all two of my previously published books). It parses through any text, matching doublets from the text against an external identifier-free doublet list, preserving all matching doublets from the text, and blocking all non-matching words with an asterisk. If your list of doublets contains no identifiers, the scrubbed output should be perfectly de-identified. Though perfection can never be guaranteed, I have never encountered any "missed" identifiers in a text that was parsed under these conditions.
  
  The doublet scrubber is small (just a few dozen lines of code) and fast. It took approximately 2 seconds to parse the 15000 citations using a Perl script with access to a list of about 200,000 identifier-free doublets. I used my home computer (2.8 GHz, 512 MByte RAM). This is a scrubbing rate of 1 MegaByte per second. At this speed, a 1 GByte file could be parsed in about 15 minutes. It can parse a 1 Terabyte file in about a week. Large hospitals produce about 1 Terabyte of data each week, so this scrubber can, for now, "keep up" with the vast load of data produced by many hospital on a modest desktop computer.
  
  The only limitation that I have found for the doublet scrubber is that it scrubs too much, blocking all doublets not found in the external doublet list. You can be the judge by studying the scrubbed output files:
  
  Over 15000 PubMed Citations, scrubbed
  
  Anomalies and Curiosities of Medcine (scrubbed output)
  
  
• Medical Autocoding: A Ruby medical autocoder , a Perl medical autocoder and a Python medical autocoder.
  
  
• Prime number generation: A Ruby prime number generator , a Perl prime number generator and a Python prime number generator.
  
  
• A phylogeny annotator for taxonomy.dat, the European Bioinformatics Institute's 100+ Mbyte list of species, in Ruby , Perl, and Python.
  
  
• A MeSH Tree (more precisely MeSH ontology) climber in Perl , which produces a full listing of the term relationships for every entry in MeSH (the U.S. National Library of Medicine's Medical Subject Headings).
  
  
• Combined Autocoded and Scrubbed output for 95,260 PubMed Citations (computed in under a minute)
  
  
• Berman JJ. Nomenclature-based data retrieval without prior annotation: facilitating biomedical data integration with fast doublet matching.
  In Silico Biology 5, 0029 (2005)
  
  
• Approximately 18,000 biomedical factoids (distributed in 180 information pages).
  
  
• Over 12,000 biomedical abbreviations.
  
  
• Chronology of Earth (important events in the history of science, medicine, informatics, and society).
  
  
• Common misspellings in medicine and pathology.
  
  
• Confusing terms in medicine and pathology.
  
  
• Specified Life Blog: Devoted to the topic of data specification (including data organization, data description, data retrieval and data sharing) in the life sciences and in medicine.
  
  
• Perl script, omim_4.pl extracts classified neoplasm terms from OMIM (Online Mendelian Inheritance in Man). Described in Chapter 19, of Perl Programming for Medicine and Biology
  
  
• Ruby Scripts
  • Basics of image display in Ruby
  • Using RMagick, Ruby's interface to ImageMagick
  • Creating and displaying a simple image in Ruby using Tk and RMagick.
  • Extracting and displaying a section of a simple image in Ruby using Tk and RMagick.
  • Ruby script extracts a hierarchy for each individual organism listed in taxonomy.dat
  • Ruby script, embryo.rb for building an anatomic hierarchy from an embryological nomenclature
  • Ruby script, combo.rb for autocoding narrative medical text.
  • Another Ruby medical autocoder.
  • A prime number generator in Ruby.