Asking the scientific literature to tell us about metabolismpetermurrayrust
Talk to Lhasa Ltd (a world leader in predicting drug metabolism and toxicity. Uses the scientific literature to answer questions on metabolism, chemical transformation. Almost all of the data in a paper can be queried.
Mining the scientific literature for plants and chemistrypetermurrayrust
ContentMine can read the daily scientific literature and extract facts. This talk was given to the OpenPlant project - with whom ContentMine collaborate at a meeting on 2016-07-25/27 in Norwich. Examples of extracted facts are given.
Architecture of ContentMine Components contentmine.orgpetermurrayrust
This is the evolving architecture of ContentMine (contentmine.org) architecture. It includes an overview ( slide #2, ) showing getpapers, quickscrape, norma and ami.
The key container is the CTree and the architecture shows where components are added or transformed to this.
These slides are dated and may be out-of-date wrt code. Some diagrams are autogenerated from *.dot files.
Please use http://discuss.contentmine.org/c/software as the main source of up-to-date info. Feel free to ask questions, offer help, critique, etc.
All s/w is Open (BSD, Apache2)
Amanuens.is HUmans and machines annotating scholarly literaturepetermurrayrust
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Towards Responsible Content Mining: A Cambridge perspectivepetermurrayrust
ContentMining (Text and Data Mining) is now legal in the UK for non-commercial research. Cambridge UK is a natural centre, with several components:
* a world-class University and Library
* many publishers, both Open Access and conventional
* a digital culture
* ContentMine - a leading proponent and practitioner of mining
Cambridge University Press welcomes content mining and invited PMR to give a talk there. He showed the technology and protocols and proposed a practical way forward in 2017
Asking the scientific literature to tell us about metabolismpetermurrayrust
Talk to Lhasa Ltd (a world leader in predicting drug metabolism and toxicity. Uses the scientific literature to answer questions on metabolism, chemical transformation. Almost all of the data in a paper can be queried.
Mining the scientific literature for plants and chemistrypetermurrayrust
ContentMine can read the daily scientific literature and extract facts. This talk was given to the OpenPlant project - with whom ContentMine collaborate at a meeting on 2016-07-25/27 in Norwich. Examples of extracted facts are given.
Architecture of ContentMine Components contentmine.orgpetermurrayrust
This is the evolving architecture of ContentMine (contentmine.org) architecture. It includes an overview ( slide #2, ) showing getpapers, quickscrape, norma and ami.
The key container is the CTree and the architecture shows where components are added or transformed to this.
These slides are dated and may be out-of-date wrt code. Some diagrams are autogenerated from *.dot files.
Please use http://discuss.contentmine.org/c/software as the main source of up-to-date info. Feel free to ask questions, offer help, critique, etc.
All s/w is Open (BSD, Apache2)
Amanuens.is HUmans and machines annotating scholarly literaturepetermurrayrust
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Towards Responsible Content Mining: A Cambridge perspectivepetermurrayrust
ContentMining (Text and Data Mining) is now legal in the UK for non-commercial research. Cambridge UK is a natural centre, with several components:
* a world-class University and Library
* many publishers, both Open Access and conventional
* a digital culture
* ContentMine - a leading proponent and practitioner of mining
Cambridge University Press welcomes content mining and invited PMR to give a talk there. He showed the technology and protocols and proposed a practical way forward in 2017
Automatic Extraction of Knowledge from Biomedical literaturepetermurrayrust
a plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
The scientific and medical literature contains huge amounts of valuable unused information. This talk shows how to discover it, extract, re-use and interpret it. Wikidata is presented as a key new tool and infrastructure. Everyone can become involved. However some of the barriers to use are sociopolitical and these are identified and discussed.
ContentMining for France and Europe; Lessons from 2 years in UKpetermurrayrust
I have spend 2 years carrying out Content Mining (aka Text and Data Mining) in the UK under the 2014 "Hargreaves" exception. This talk was given in Paris, to ADBU , after France had passed the law of the numeric Republique. I illustrate what worked in what did not and why and offer ideas to France and Europe
Asking the scientific literature to tell us about metabolismpetermurrayrust
Talk at Lhasa (https://www.lhasalimited.org/) a leading organization for "in silico prediction and database systems for use in metabolism, toxicology and related sciences". ContentMine software can extract data from papers on compound metabolism in reusable semantic form, including metabolic pathways, pharmacokinetic data.
Can Computers understand the scientific literature (includes compscie material)TheContentMine
Published on Jan 24, 2014 by PMR
With the semantic web machines can autonomously carry out many knowledge-based tasks as well as humans. The main problems are not technical but the prevention of access to information. I advocate automatic downloading and indexing of all scientific information
High throughput mining of the scholarly literature TheContentMine
Published on Jun 7, 2016 by PMR
Talk given to statisticians in Tilburg, with emphasis on scholarly comms for detecting unusual features. Includes demo of Amanuens.is and image mining
We have developed image processing techniques to extract data from diagrams used in science and scientific publications. These slides were presented at a workshop session for the Cambridge MPhil in Computational biology. There is an overview of the main techniques for cleaning diagrams, such as thresholding, binarization, edge detection and thinning. Examples are given from plots, phylogenetic trees, chemistry and neuroscience spikes. All software is Open Source and most is Java
Amanuens.is HUmans and machines annotating scholarly literature TheContentMine
Published on May 19, 2016 by PMR
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Published on May 18, 2016 by PMR
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
Automatic Extraction of Knowledge from the LiteratureTheContentMine
Published on May 11, 2016 by PMR
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
Automatic Extraction of Knowledge from Biomedical literature TheContentMine
Published on Mar 16, 2016 by PMR
A plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
Liberating facts from the scientific literature - Jisc Digifest 2016 TheContentMine
Published on Mar 4, 2016 by PMR
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
Published on Feb 29, 2016 by PMR
An overview of Text and Data Mining (ContentMining) including live demonstrations. The fundamentals: discover, scrape, normalize , facet/index, analyze, publish are exemplified using the recent Zika outbreak. Mining covers textual and non-textual content and examples of chemistry and phylogenetic tress are given.
Published on Feb 07, 2016 by PMR
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus. Includes clips of the software in action
Published on Jan 29, 2016 by PMR
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuous Integration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of content mining (TDM)
Published on Jan 27, 2016 by PMR
We have developed image processing techniques to extract data from diagrams used in science and scientific publications. These slides were presented at a workshop session for the Cambridge MPhil in Computational biology. There is an overview of the main techniques for cleaning diagrams, such as thresholding, binarization, edge detection and thinning. Examples are given from plots, phylogenetic trees, chemistry and neuroscience spikes. All software is Open Source and most is Java
Automatic Extraction of Knowledge from Biomedical literaturepetermurrayrust
a plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
The scientific and medical literature contains huge amounts of valuable unused information. This talk shows how to discover it, extract, re-use and interpret it. Wikidata is presented as a key new tool and infrastructure. Everyone can become involved. However some of the barriers to use are sociopolitical and these are identified and discussed.
ContentMining for France and Europe; Lessons from 2 years in UKpetermurrayrust
I have spend 2 years carrying out Content Mining (aka Text and Data Mining) in the UK under the 2014 "Hargreaves" exception. This talk was given in Paris, to ADBU , after France had passed the law of the numeric Republique. I illustrate what worked in what did not and why and offer ideas to France and Europe
Asking the scientific literature to tell us about metabolismpetermurrayrust
Talk at Lhasa (https://www.lhasalimited.org/) a leading organization for "in silico prediction and database systems for use in metabolism, toxicology and related sciences". ContentMine software can extract data from papers on compound metabolism in reusable semantic form, including metabolic pathways, pharmacokinetic data.
Can Computers understand the scientific literature (includes compscie material)TheContentMine
Published on Jan 24, 2014 by PMR
With the semantic web machines can autonomously carry out many knowledge-based tasks as well as humans. The main problems are not technical but the prevention of access to information. I advocate automatic downloading and indexing of all scientific information
High throughput mining of the scholarly literature TheContentMine
Published on Jun 7, 2016 by PMR
Talk given to statisticians in Tilburg, with emphasis on scholarly comms for detecting unusual features. Includes demo of Amanuens.is and image mining
We have developed image processing techniques to extract data from diagrams used in science and scientific publications. These slides were presented at a workshop session for the Cambridge MPhil in Computational biology. There is an overview of the main techniques for cleaning diagrams, such as thresholding, binarization, edge detection and thinning. Examples are given from plots, phylogenetic trees, chemistry and neuroscience spikes. All software is Open Source and most is Java
Amanuens.is HUmans and machines annotating scholarly literature TheContentMine
Published on May 19, 2016 by PMR
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Published on May 18, 2016 by PMR
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
Automatic Extraction of Knowledge from the LiteratureTheContentMine
Published on May 11, 2016 by PMR
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
Automatic Extraction of Knowledge from Biomedical literature TheContentMine
Published on Mar 16, 2016 by PMR
A plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
Liberating facts from the scientific literature - Jisc Digifest 2016 TheContentMine
Published on Mar 4, 2016 by PMR
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
Published on Feb 29, 2016 by PMR
An overview of Text and Data Mining (ContentMining) including live demonstrations. The fundamentals: discover, scrape, normalize , facet/index, analyze, publish are exemplified using the recent Zika outbreak. Mining covers textual and non-textual content and examples of chemistry and phylogenetic tress are given.
Published on Feb 07, 2016 by PMR
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus. Includes clips of the software in action
Published on Jan 29, 2016 by PMR
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuous Integration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of content mining (TDM)
Published on Jan 27, 2016 by PMR
We have developed image processing techniques to extract data from diagrams used in science and scientific publications. These slides were presented at a workshop session for the Cambridge MPhil in Computational biology. There is an overview of the main techniques for cleaning diagrams, such as thresholding, binarization, edge detection and thinning. Examples are given from plots, phylogenetic trees, chemistry and neuroscience spikes. All software is Open Source and most is Java
Digital Scholarship: Enlightenment or Devastated Landscape? TheContentMine
Published on Dec 17, 2015 by PMR
Every year 500 Billion USD of public funding is spent on research, but much of this lies hidden in papers that are never read. I describe how machines can help us to read the literature. However there is massive opposition from publishers who are trying to prevent open scholarship and who build walled gardens that they control
Published on Jul 21, 2014 by PMR
Jean-Claude Bradley was a pioneer of doing Open Science and on 2014-07-14 we held a memorial meeting in Cambridge (see also http://inmemoriamjcb.wikispaces.com/Jean-Claude+Bradley+Memorial+Symposium)
Published on Jul 24, 2014 by PMR
PhD Theses are normally locked away digitally. They cost 20 billion dollars to create and we waste much of this value. By making them open we can use software to read, index, reuse, compute and add massive value
Published on Aug 22, 2014 by PMR
Open Data and Open Science presented in Rio for Open Science 2014-08-22. I argue that Open Notebook Science is the way forward and will lead to great benefits
ContentMine: Open Data and Social MachinesTheContentMine
Published on Nov 13, 2014 by PMR
Scientific information is often hidden or not published properly. The ContentMine is a Social Machine consisting of semantic software and communities of domain expertise; it aims to liberate all scientific facts from the published literature on a daily basis.
The talk , delivered to the Computational Institute, will be /was followed by a hands-on workshop learning how to use the technology and work as a community.
Published on Nov 26, 2014 by PMR
Followup meeting in London to OpenCon2014, on the need for different models of scholarly communication. I explore the history of 20thC academic student-based revolutions, with special relevance to young people and the scope for action today.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
2. The Right to Read is the Right to Mine
http://contentmine.org
3. ContentMine
• 1-2 year Shuttleworth Funding from 2014-03
• Free to everyone, Open Source, updated daily
• Structured Text, and Image/Diagram Mining
• Workshops for training and training trainers
• Bottom-up community development
– Bioscience (EuropePMC, BBSRC)
– Disease Ebola
– Astrophysics (Stray Toaster)
– Chemistry (TSB, EBI, PennState - Citeseer)
• We fight for Justice and Freedom
4. ContentMine People
• Jenny Molloy
• Ross Mounce
• Peter Murray-Rust + volunteers (Bioscience, disease)
• Richard Smith-Unna + 20 quickscrape volunteers
• Steph Unna
• Cottage Labs (Mark MacGillivray, Emanuil Tolev,
Richard Jones)
• Prof Charles Oppenheim
• Karien Bezuidenhout (Shuttleworth)
• Advisory Board RSN
5. ContentMine Workshops
(1-hour -> full day or more)
2014-May->Nov
• Budapest/Shuttleworth
• Leicester Univ
• Electronic Theses and Dissertations
• Austrian Science Fund AT
• OKFest DE
• Eur. Bioinformatics Institute
• Open Science Rio de Janeiro BR
• Sci DataCon , Delhi IN
• Univ of Chicago US
• OpenCon 2014, Wash DC. US
Upcoming
• JISC
• LIBER
• BL
• Wellcome Trust
• WHO
7. Regular Expressions
(Easier than Crosswords or Sudoku)
Ebola Ebola
Mali (not
Malicious)
MaliW (end of word)
Bat or bat [Bb]at (alternatives)
bat or bats bats? (optional letter)
Bat or Bats or bat
or bats
[Bb]ats?
Sudden onset [Ss]uddens+onset (space/s)
Panthera leoor
Gorilla gorilla
[A-Z][a-z]+s+[a-z]+
(ranges of letters)
8. Ebola regex
• <compoundRegex title="ebola">
• <regex weight="1.0" fields="ebola" case="">(Ebola)</regex>
• <regex weight="1.0" fields="marburg">(Marburg)</regex>
• <regex weight="1.0" fields="hemorrhagic_fever">([Hh]a?emorrhagics+fever)</regex>
• <regex weight="0.8" fields="sudden_onset">([Ss]uddens+onset)</regex>
• <regex weight="0.6" fields="vomiting_diarrhoea">([Vv]omitings+diarrho?ea)</regex>
• <regex weight="0.5" fields="guinea">(Guinea)</regex>
• <regex weight="0.5" fields="sierra_leone">(Sierras+Leone)</regex>
• <regex weight="0.5" fields="liberia">(Liberia)</regex>
• <regex weight="0.5" fields="mali">(Mali)W</regex>
• <regex weight="0.6" fields="contact_tracing">([Cc]ontacts+tracing)</regex>
• <regex weight="0.5" fields="bat">W([Bb]ats?W)</regex>
• <regex weight="0.5" fields="bushmeat">([Bb]ushmeat)</regex>
• <regex weight="0.5" fields="drc">(Democratic Republics*(s*of)?(s*the)?s*Congo)(DRC)</regex>
• <regex weight="0.6" fields="safe_burial">([Ss]afes+burials+practice?s)</regex>
• <regex weight="1.0" fields="etu">([Ee]bolas+treatments+units?)(ETU)</regex>
• </compoundRegex>
I
15 mins to create, 15 mins to install and test
Or run online at CottageLabs
9. Results of Regex on Ebola
• <resultsList xmlns="http://www.xml-cml.org/ami">
• <results xmlns="">
• <source xmlns="http://www.xml-cml.org/ami"
• name="/Users/pm286/workspace/ami-core/./docs/ebola/text/14Nov.txt" />
• <result>
• <regex xmlns="http://www.xml-cml.org/ami" lineNumber="7"
• lineValue=" There have been 14 413 reported Ebola cases in eight countries since the outbreak ">
• <regex xmlns="" weight="1.0" fields="[ebola]">
• <pattern>(Ebola)</pattern>
• </regex>
• <hits xmlns="">
• <hit ebola="Ebola" />
• </hits>
• </regex>
• </result>
• <result>
• <regex xmlns="http://www.xml-cml.org/ami" lineNumber="9"
• lineValue="HIGHLIGHTS Case incidence continues to increase in Sierra Leone, and transmission also remains ">
• <regex xmlns="" weight="0.5" fields="[sierra_leone]">
• <pattern>(Sierras+Leone)</pattern>
• </regex>
• <hits xmlns="">
• <hit sierra_leone="Sierra Leone" />
• </hits>
• </regex>
• </result>
10. Demo of Content Mining
ChemicalTagger (Lezan Hawizy) a shallow,
domain-specific, semantic parser for un/natural
language.
11. Bacterial WP_phylogenetic tree
Our machines have read and interpreted 4300 in an hour with > 95% accuracy
Trees From http://ijs.sgmjournals.org/ used under new UK legislation (Hargreaves)
WP: Clostridium_butyricum
Genbank ID
American Type
Culture Collection
12. RSU: Richard Smith-Unna
PMR: Peter Murray-Rust
CL: CottageLabs
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