Synthesis Infrastructures - User contributions [en]

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-10T06:37:25Z

AWY:

{{Project
|Description=Looking at possibilities to generalized knowledge graph in Logseq based on Discourse Graph developed by Prof. Joel Chan and David Vargas for knowledge synthesis, with the possibility to export the information to Systems biology graphical notation markup language to create the network/graph.
|Publications=10.1515/jib-2020-0016, 10.1016/j.tplants.2010.03.005, 10.1002/psp4.12155
|Contributors=Akila Wijerathna-Yapa
}}
We are interested in adopting a modified version of Prof. Joel Chan, and David Varga's discourse graph Roam extension to literature synthesis for our ongoing text-mining project for biological network modeling. Having a knowledge graph in Logseq with its essential sections - Hypothesis, Evidence, and Experimental Results will enable us (researchers) to get insights into knowledge synthesis/biological network modeling.

For the first phase, we look opportunity to create a knowledge graph in Logseq using limited discourse graph features — Hypothesis, Evidence, and Experimental Results, using standardized Bio-ontologies. This will offer a method for formalizing biological knowledge, such as that pertaining to genes, anatomy, and phenotypes, in complicated hierarchies made of concepts and rules. For this we can probably use '''[[wikipedia:Systems_Biology_Graphical_Notation|Systems Biology Graphical Notations (SBGN)]] based arcs — i.e. [https://www.nature.com/articles/npre.2009.3724.1 SBGN Activity Flow language].'''

In the second phase, we intend to convert the knowledge network into System Biology Graphical representation using '''SBGN''' with an expectation to import the SBGN Biological network into [[wikipedia:SBML|'''Systems Biology Markup Language''' ('''SBML)''']] for downstream computational/mathematical simulations.

'''The Challenge:''' Building Knowledge netwok in Logseq and translate into SBGN.
[[File:LogSeq to SBGN.jpg|thumb|935x935px|LogSeq to SBGN]]

We can write biological statements using SBGN vocabulary (Activity Flow reference card) in Logseq and create knowledge graph. Later we should be able to import these statements to SBGN and create the network model using edge description (Activity Flow reference card) used in the knowledge graph.
[[File:SBGN Activity flow.png|center|thumb|560x560px|SBGN Activity flow reference card]]

"Newt is a sample application for the web based library named ChiSE developed to visualize and edit the pathway models represented by process description (PD) and activity flow (AF) languages of SBGN or in simple interaction format (SIF)." https://github.com/iVis-at-Bilkent/newt
[[File:GitHub - iVis-at-Bilkent-newt- A web application to visualize and edit pathway models.png|center|thumb|592x592px|GitHub - iVis-at-Bilkent/newt: A web application to visualize and edit pathway models]]

At the moment we can do create network manually by [https://web.newteditor.org/ Newt Editor] webtool.
[[File:Newt Editor 2.png|center|thumb|512x512px|Newt Editor]]

Let's say for e.g. we collated information on Logseq about how the glycolysis work, then we manualy draw this on [https://web.newteditor.org/ Newt Editor], later we can export the relationships using SBML for simulations. Like the same way we can import SBML files to this website to create network. So ideally what we should work here is how to collate curated information on LogSeq in a way we can export as SBML
[[File:Newt Editor .png|center|thumb|532x532px|Newt Editor]]

{{DEFAULTSORT:Translate Logseq Knowledge Graph to Systems Biology Network Diagrams}}

File:GitHub - iVis-at-Bilkent-newt- A web application to visualize and edit pathway models.png

2022-11-10T06:36:43Z

AWY:

GitHub - iVis-at-Bilkent/newt: A web application to visualize and edit pathway models

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-10T06:34:56Z

AWY: added more description

{{Project
|Description=Looking at possibilities to generalized knowledge graph in Logseq based on Discourse Graph developed by Prof. Joel Chan and David Vargas for knowledge synthesis, with the possibility to export the information to Systems biology graphical notation markup language to create the network/graph.
|Publications=10.1515/jib-2020-0016, 10.1016/j.tplants.2010.03.005, 10.1002/psp4.12155
|Contributors=Akila Wijerathna-Yapa
}}
We are interested in adopting a modified version of Prof. Joel Chan, and David Varga's discourse graph Roam extension to literature synthesis for our ongoing text-mining project for biological network modeling. Having a knowledge graph in Logseq with its essential sections - Hypothesis, Evidence, and Experimental Results will enable us (researchers) to get insights into knowledge synthesis/biological network modeling.

For the first phase, we look opportunity to create a knowledge graph in Logseq using limited discourse graph features — Hypothesis, Evidence, and Experimental Results, using standardized Bio-ontologies. This will offer a method for formalizing biological knowledge, such as that pertaining to genes, anatomy, and phenotypes, in complicated hierarchies made of concepts and rules. For this we can probably use '''[[wikipedia:Systems_Biology_Graphical_Notation|Systems Biology Graphical Notations (SBGN)]] based arcs — i.e. [https://www.nature.com/articles/npre.2009.3724.1 SBGN Activity Flow language].'''

In the second phase, we intend to convert the knowledge network into System Biology Graphical representation using '''SBGN''' with an expectation to import the SBGN Biological network into [[wikipedia:SBML|'''Systems Biology Markup Language''' ('''SBML)''']] for downstream computational/mathematical simulations.

'''The Challenge:''' Building Knowledge netwok in Logseq and translate into SBGN.
[[File:LogSeq to SBGN.jpg|thumb|935x935px|LogSeq to SBGN]]

We can write biological statements using SBGN vocabulary (Activity Flow reference card) in Logseq and create knowledge graph. Later we should be able to import these statements to SBGN and create the network model using edge description (Activity Flow reference card) used in the knowledge graph.
[[File:SBGN Activity flow.png|center|thumb|560x560px|SBGN Activity flow reference card]]

At the moment we can do create network manually by [https://web.newteditor.org/ Newt Editor] webtool.

"Newt is a sample application for the web based library named ChiSE developed to visualize and edit the pathway models represented by process description (PD) and activity flow (AF) languages of SBGN or in simple interaction format (SIF)." https://github.com/iVis-at-Bilkent/newt
[[File:Newt Editor 2.png|center|thumb|512x512px|Newt Editor]]

Let's say for e.g. we collated information on Logseq about how the glycolysis work, then we manualy draw this on [https://web.newteditor.org/ Newt Editor], later we can export the relationships using SBML for simulations. Like the same way we can import SBML files to this website to create network. So ideally what we should work here is how to collate curated information on LogSeq in a way we can export as SBML
[[File:Newt Editor .png|center|thumb|532x532px|Newt Editor]]

{{DEFAULTSORT:Translate Logseq Knowledge Graph to Systems Biology Network Diagrams}}

File:Newt Editor 2.png

2022-11-10T06:32:04Z

AWY:

Newt Editor 2

File:SBGN Activity flow.png

2022-11-10T06:28:15Z

AWY:

SBGN Activity flow

File:Newt Editor .png

2022-11-10T06:24:44Z

AWY:

Newt Editor

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-10T06:19:03Z

AWY:

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-01T10:25:20Z

AWY: Explaining the project

We are interested in adopting a modified version of Prof. Joel Chan, and David Varga's discourse graph Roam extension to literature synthesis for our ongoing text-mining project for biological network modeling. Having a knowledge graph in Logseq with its essential sections - Hypothesis, Evidence, and Experimental Results will enable us (researchers) to get insights into knowledge synthesis/biological network modeling.

For the first phase, we look opportunity to create a knowledge graph in Logseq using limited discourse graph features — Hypothesis, Evidence, and Experimental Results, using standardized Bio-ontologies. This will offer a method for formalizing biological knowledge, such as that pertaining to genes, anatomy, and phenotypes, in complicated hierarchies made of concepts and rules. For this we can probably use '''[[wikipedia:Systems_Biology_Graphical_Notation|Systems Biology Graphical Notations (SBGN)]] based arcs — i.e. [https://www.nature.com/articles/npre.2009.3724.1 SBGN Activity Flow language].'''

In the second phase, we intend to convert the knowledge network into System Biology Graphical representation using '''SBGN''' with an expectation to import the SBGN Biological network into [[wikipedia:SBML|'''Systems Biology Markup Language''' ('''SBML)''']] for downstream computational/mathematical simulations.

'''The Challenge:''' Building Knowledge netwok in Logseq and translate into SBGN.
[[File:LogSeq to SBGN.jpg|thumb|935x935px|LogSeq to SBGN]]
{{Project
|Description=Looking at possibilities to generalized knowledge graph in Logseq based on Discourse Graph developed by Prof. Joel Chan and David Vargas for knowledge synthesis, with the possibility to export the information to Systems biology graphical notation markup language to create the network/graph.
|Publications=10.1515/jib-2020-0016, 10.1016/j.tplants.2010.03.005, 10.1002/psp4.12155
|Contributors=Akila Wijerathna-Yapa
}}
{{DEFAULTSORT:Translate Logseq Knowledge Graph to Systems Biology Network Diagrams}}

File:LogSeq to SBGN.jpg

2022-11-01T10:24:26Z

AWY:

LogSeq to SBGN

Building a knowledge graph in Logseq

2022-11-01T09:39:04Z

AWY: AWY moved page Building a knowledge graph in Logseq to Translate Logseq Knowledge Graph to Systems Biology Network Diagrams: To give more context

#REDIRECT [[Translate Logseq Knowledge Graph to Systems Biology Network Diagrams]]

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-01T09:39:04Z

AWY: AWY moved page Building a knowledge graph in Logseq to Translate Logseq Knowledge Graph to Systems Biology Network Diagrams: To give more context

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-11-01T09:34:13Z

AWY: Title changes

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-10-29T12:37:33Z

AWY:

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-10-29T12:36:53Z

AWY:

Translate Logseq Knowledge Graph to Systems Biology Network Diagrams

2022-10-29T12:23:17Z

AWY: Created page with "{{Project |Description=Looking at possibilities to generalized knowledge graph in Logseq based on Discourse Graph developed by Prof. Joel Chan and David Vargas for knowledge synthesis, with the possibility to export the information to Systems biology graphical notation markup language to create the network/graph. |Publications=https://doi.org/10.1515/jib-2020-0016, https://doi.org/10.1016/j.tplants.2010.03.005, https://doi.org/10.1002/psp4.12155 }}"

Akila Wijerathna-Yapa

2022-10-29T12:08:23Z

AWY: Add Links

{{Participant
|Timezone=Australia/Brisbane (GMT+10:00/GMT+10:00)
|Affiliation=[https://www.plantsuccess.org/ ARC Centre of Excellence for Plant Success in Nature and Agriculture], [https://en.wikipedia.org/wiki/University_of_Queensland The University of Queensland]
|Table Assignment=Table 2
}}
{{Workshop Submission
|Interest=We use knowledge of networks, multidisciplinary hypothesis-driven mechanistic modeling, molecular plant physiology, genetics, and genomics approach to understand how plants regulate bud outgrowth and discover new network components that facilitate breeding. I look forward to learning how the community uses Computer-Supported tools for hypothesis and evidence synthesis for life science research.
|Frame=Practitioner
|Materials=I am interested in using prior knowledge for plant phenotypic prediction research in predictive breeding. Today we have a substantial understanding of the biology of how plant regulates their phenotypes—however, this information accumulated over the years and scattered. Therefore I am keen to build a knowledge network that mathematicians and computational modelers can use this knowledge network to develop algorithms to predict phenotypes.
As a first challenge of the project, I have to bridge the gap between computational biologists and wet lab researchers to communicate biology to understand molecular physiological mechanisms. For this, I'm intended to use Systems Biology Graphical Notations. By joining CSCW 2022, I would like to learn how to use markdown-based note-taking Apps (i.e., [https://logseq.com/ LogSeq], [https://obsidian.md/ Obsidian], [https://roamresearch.com/ Roam Research]) to synthesize the knowledge and import it into [https://en.wikipedia.org/wiki/Systems_Biology_Graphical_Notation Systems biology graphical notation markup language] to create the network.
|Organizer Topics=Research Data, Knowledge Graphs, Knowledge Network, [https://sbgn.github.io/ Systems Biology Graphical Notation (SBGN)]
}}

Akila Wijerathna-Yapa

2022-10-29T12:02:53Z

AWY:

{{Participant
|Timezone=Australia/Brisbane (GMT+10:00/GMT+10:00)
|Affiliation=[https://www.plantsuccess.org/ ARC Centre of Excellence for Plant Success in Nature and Agriculture], [https://en.wikipedia.org/wiki/University_of_Queensland The University of Queensland]
|Table Assignment=Table 2
}}
{{Workshop Submission
|Interest=We use knowledge of networks, multidisciplinary hypothesis-driven mechanistic modeling, molecular plant physiology, genetics, and genomics approach to understand how plants regulate bud outgrowth and discover new network components that facilitate breeding. I look forward to learning how the community uses Computer-Supported tools for hypothesis and evidence synthesis for life science research.
|Frame=Practitioner
|Materials=I am interested in using prior knowledge for plant phenotypic prediction research in predictive breeding. Today we have a substantial understanding of the biology of how plant regulates their phenotypes—however, this information accumulated over the years and scattered. Therefore I am keen to build a knowledge network that mathematicians and computational modelers can use this knowledge network to develop algorithms to predict phenotypes.
As a first challenge of the project, I have to bridge the gap between computational biologists and wet lab researchers to communicate biology to understand molecular physiological mechanisms. For this, I'm intended to use Systems Biology Graphical Notations. By joining CSCW 2022, I would like to learn how to use markdown-based note-taking Apps (i.e. LogSeq, Obsidian) to synthesize the knowledge and import it into Systems biology graphical notation markup language to create the network.
|Organizer Topics=Research Data, Graphs, Knowledge Network
}}