From 36134efef452702e928cc0c61782077d70587682 Mon Sep 17 00:00:00 2001 From: Vincent Rubinetti Date: Thu, 27 Jun 2019 15:34:03 -0400 Subject: [PATCH] Revamp website with new home page Merges https://github.com/hetio/het.io/pull/7 Closes https://github.com/hetio/het.io/issues/4 (permalinks to headers) Closes https://github.com/hetio/het.io/issues/5 (creates new favicon) Closes https://github.com/hetio/het.io/issues/6 (centers landing page) Revamps or creates homepage /about /explore /studies /software /media /hnep. Adopt Jekyll for templating pages /repurpose and /disease-genes imported verbatim from previous version --- .gitignore | 4 + Gemfile | 30 ++ Gemfile.lock | 74 +++ _includes/footer.html | 9 + _includes/header.html | 6 + _includes/nav.html | 42 ++ _layouts/default.html | 67 +++ _layouts/landing.html | 44 ++ about/index.html | 426 +++++++++++++++ about/metagraph-large.png | Bin 0 -> 868309 bytes about/metagraph-src.png | Bin 0 -> 1000221 bytes about/metagraph.png | Bin 0 -> 334773 bytes about/metagraph.svg | 2 + about/page.css | 24 + android-chrome-192x192.png | Bin 0 -> 8786 bytes android-chrome-512x512.png | Bin 0 -> 24260 bytes apple-touch-icon.png | Bin 0 -> 8341 bytes browserconfig.xml | 9 + deep-field.js | 413 ++++++++++++++ .../disease-genes.png | Bin explore/connectivity-search.png | Bin 0 -> 143966 bytes explore/download.svg | 42 ++ explore/index.html | 98 ++++ explore/neo4j.png | Bin 0 -> 223553 bytes favicon-192x192.png | Bin 0 -> 8786 bytes favicon.ico | Bin 318 -> 0 bytes global.css | 503 ++++++++++++++++++ google01fdd8bdcb7ae925.html | 1 - hetnet-bg.png | Bin 450115 -> 0 bytes hnep/hnep.png | Bin 0 -> 247348 bytes hnep/index.html | 205 ++----- icons/1397872113_mail-01.svg | 1 - icons/bug-14x14.png | Bin 571 -> 0 bytes icons/bug-16x16.png | Bin 404 -> 0 bytes icons/bug.png | Bin 4660 -> 0 bytes icons/rat-14x14.png | Bin 450 -> 0 bytes icons/rat-20x20.png | Bin 241 -> 0 bytes icons/rat.png | Bin 1101 -> 0 bytes index.html | 302 ++--------- logo-animated.svg | 119 +++++ logo-background.svg | 68 +++ logo-silhouette.svg | 67 +++ logo.svg | 67 +++ media/ASHG-2014-poster.jpg | Bin 269647 -> 0 bytes media/ASHG-2014-poster.png | Bin 0 -> 590655 bytes media/bcr-cover-src.png | Bin 0 -> 609747 bytes media/bcr-cover.jpg | Bin 126937 -> 0 bytes media/bcr-cover.png | Bin 0 -> 481940 bytes media/bioRxiv_logo.png | Bin 24444 -> 0 bytes media/graph-gist.png | Bin 0 -> 101266 bytes media/graphcon.jpg | Bin 0 -> 175720 bytes media/hetnet-awakens.png | Bin 0 -> 1572652 bytes media/hetnet-cytoscape.png | Bin 0 -> 406557 bytes media/index.html | 395 ++++++++------ media/ismb.jpg | Bin 0 -> 145633 bytes media/legal-confusion.jpg | Bin 0 -> 63813 bytes media/mia.jpg | Bin 0 -> 126893 bytes media/neo4j-meetup.jpg | Bin 0 -> 93723 bytes media/page.css | 62 +++ media/phd.jpg | Bin 0 -> 210983 bytes media/podcast.jpg | Bin 0 -> 156422 bytes mstile-144x144.png | Bin 0 -> 2991 bytes mstile-150x150.png | Bin 0 -> 3135 bytes mstile-310x150.png | Bin 0 -> 3387 bytes mstile-310x310.png | Bin 0 -> 6892 bytes mstile-70x70.png | Bin 0 -> 2267 bytes page.css | 27 + repurpose/rephetio.svg | 42 ++ robots.txt | 2 - safari-pinned-tab.svg | 67 +++ scripts.js | 49 ++ search/index.html | 10 + share-thumbnail.html | 20 + share-thumbnail.png | Bin 0 -> 38381 bytes site.webmanifest | 19 + software/hetmat.svg | 41 ++ software/hetnet.svg | 49 ++ software/index.html | 84 +++ software/xswap.svg | 82 +++ studies/index.html | 49 ++ 80 files changed, 3027 insertions(+), 594 deletions(-) create mode 100644 Gemfile create mode 100644 Gemfile.lock create mode 100644 _includes/footer.html create mode 100644 _includes/header.html create mode 100644 _includes/nav.html create mode 100644 _layouts/default.html create mode 100644 _layouts/landing.html create mode 100644 about/index.html create mode 100644 about/metagraph-large.png create mode 100644 about/metagraph-src.png create mode 100644 about/metagraph.png create mode 100644 about/metagraph.svg create mode 100644 about/page.css create mode 100644 android-chrome-192x192.png create mode 100644 android-chrome-512x512.png create mode 100644 apple-touch-icon.png create mode 100644 browserconfig.xml create mode 100644 deep-field.js rename media/hetnet-visualization.png => disease-genes/disease-genes.png (100%) create mode 100644 explore/connectivity-search.png create mode 100644 explore/download.svg create mode 100644 explore/index.html create mode 100644 explore/neo4j.png create mode 100644 favicon-192x192.png delete mode 100644 favicon.ico create mode 100644 global.css delete mode 100644 google01fdd8bdcb7ae925.html delete mode 100644 hetnet-bg.png create mode 100644 hnep/hnep.png delete mode 100644 icons/1397872113_mail-01.svg delete mode 100644 icons/bug-14x14.png delete mode 100644 icons/bug-16x16.png delete mode 100644 icons/bug.png delete mode 100644 icons/rat-14x14.png delete mode 100644 icons/rat-20x20.png delete mode 100644 icons/rat.png create mode 100644 logo-animated.svg create mode 100644 logo-background.svg create mode 100644 logo-silhouette.svg create mode 100644 logo.svg delete mode 100644 media/ASHG-2014-poster.jpg create mode 100644 media/ASHG-2014-poster.png create mode 100644 media/bcr-cover-src.png delete mode 100644 media/bcr-cover.jpg create mode 100644 media/bcr-cover.png delete mode 100644 media/bioRxiv_logo.png create mode 100644 media/graph-gist.png create mode 100644 media/graphcon.jpg create mode 100644 media/hetnet-awakens.png create mode 100644 media/hetnet-cytoscape.png create mode 100644 media/ismb.jpg create mode 100644 media/legal-confusion.jpg create mode 100644 media/mia.jpg create mode 100644 media/neo4j-meetup.jpg create mode 100644 media/page.css create mode 100644 media/phd.jpg create mode 100644 media/podcast.jpg create mode 100644 mstile-144x144.png create mode 100644 mstile-150x150.png create mode 100644 mstile-310x150.png create mode 100644 mstile-310x310.png create mode 100644 mstile-70x70.png create mode 100644 page.css create mode 100644 repurpose/rephetio.svg delete mode 100644 robots.txt create mode 100644 safari-pinned-tab.svg create mode 100644 scripts.js create mode 100644 search/index.html create mode 100644 share-thumbnail.html create mode 100644 share-thumbnail.png create mode 100644 site.webmanifest create mode 100644 software/hetmat.svg create mode 100644 software/hetnet.svg create mode 100644 software/index.html create mode 100644 software/xswap.svg create mode 100644 studies/index.html diff --git a/.gitignore b/.gitignore index 6e1edfd..29bc2d1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,6 @@ +_site +.sass-cache +.jekyll-metadata + ### Linux ### *~ diff --git a/Gemfile b/Gemfile new file mode 100644 index 0000000..2bf1ac7 --- /dev/null +++ b/Gemfile @@ -0,0 +1,30 @@ +source "https://rubygems.org" + +# Hello! This is where you manage which Jekyll version is used to run. +# When you want to use a different version, change it below, save the +# file and run `bundle install`. Run Jekyll with `bundle exec`, like so: +# +# bundle exec jekyll serve +# +# This will help ensure the proper Jekyll version is running. +# Happy Jekylling! +gem "jekyll", "~> 3.8.5" + +# This is the default theme for new Jekyll sites. You may change this to anything you like. +gem "minima", "~> 2.0" + +# If you want to use GitHub Pages, remove the "gem "jekyll"" above and +# uncomment the line below. To upgrade, run `bundle update github-pages`. +# gem "github-pages", group: :jekyll_plugins + +# If you have any plugins, put them here! +group :jekyll_plugins do + gem "jekyll-feed", "~> 0.6" +end + +# Windows does not include zoneinfo files, so bundle the tzinfo-data gem +gem "tzinfo-data", platforms: [:mingw, :mswin, :x64_mingw, :jruby] + +# Performance-booster for watching directories on Windows +gem "wdm", "~> 0.1.0" if Gem.win_platform? + diff --git a/Gemfile.lock b/Gemfile.lock new file mode 100644 index 0000000..143daf9 --- /dev/null +++ b/Gemfile.lock @@ -0,0 +1,74 @@ +GEM + remote: https://rubygems.org/ + specs: + addressable (2.6.0) + public_suffix (>= 2.0.2, < 4.0) + colorator (1.1.0) + concurrent-ruby (1.1.5) + em-websocket (0.5.1) + eventmachine (>= 0.12.9) + http_parser.rb (~> 0.6.0) + eventmachine (1.2.7) + ffi (1.11.1) + forwardable-extended (2.6.0) + http_parser.rb (0.6.0) + i18n (0.9.5) + concurrent-ruby (~> 1.0) + jekyll (3.8.5) + addressable (~> 2.4) + colorator (~> 1.0) + em-websocket (~> 0.5) + i18n (~> 0.7) + jekyll-sass-converter (~> 1.0) + jekyll-watch (~> 2.0) + kramdown (~> 1.14) + liquid (~> 4.0) + mercenary (~> 0.3.3) + pathutil (~> 0.9) + rouge (>= 1.7, < 4) + safe_yaml (~> 1.0) + jekyll-feed (0.12.1) + jekyll (>= 3.7, < 5.0) + jekyll-sass-converter (1.5.2) + sass (~> 3.4) + jekyll-seo-tag (2.6.1) + jekyll (>= 3.3, < 5.0) + jekyll-watch (2.2.1) + listen (~> 3.0) + kramdown (1.17.0) + liquid (4.0.3) + listen (3.1.5) + rb-fsevent (~> 0.9, >= 0.9.4) + rb-inotify (~> 0.9, >= 0.9.7) + ruby_dep (~> 1.2) + mercenary (0.3.6) + minima (2.5.0) + jekyll (~> 3.5) + jekyll-feed (~> 0.9) + jekyll-seo-tag (~> 2.1) + pathutil (0.16.2) + forwardable-extended (~> 2.6) + public_suffix (3.1.0) + rb-fsevent (0.10.3) + rb-inotify (0.10.0) + ffi (~> 1.0) + rouge (3.4.1) + ruby_dep (1.5.0) + safe_yaml (1.0.5) + sass (3.7.4) + sass-listen (~> 4.0.0) + sass-listen (4.0.0) + rb-fsevent (~> 0.9, >= 0.9.4) + rb-inotify (~> 0.9, >= 0.9.7) + +PLATFORMS + ruby + +DEPENDENCIES + jekyll (~> 3.8.5) + jekyll-feed (~> 0.6) + minima (~> 2.0) + tzinfo-data + +BUNDLED WITH + 2.0.2 diff --git a/_includes/footer.html b/_includes/footer.html new file mode 100644 index 0000000..c1a912c --- /dev/null +++ b/_includes/footer.html @@ -0,0 +1,9 @@ + diff --git a/_includes/header.html b/_includes/header.html new file mode 100644 index 0000000..8595c26 --- /dev/null +++ b/_includes/header.html @@ -0,0 +1,6 @@ +
+ + + + {% include nav.html %} +
diff --git a/_includes/nav.html b/_includes/nav.html new file mode 100644 index 0000000..3be8566 --- /dev/null +++ b/_includes/nav.html @@ -0,0 +1,42 @@ + diff --git a/_layouts/default.html b/_layouts/default.html new file mode 100644 index 0000000..f5331a3 --- /dev/null +++ b/_layouts/default.html @@ -0,0 +1,67 @@ + + + + Hetionet - {{ page.title }} + + + + + + + + + + + + + + + {% include header.html %} +
+
+
+

+ {{ page.title }} +

+
+
+ {{ content }} +
+ {% include footer.html %} + + + + + + + diff --git a/_layouts/landing.html b/_layouts/landing.html new file mode 100644 index 0000000..9b73a9f --- /dev/null +++ b/_layouts/landing.html @@ -0,0 +1,44 @@ + + + + {{ page.title }} + + + + + + + + + + + + + + + + + + + + + + + + + + + + {{ content }} + + + diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..70164fa --- /dev/null +++ b/about/index.html @@ -0,0 +1,426 @@ +--- +layout: default +title: About +icon: fas fa-book-open +category: about +--- + +
+
+

What is a "hetnet"?

+

+ A network (also known as a + graph) is a conceptual representation of a group of things — called + nodes — and the relationships between them — called + edges. Typically, a network has only one type of node and one type + of edge. But in many cases, it is necessary to be able to distinguish + between different types of entities and relationships. +

+

+ A hetnet (short for + heterogeneous information network) is a network where nodes + and edges can be multiple types. This additional dimension allows a hetnet + to accurately describe more complex data. Hetnets are particularly useful + in biomedicine, where it is important to capture the conceptual + distinctions between various components and mechanisms, such as genes and + diseases, or upregulation and binding. +

+

+ The prefix meta is used on this site to refer to the type of the + node/edge (e.g. compound), as opposed to the specific node/edge itself + (e.g. acetaminophen). +

+
+
+
+
+
+

What is Hetionet?

+

+ Hetionet is a hetnet of biomedical knowledge. It encodes relationships + uncovered by millions of studies conducted over the last half-century into + a single resource. The network is constructed from a collection of + publicly available databases, and is itself + open-source and free to use, barring any upstream restrictions. +

+

+ Hetionet enables scientists and biologists to formulate novel hypotheses, + predictions, and other valuable insights by connecting an existing body of + biomedical data across multiple levels and types in a convenient, + accessible, holistic way. +

+
+
+
+
+
+

Why was Hetionet made?

+

+ Hetionet was originally created as part of + Project Rephetio, a study that utilized the + benefits of hetnets to predict new uses for existing drugs. Although the + original resources for the network were selected for drug repurposing, + Hetionet is now useful in a much broader sense, and + has been used for a variety of purposes. +

+

+ Hetionet was also made to alleviate some of the inaccuracies and + inconveniences of using other integrative networks, or trying to use + multiple, separate databases in the same analysis. It unifies data from + several different, disparate sources into a single, comprehensive, + accessible, common-format network. +

+
+
+
+
+
+

What's in Hetionet?

+

+ Hetionet combines information from + 29 public databases. The network contains 47,031 nodes of + 11 types + and 2,250,197 edges of + 24 types. +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MetanodeDescription
Gene + Protein-coding human genes. From + Entrez Gene. +
Compound + Approved small molecule compounds with documented chemical + structures. From DrugBank. +
Anatomy + Anatomical structures, excluding structures that are known not to + be found in humans. From + Uberon. +
Disease + Complex diseases, selected to be distinct and specific enough to + be clinically relevant yet general enough to be well annotated. + From Disease Ontology. +
Symptom + Signs and Symptoms (i.e. clinical abnormalities that can indicate + a medical condition). From the + MeSH ontology. +
Side Effect + Adverse drug reactions. From + SIDER/UMLS. +
Biological Process + Larger processes or biological programs accomplished by multiple + molecular activities. From + Gene Ontology. +
Cellular Component + The locations relative to cellular structures in which a gene + product performs a function. From + Gene Ontology. +
Molecular Function + Activities that occur at the molecular level, such as "catalysis" + or "transport". From + Gene Ontology. +
Pathway + A series of actions among molecules in a cell that leads to a + certain product or change in the cell. From + WikiPathways, Reactome, and Pathway + Interaction Database. +
Pharmacologic Class + "Chemical/Ingredient", "Mechanism of Action", and + "Physiologic Effect" FDA class types. From + DrugCentral. +
+
+

+ The metagraph diagram below illustrates the connectivity between + different types of nodes and edges in the network. +

+
+ + + +
+

+ Notice that multiple types of edges can connect the same two nodes. For + example, a compound can have a relationship to a gene by binding its + protein and/or by upregulating it. Since the biological effects of these + two mechanisms are very different, they are given separate representations + in the network. +

+
+
+
+
+
+

How was Hetionet made?

+

+ Hetionet was created openly, with a process of continuous + public discussion and feedback. In general, great care was taken to "clean" and "de-duplicate" the + network such that it would be as easy and effective to use as possible. +

+

+ A standardized vocabulary was selected to define each metanode (e.g. + Entrez Gene for genes or Disease Ontology for diseases). These + vocabularies provide a robust, common backbone to overlay nodes and edges + onto without repeating equivalent concepts. +

+

+ Edges were extracted from + omics-scale resources + (ranging from + text-mining to + high-throughput screens) and their identifiers were converted to the appropriate node + vocabularies. Edges were consolidated such that one relationship is + encoded into a single edge, even when multiple studies or resources have + reported it. +

+

+ See here + for a more detailed description of the methodologies used to construct the + network. +

+
+
+
+
+
+

Acknowledgements

+

+ Hetionet was originally developed by + Daniel Himmelstein and collaborators in + the Baranzini Lab at + the University of California, San Fransisco, as part of + Project Rephetio. Thank you to the + team of online contributors + who + provided feedback + throughout the course of the project. +

+

+ Other applications of Hetionet and algorithms for hetnets continue to be + developed by Daniel in the + Greene Lab at + the University of Pennsylvania. +

+

+ Daniel's work in the Baranzini Lab was supported by the + National Science Foundation Graduate Research Fellowship + (1144247). Sergio's work was in part supported by the + National Institutes of Health (R01 + NS088155). Development of hetnet software, applications, and algorithms in + the Greene Lab is supported by the + Gordon and Betty Moore Foundation + (GBMF4552) and through a research collaboration with + Pfizer Worldwide Research and Development. For a comprehensive list of contributors, grants, and funding sources, + see the acknowledgement sections of related publications. +

+
+
+
+ +
+
+

Cite

+
+
+ Systematic integration of biomedical knowledge prioritizes drugs for + repurposing +
+ Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, + Green A, Khankhanian P, Baranzini SE +
+ eLife (2017) + doi:10.7554/eLife.26726 +
+
+ +
+
+
+
+

+ Cite for topics:
+ eLife: Project Rephetiodrug repurposingHetionet v1.0data licensingrealtime open scienceconsensus LINCS L1000 signatureshetnets + HNEP +

+
+
+
+ Heterogeneous Network Edge Prediction: A Data Integration Approach to + Prioritize Disease-Associated Genes
+ Himmelstein DS, Baranzini SE
+ PLOS Computational Biology (2015) + doi:10.1371/journal.pcbi.1004259 +
+
+ +
+
+
+
+

+ Cite for topics:
+ PLOS: DWPCpredicting disease-gene associationshetnet permutationHNEP +

+
+
+
+
+
+
+

Contact

+

+ For questions or feedback regarding existing projects, please open an + issue on the relevant GitHub repository (under + @hetio or + @dhimmel). For other inquiries, send an + email to + daniel.himmelstein@gmail.com. +

+
+
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Compound
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Symptom
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Side Effect
Side Effect
Pharma-cologic Class
Pharma-cologic Class
Biologic Class
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Cellular Component
Cellular Component
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Pathway
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Gene
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Anatomy
Anatomy
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px per sec^2 +const minAlpha = 0.25; // resting dot opacity +const maxAlpha = 1; // peak dot opacity +const alphaSpeed = 0.1; // how fast alpha transitions, in % per frame +const minDist = 10; // dist below which dots aren't visibly connected +const midDist = 30; // distance at which dots are most visibly connected +const maxDist = 100; // dist above which dots aren't visibly connected +// special animation settings +const pathMaxLength = 6; // max number of dots in a random path +const pathMinDistance = 50; // min distance between dots in a path +const pathMaxDistance = 150; // max distance between dots in a path +const pathMaxAngle = 60; // max angle between to successive lines in a path +const pathGlowTime = 2000; // time that glow lasts in milliseconds +const pathGlowCascade = 20; // milliseconds between path elements glowing +const rippleGlowSpeed = 300; // speed of ripple in px per sec +const rippleGlowTime = 500; // time that glow lasts in milliseconds +const glowInterval = 5000; // milliseconds between glows +const rippleOdds = 7; // 1/n chance that glow will be ripple, not path + +// global vars +const canvas = document.querySelector('canvas'); +const ctx = canvas.getContext('2d'); +let dots = []; +let lines = []; +let width = 0; +let height = 0; +let resizeTimer = null; + +// dot object +class Dot { + // initialize instance + constructor(x, y) { + // make boundaries around initial grid point + this.left = x - spacing / 2 + radius; + this.top = y - spacing / 2 + radius; + this.right = x + spacing / 2 - radius; + this.bottom = y + spacing / 2 - radius; + + // place randomly within boundaries + this.x = this.left + (this.right - this.left) * Math.random(); + this.y = this.top + (this.bottom - this.top) * Math.random(); + + // random speed and angle + const speed = minSpeed + (maxSpeed - minSpeed) * Math.random(); + const angle = Math.random() * 2 * Math.PI; + this.vx = Math.cos(angle) * speed; + this.vy = -Math.sin(angle) * speed; + + // other props + this.color = colors[Math.floor(Math.random() * colors.length)]; + this.alpha = 0; + this.targetAlpha = minAlpha; + } + // get distance from this dot to specified point + distanceTo(x, y) { + return Math.sqrt(Math.pow(x - this.x, 2) + Math.pow(y - this.y, 2)); + } + // get angle (in radians) from this dot to specified point + angleTo(x, y) { + return Math.atan2(y - this.y, x - this.x); + } + // get list of other dots in order of closeness to this one + findClosest(list) { + list = list || dots; + const closest = []; + for (const dot of list) { + if (dot !== this) { + closest.push({ + dot: dot, + dist: this.distanceTo(dot.x, dot.y) + }); + } + } + closest.sort((a, b) => a.dist - b.dist); + return closest; + } + // calculate values + step() { + // bounce off boundaries + if (this.x < this.left) + this.vx += contain / fps; + if (this.y < this.top) + this.vy += contain / fps; + if (this.x > this.right) + this.vx -= contain / fps; + if (this.y > this.bottom) + this.vy -= contain / fps; + + // limit velocity + if (this.vx < -maxSpeed) + this.vx = -Math.abs(this.vx); + if (this.vy < -maxSpeed) + this.vy = -Math.abs(this.vy); + if (this.vx > maxSpeed) + this.vx = Math.abs(this.vx); + if (this.vy > maxSpeed) + this.vy = Math.abs(this.vy); + + // increment position + this.x += this.vx / fps; + this.y += this.vy / fps; + + // increment alpha + this.alpha += (this.targetAlpha - this.alpha) * alphaSpeed; + } + // draw instance + draw() { + const blendAmount = (this.alpha - minAlpha) / (maxAlpha - minAlpha); + const color = blendColors(greyColor, this.color, blendAmount); + ctx.globalAlpha = this.alpha; + ctx.fillStyle = color; + ctx.beginPath(); + ctx.arc(this.x, this.y, radius, 0, 2 * Math.PI); + ctx.fill(); + } +} + +// line object +class Line { + // initialize instance + constructor(startDot, endDot) { + this.startDot = startDot; + this.endDot = endDot; + this.x1; + this.y1; + this.x2; + this.y2; + this.alpha = 0; + this.targetAlpha = minAlpha; + } + // find line that links specified dots together + static lookup(dotA, dotB) { + for (const line of lines) { + if ( + (line.startDot === dotA && line.endDot === dotB) || + (line.startDot === dotB && line.endDot === dotA) + ) + return line; + } + } + // get distance from this line (midpoint) to specified point + distanceTo(x, y) { + const midX = (this.x1 + this.x2) / 2; + const midY = (this.y1 + this.y2) / 2; + return Math.sqrt(Math.pow(x - midX, 2) + Math.pow(y - midY, 2)); + } + // calculate values + step() { + const dist = this.startDot.distanceTo(this.endDot.x, this.endDot.y); + const angle = this.startDot.angleTo(this.endDot.x, this.endDot.y); + this.x1 = this.startDot.x + Math.cos(angle) * radius; + this.y1 = this.startDot.y + Math.sin(angle) * radius; + this.x2 = this.endDot.x - Math.cos(angle) * radius; + this.y2 = this.endDot.y - Math.sin(angle) * radius; + + if (this.targetAlpha !== maxAlpha) { + this.targetAlpha = + Math.min( + (-minDist + dist) * (2 / (midDist - minDist)), + (-maxDist + dist) * (2 / (midDist - maxDist)) + ) * minAlpha; + this.targetAlpha = Math.max(Math.min(this.targetAlpha, 1), 0); + } + + this.alpha += (this.targetAlpha - this.alpha) * alphaSpeed; + } + // draw instance + draw() { + // for performance, skip drawing if not/barely visible + if (this.alpha < 0.01) + return; + + ctx.globalAlpha = this.alpha; + ctx.strokeStyle = greyColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.moveTo(this.x1, this.y1); + ctx.lineTo(this.x2, this.y2); + ctx.stroke(); + } +} + +// create dots +function generateDots() { + // reset + dots = []; + + // evenly space + const offsetX = (width % spacing) / 2; + const offsetY = (height % spacing) / 2; + for (let x = offsetX; x < width; x += spacing) { + for (let y = offsetY; y < height; y += spacing) + dots.push(new Dot(x, y)); + } + + // hard limit dots for performance + while (dots.length > limitNumber) + dots.splice(Math.floor(dots.length * Math.random()), 1); +} + +// create lines +function generateLines() { + // reset + lines = []; + + // connect each pair of dots + for (let a = 0; a < dots.length; a++) { + const dotA = dots[a]; + for (let b = 0; b < dots.length; b++) { + const dotB = dots[b]; + // triangular matrix to avoid duplicates and lines to self + if (a < b) + lines.push(new Line(dotA, dotB)); + } + } +} + +// periodically glow +function glow() { + if (Math.floor(Math.random() * rippleOdds) === 0) + rippleGlow(); + else + pathGlow(); +} + +// glow dots and lines in ripple outward from center +function rippleGlow() { + // reset any currently glowing elements to rest + dots.concat(lines).forEach((element) => { + if (element.targetAlpha === maxAlpha) + element.targetAlpha = minAlpha; + }); + + // combine dots and lines into list + // only include lines that are currently visible above certain threshold + const elements = dots.concat( + lines.filter((line) => line.targetAlpha > minAlpha * 0.5) + ); + + for (const element of elements) { + // calc distance to center + const dist = element.distanceTo(width / 2, height / 2); + // calc delay based on dist + const delay = dist * (1000 / rippleGlowSpeed); + // glow and unglow after delays + window.setTimeout(() => { + element.targetAlpha = maxAlpha; + window.setTimeout(() => (element.targetAlpha = minAlpha), rippleGlowTime); + }, delay); + } +} + +// glow a random path +function pathGlow() { + const path = getGoodPath(); + function setGlow(value) { + let delay = 0; + for (const element of path) { + window.setTimeout(() => (element.targetAlpha = value), delay); + delay += pathGlowCascade; + } + } + setGlow(maxAlpha); + window.setTimeout(() => setGlow(minAlpha), pathGlowTime); +} + +// get a good random path +function getGoodPath() { + // get a few random paths and pick longest + const paths = []; + for (let count = 0; count < 10; count++) + paths.push(getPath()); + paths.sort((a, b) => b.length - a.length); + return paths[0]; +} + +// get a random path through dots +function getPath() { + // start with randomly picked dot + if (dots.length <= 0) + return []; + const randomDot = dots[Math.floor(dots.length * Math.random())]; + const path = []; + path.push(randomDot); + + // run loop to get desired length of path + for (let count = 0; count < pathMaxLength - 1; count++) { + // get current and previous dots + const thisDot = path[path.length - 1]; + let prevDot; + if (count > 0) + prevDot = path[count - 1]; + + // get list of closest dots to current dot + let list = thisDot.findClosest(); + + // remove dots that are too close or too far + list = list.filter( + (entry) => entry.dist > pathMinDistance && entry.dist < pathMaxDistance + ); + + // remove dots whose angles are too far away from previous angle + if (prevDot) { + list = list.filter((entry) => { + const nextDot = entry.dot; + // get angle between prev line and next line + const crossProduct = + (thisDot.x - prevDot.x) * (nextDot.y - thisDot.y) - + (thisDot.y - prevDot.y) * (nextDot.x - thisDot.x); + const dotProduct = + (thisDot.x - prevDot.x) * (nextDot.x - thisDot.x) + + (thisDot.y - prevDot.y) * (nextDot.y - thisDot.y); + const angle = Math.abs(Math.atan2(crossProduct, dotProduct)); + return angle < pathMaxAngle * (Math.PI / 180); + }); + } + + // if no viable dots, end path + if (!list || list.length < 1) + break; + + // add top dot candidate on list to path + path.push(list[0].dot); + } + + // insert connecting lines between each pair of dots + for (let index = 1; index < path.length; index++) { + path.splice(index, 0, Line.lookup(path[index - 1], path[index])); + index++; + } + + return path; +} + +// blend two hex colors together by an amount +function blendColors(colorA, colorB, amount) { + const [rA, gA, bA] = colorA.match(/\w\w/g).map((c) => parseInt(c, 16)); + const [rB, gB, bB] = colorB.match(/\w\w/g).map((c) => parseInt(c, 16)); + const r = Math.round(rA + (rB - rA) * amount) + .toString(16) + .padStart(2, '0'); + const g = Math.round(gA + (gB - gA) * amount) + .toString(16) + .padStart(2, '0'); + const b = Math.round(bA + (bB - bA) * amount) + .toString(16) + .padStart(2, '0'); + return '#' + r + g + b; +} + +// wipe canvas to start fresh for next frame +function clearCanvas() { + ctx.clearRect(0, 0, width, height); +} + +// update canvas DOM width/height to match CSS width/height +function resizeCanvas() { + const scaleFactor = dpi / 96; + width = canvas.clientWidth; + height = canvas.clientHeight; + canvas.width = width * scaleFactor; + canvas.height = height * scaleFactor; + ctx.scale(scaleFactor, scaleFactor); +} + +// run one frame of simulation +function frame() { + clearCanvas(); + dots.forEach((dot) => dot.step()); + lines.forEach((line) => line.step()); + dots.forEach((dot) => dot.draw()); + lines.forEach((line) => line.draw()); +} + +// when window is resized +function onResize() { + window.clearTimeout(resizeTimer); + resizeTimer = window.setTimeout(start, 100); +} + +// start/restart simulation +function start() { + window.clearTimeout(resizeTimer); + resizeCanvas(); + generateDots(); + generateLines(); +} + +// start app +window.setInterval(glow, glowInterval); +window.setInterval(frame, 1000 / fps); +window.addEventListener('resize', onResize); +window.setTimeout(start, 1000); diff --git a/media/hetnet-visualization.png b/disease-genes/disease-genes.png similarity index 100% rename from media/hetnet-visualization.png rename to disease-genes/disease-genes.png diff --git a/explore/connectivity-search.png b/explore/connectivity-search.png new file mode 100644 index 0000000000000000000000000000000000000000..d99b21ffeffa906a7f736b810985b182512c58d1 GIT binary patch literal 143966 zcmeFYXHXPfxGswFAqoPb1SP8EoFr$FoHGmzqmpxuLll7z5Rfbq2Fc8jb4EpS&KV?U zkTe4W45!Dvch$LdtM1u9PSv?TPFFQ_^>nZGuICNwO&zJFp-4nPMSz2YL!_+qMh6E6 zcN=@&zX#k=&z)EY{ylI}GIqzoA^wFu?<8{&Q{&(~$5DPGtLKxkzZhgRfyz9(q2%TL z{q%d~r@OB+{vjt_BYF7dv2v%+2NUyG1@`91yw2SWc-fb8l zg?Vq)20KCtKzI$!mWQ`aF&}-){h)XNp*T33dKaeNf3I*JjoiUr@cwgG?C*_xGBWts 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+
+
+
+

Neo4j Browser

+
+
+

+ Neo4j is a third-party graph database that supports the Cypher + language for querying and visualizing hetnets. Users can make advanced + queries on Hetionet right away — without downloading anything + — by using the public Neo4j browser app hosted here. The + connectivity search above uses Neo4j for some of its real-time + computations and queries. Neo4j also provides an API, which makes it + possible for developers to create their own services/apps that query + Hetionet. +

+ Open the Neo4j browser +
+
+ +
+
+
+
+
+
+
+

Download

+
+
+

+ Hetionet is available in multiple machine-readable formats: +

+
    +
  • JSON .json (readable by hetnetpy)
  • +
  • + tablular .tsv (alternative when JSON not + possible/practical) +
  • +
  • Neo4j .graph (for import into Neo4j)
  • +
  • + matrix .npy .npz (readable by + hetmatpy) +
  • +
+

+ Users looking to perform more advanced queries and analyses not + supported by the web apps above can use these datasets for local + processing. +

+ View the dataset on GitHub +
+
+ +
+
+
+
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position: relative; + min-height: 100vh; + display: flex; + justify-content: center; + align-items: center; + flex-direction: column; +} +h1 { + margin: 0; + font-size: 42px; + font-weight: 500; + letter-spacing: 5px; +} +h2 { + margin: 0; + font-size: 22px; + font-weight: 500; + text-transform: uppercase; + width: 100%; + text-align: center; +} +h3 { + margin: 0; + margin-bottom: 20px; + font-size: 18px; + font-weight: 700; + text-align: left; + width: 100%; +} +a { + color: #c0ecf8; + color: #81d9f2; + color: #41c6eb; + color: #02b3e4; +} +p:first-child, +ul:first-child, +ol:first-child { + margin-top: 0; +} +p:last-child, +ul:last-child, +ol:last-child { + margin-bottom: 0; +} +code { + font-family: 'Roboto Mono', monospace; + font-weight: 500; + font-size: 0.9em; + background: #e0e0e0; + padding: 0 3px; +} +figure { + display: block; + margin: 0; + padding: 20px; + text-align: center; +} +figure img { + max-width: 100%; + max-height: 100vh; +} +figcaption { + margin-top: 1em; + text-align: center; +} +sup { + font-size: 0.65em; + top: -2px; + position: relative; +} +iframe { + border: none; +} + +/* -------------------------------------------------- */ +/* page section styles */ +/* -------------------------------------------------- */ +main { + flex-grow: 1; +} +header, +footer { + text-align: center; + background: #303030; + color: #f0f0f0; + padding: 30px; +} +header a, +footer a { + color: #f0f0f0; +} +header, +main, +footer { + box-sizing: border-box; + width: 100%; +} +.title_section { + background: #e0e0e0; +} +.section { + margin: 0 auto; + max-width: 640px; + padding: 30px; +} +.row { + display: flex; + flex-direction: row; + flex-wrap: wrap; + align-items: flex-start; + margin-top: 20px; + margin-bottom: 20px; +} +.row:first-child { + margin-top: 0; +} +.row:last-child { + margin-bottom: 0; +} +.col_text { + flex-grow: 1; + flex-basis: 0; +} +.col_image { + margin-left: 20px; + text-align: center; +} +hr { + background: #e0e0e0; + border: none; + height: 2px; + margin: 0 auto; + max-width: 640px; +} +@media screen and (max-width: 740px) { + .col_image { + margin-left: unset; + margin-top: 20px; + flex-basis: 100%; + } +} + +/* -------------------------------------------------- */ +/* nav bar styles */ +/* -------------------------------------------------- */ +nav { + display: flex; + justify-content: center; + align-items: center; + flex-wrap: wrap; + width: 100%; +} +.logo_button { + box-sizing: border-box; + display: block; + width: 75px; + height: 75px; + margin: 0 auto 10px auto; + padding: 10px; +} +.logo_button img { + height: 100%; +} +.nav_button { + box-sizing: border-box; + display: inline-flex; + justify-content: center; + align-items: center; + position: relative; + color: #f0f0f0; + text-decoration: none; + width: 110px; + height: 50px; + margin: 0 5px; + font-weight: 500; + transition: background 0.25s ease; +} +.nav_button[data-active='true'] { + background: rgba(255, 255, 255, 0.1); +} +.nav_button:hover, +.nav_button:focus { + background: rgba(255, 255, 255, 0.25); +} +.nav_button:before { + content: ''; + position: absolute; + height: 2px; + bottom: 0; + left: 0; + right: 0; + background: rgba(255, 255, 255, 0.25); + opacity: 1; + transition: opacity 0.25s ease, left 0.25s ease, right 0.25s ease; +} +.nav_button:hover:before, +.nav_button:focus:before { + left: 50%; + right: 50%; + opacity: 0; +} +.nav_button[title]:after { + box-sizing: border-box; + content: attr(title); + display: flex; + font-size: 0.85em; + text-align: center; + justify-content: center; + align-items: center; + position: absolute; + left: 0; + right: 0; + top: 100%; + background: #303030; + padding: 10px 3px; + opacity: 0; + transition: opacity 0.25s ease; + z-index: 2; + pointer-events: none; +} +.nav_button[title]:hover:after, +.nav_button[title]:focus:after { + opacity: 1; +} +@media screen and (max-width: 740px) { + .logo_button { + width: 100%; + } + .nav_button { + width: 100%; + } + .nav_button:hover, + .nav_button:focus { + background: none; + } + .nav_button[title]:after { + left: 0; + top: 0; + right: 0; + bottom: 0; + } +} + +/* -------------------------------------------------- */ +/* input and button styles */ +/* -------------------------------------------------- */ +input { + font-family: 'Montserrat', 'Raleway', sans-serif !important; + background: #e0e0e0 !important; + border: none; + transition: background 0.25s ease; +} +input:focus { + background: #c0ecf8 !important; +} +textarea { + font-size: 1em; + overflow: scroll; + white-space: nowrap; +} +.button, +button { + box-sizing: border-box; + display: inline-flex; + justify-content: center; + align-items: center; + margin: 2px; + padding: 5px 10px; + border: none; + background: none; + text-decoration: none; + font-size: 1em; + font-weight: 500; + user-select: none; + transition: background 0.25s ease, color 0.25s ease; +} +.button:hover, +button:hover, +.button:focus, +button:focus { + background: #e0e0e0; + color: #303030; + cursor: pointer; +} +.button > svg:not(:first-child), +button > svg:not(:first-child) { + margin-left: 5px; +} +.button > svg:not(:last-child), +button > svg:not(:last-child) { + margin-right: 5px; +} +.sort_button { + background: none; + width: 100%; + height: 100%; + margin: 0; + padding-top: 2px; + padding-bottom: 2px; + text-align: unset; + color: #808080; +} +.sort_button > svg { + margin-left: 5px; +} +.checkbox_button { + width: 15px; + height: 15px; + padding: 0; + background: #e0e0e0; +} +.checkbox_button:hover, +.checkbox_button:focus { + background: #d0d0d0; +} + +/* -------------------------------------------------- */ +/* general utility styles */ +/* -------------------------------------------------- */ +.small { + font-size: 0.8em; +} +.light { + color: #808080; +} +.blue { + color: #02b3e4; +} +.semibold { + font-weight: 600; +} +.left { + text-align: left !important; + justify-content: left !important; + justify-self: left !important; +} +.center { + text-align: center !important; + justify-content: center !important; + justify-self: center !important; +} +.right { + text-align: right !important; + justify-content: right !important; + justify-self: right !important; +} +.nowrap { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} +.line_sides { + display: flex; + flex-direction: row; +} +.line_sides:before, +.line_sides:after { + content: ''; + flex-grow: 1; + background: #e0e0e0; + height: 2px; + margin: auto; +} +.line_sides:before { + margin-right: 10px; +} +.line_sides:after { + margin-left: 10px; +} + +/* -------------------------------------------------- */ +/* table styles */ +/* -------------------------------------------------- */ +.table_container { + text-align: center; + box-sizing: border-box; + max-height: 50vh; + overflow: auto; +} +@media screen and (min-width: 320px) { + .table_container[data-expanded='true'] { + overflow-x: auto; + width: calc(100vw - 20px - 20px); + position: relative; + left: 50%; + transform: translateX(-50%); + } +} +table { + border-collapse: collapse; + width: 100%; + table-layout: fixed; + margin: 0 auto; +} +th { + position: sticky; + top: 0; + z-index: 99; + background: #eaeaea; +} +th, +td { + box-sizing: border-box; + padding: 0; + height: 28px; +} +td > * { + vertical-align: middle; +} +.col_xs { + width: 30px; +} +.col_s { + width: 80px; +} +.col_m { + width: 100px; +} +.col_l { + width: 200px; +} +.col_xl { + width: 300px; +} +.col_xxl { + width: 600px; +} + +/* -------------------------------------------------- */ +/* icon styles */ +/* -------------------------------------------------- */ +/* prevent icon from wrapping to new line alone */ +.icon { + white-space: nowrap; +} +/* prevent icon from wrapping to new line alone */ +.icon:before { + content: ''; +} +.fas[data-right], +.fab[data-right] { + margin-left: 10px; +} +.fas[data-left], +.fab[data-left] { + margin-right: 10px; +} + +/* -------------------------------------------------- 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    HNEP Heterogeneous Network Edge Prediction

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    Heterogeneous Network Edge Prediction

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    The goal of HNEP is to produce biologically-meaningful predictions by integrating multiple high-throughput data sources. The approach computes features describing the network topology connecting two nodes. These features are used as input to a machine learning method which predicts the probability that an edge exists.

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    We adapted an alorithm originally developed for social network analysis called PathPredict -[pdf, -doi]. -Our extensions to this method focused on scalability and performance. -

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    Methodology of Metapath-Based Edge Prediction

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    Here we show the process of calculating features that describe the network topology between two nodes. Features are used as predictors for subsequent modeling.

    - Visualization of HNEP Methodology -
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    1. We constructed the network according to a schema, called a metagraph, which is composed of metanodes (node types) and metaedges (edge types).
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    3. The network topology connecting a gene and disease node is measured along metapaths (types of paths). Starting on Gene and ending on Disease, all metapaths length three or less are computed by traversing the metagraph.
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    5. A hypothetical graph subset showing select nodes and edges surrounding IRF1 and multiple sclerosis. To characterize this relationship, features are computed that measure the prevalence of a specific metapath between IRF1 and multiple sclerosis.
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    7. Two features (for the GeTlD and GiGaD metapaths) are calculated to describe the relationship between IRF1 and multiple sclerosis. The metric underlying the features is degree-weighted path count (DWPC). First, for the specified metapath, all paths are extracted from the network. Next, each path receives a path-degree product measuring its specificity (calculated from node-degrees along the path, Dpath). This step requires a damping exponent (here w = 0.5), which adjusts how severely high-degree paths are downweighted. Finally, the path-degree products are summed to produce the DWPC.
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  • + Two features (for the GeTlD and GiGaD metapaths) are + calculated to describe the relationship between IRF1 and multiple + sclerosis. The metric underlying the features is degree-weighted path + count (DWPC). First, for the specified metapath, all paths are + extracted from the network. Next, each path receives a path-degree + product measuring its specificity (calculated from node-degrees along + the path, Dpath). This step requires a damping exponent (here w = 0.5), which + adjusts how severely high-degree paths are downweighted. Finally, the + path-degree products are summed to produce the DWPC. +
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    Hetnets - in biomedicine -

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    Hetnets — short for heterogeneous networks — are networks with multiple node or relationship types. They provide a scalable, intuitive, and frictionless structure for data integration and translation of biomedical knowledge.

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    +--- +layout: landing +title: Hetionet - An integrative network of biomedical knowledge +description: Hetionet is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. +keywords: heterogeneous, network, hetnet, graph, gene, compound, anatomy, disease, symptom, side effect, biological process, cellular component, molecular function, pathway, pharmacologic class +icons: true +topLevel: true +--- + + +
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    + Hetionet is an integrative + network + of biomedical knowledge assembled from + 29 different databases of genes, + compounds, diseases, and more. The network combines over 50 years of + biomedical information into a single resource, consisting of + 47,031 nodes (11 types) and 2,250,197 relationships (24 + types). +

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    + Hetionet enables scientists and biologists to formulate novel hypotheses, + predictions, and other valuable insights by connecting an existing body of + biomedical data across multiple levels and types in a convenient, + accessible, holistic way. +

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    Hetionet (Biomedical Hetnet)

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    - Hetionet is a network that integrates knowledge from over 50 years of biomedical into a single resource. Hetionet encodes biology through its 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Version 1.0 was created for Project Rephetio and is therefore ideally suited for drug repurposing. We host a public Neo4j Browser allowing setup-free interaction with Hetionet. -

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    - Neo4j Browser » - Data on GitHub » -

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    Project Rephetio (Drug Repurposing)

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    - Project Rephetio used Hetionet v1.0 to predict new uses for existing compounds. Our hetnet edge prediction method learned a systematic model of drug efficacy, which we applied to predict the probability of treatment between 1,538 approved small molecule compounds and 136 complex diseases. Project Rephetio is entirely open: check out the Thinklab for discussion and navigativing to code or data. -

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    - Prediction Browser » - Publication » - Thinklab Project » - GraphGist Tutorial » -

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    Disease-Associated Genes (GWAS Prioritization)

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    Using heterogeneous network edge prediction on a network with 18 node types (metanodes) and 19 edge types (metaedges), we predict the probability that each protein-coding gene is associated with each of 29 complex human diseases. Reported associations from the GWAS Catalog were used to train the model. Potential applications include prioritizing GWAS analyses, determining the causal gene within a genomic region, identifying genes of biological interest, and comparing the ability of various information domains to identify pathogenic variants.

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    - More details » - Prediction Browser » - Publication » -

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    Heterogeneous Network Edge Prediction

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    The goal of HNEP is to produce biologically-meaningful predictions by integrating multiple high-throughput data sources. The approach computes features describing the network topology connecting two nodes. These features are used as input to a machine learning method which predicts the probability that an edge exists. Through evaluating the informativeness of each feature, the relevance of included domains can be compared providing insight into the influential mechanisms behind the process of interest.

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    - View details » -

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    Publications & Media

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    Browse the collection of media relating to the project. Check back here for citation information.

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    - View details » -

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    Code

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    Our python implementation of a framework for representing heterogeneous networks. Over time we plan to release additional code related to this project. We welcome contributions from the open source community.

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    - View details » -

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    About

    -

    This project was developed by - Daniel Himmelstein -and - Sergio Baranzini -from the - Baranzini Lab -at - UCSF. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1144247. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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    Contact

    -

    Contact - Sergio Baranzini - or - Daniel Himmelstein - by email. Ask questions and please report any - - or - - you find.

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    Publications & Media

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    Publications

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    - Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE (2017) Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife doi:10.7554/eLife.26726

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    - Himmelstein DS, Baranzini SE (2015) Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes. PLOS Computational Biology doi:10.1371/journal.pcbi.1004259

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    Poster from ASHG 2014 also at F1000Posters

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    Article in Stanford's Biomedical Computation Review

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    Heterogeneous Network Visualization takes Silver

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    Our visualization - took second place - in the Most Aesthetically Pleasing category of the - Cytoscape 3.2 Lauch Challenge. - The results were announced on December 24, 2014.

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    Title: Heterogeneous Network Visualization with Cytoscape: An Integrative Approach to the Genetics of Complex Human Disease

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    Description: To provide context for understanding disease-gene associations, we integrated public resources into a heterogeneous network. The network contains 40,343 nodes (across 18 types) and 1,608,168 edges (across 19 types). Using the network's topology, we learned influential mechanisms underlying pathogenesis and predicted the probability that each gene was associated with each disease.

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    ISMB Late Breaking Research Video Proposal

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    A short video describing the use of HNEP for predicting disease-associated genes.

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    +
    +
    +
    +

    hetmatpy

    +
    +
    +

    + Hetmatpy is a Python 3 package storing and operating on hetnets + in matrix form. Representing the network in this way enables counting + paths using matrix multiplication, which provides dramatically better + performance compared to the equivalent hetio or Neo4j + implementations. Hetmatpy depends on hetio for + representing the metagraph (network schema), but is better-suited for + certain large-scale computations. +

    + View on GitHub +
    +
    + +
    +
    +
    +
    +
    +
    +
    +

    xswap

    +
    +
    +

    + Xswap is a Python for creating randomized degree-preserving + permutations of networks using the XSwap technique. Generating many + permuted networks enables creationg null distributions for network + metrics and computing the prior probability that an edge exists based + on its source and target degree. The computation-intensive portions of + the package are implemented in C/C++ to provide rapid permutation and + significantly better performance than the legacy hetio pure + Python implementation. +

    + View on GitHub +
    +
    + +
    +
    +
    +
    diff --git a/software/xswap.svg b/software/xswap.svg new file mode 100644 index 0000000..586da8e --- /dev/null +++ b/software/xswap.svg @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/studies/index.html b/studies/index.html new file mode 100644 index 0000000..0294401 --- /dev/null +++ b/studies/index.html @@ -0,0 +1,49 @@ +--- +layout: default +title: Studies +icon: fas fa-microscope +category: studies +--- + +
    +
    +

    Drug Repurposing (Project Rephetio)

    +
    +
    +

    + Used hetnet edge prediction to predict whether each of 1,538 compounds + treats each of 136 diseases. Browse this study's 209,168 predictions + and the evidence behind them to explore new uses for existing drugs. +

    + Go to the project page +
    +
    + +
    +
    +
    +
    +
    +
    +
    +

    Prioritizing Disease-Associated Genes

    +
    +
    +

    + Used hetnet edge prediction for + disease-associated genes, based on a predecessor network to Hetionet. + Browse this study's 55,4364 results to prioritize genome-wide + association studies by narrowing down the search. +

    + Go to the project page +
    +
    + +
    +
    +
    +
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