index.json

[{"authors":null,"categories":null,"content":"I am a postdoc researcher in Computational and Systems Biology program of Sloan Kettering Institute working with Dr. Christina Leslie.\n  Download my CV.\n","date":1617321600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1617321600,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://karbalayghareh.github.io/author/alireza-karbalayghareh/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/alireza-karbalayghareh/","section":"authors","summary":"I am a postdoc researcher in Computational and Systems Biology program of Sloan Kettering Institute working with Dr. Christina Leslie.\n  Download my CV.","tags":null,"title":"Alireza Karbalayghareh","type":"authors"},{"authors":null,"categories":null,"content":"吳恩達 is a professor of artificial intelligence at the Stanford AI Lab. His research interests include distributed robotics, mobile computing and programmable matter. He leads the Robotic Neurobiology group, which develops self-reconfiguring robots, systems of self-organizing robots, and mobile sensor networks.\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed neque elit, tristique placerat feugiat ac, facilisis vitae arcu. Proin eget egestas augue. Praesent ut sem nec arcu pellentesque aliquet. Duis dapibus diam vel metus tempus vulputate.\n","date":1607817600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1607817600,"objectID":"da99cb196019cc5857b9b3e950397ca9","permalink":"https://karbalayghareh.github.io/author/%E5%90%B3%E6%81%A9%E9%81%94/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/%E5%90%B3%E6%81%A9%E9%81%94/","section":"authors","summary":"吳恩達 is a professor of artificial intelligence at the Stanford AI Lab. His research interests include distributed robotics, mobile computing and programmable matter. He leads the Robotic Neurobiology group, which develops self-reconfiguring robots, systems of self-organizing robots, and mobile sensor networks.","tags":null,"title":"吳恩達","type":"authors"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature.   Slides can be added in a few ways:\n Create slides using Wowchemy\u0026rsquo;s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.  Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://karbalayghareh.github.io/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":["Alireza Karbalayghareh","Merve Sahin","Christina S. Leslie"],"categories":null,"content":"","date":1617321600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1617321600,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"https://karbalayghareh.github.io/publication/preprint/","publishdate":"2021-04-02T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting non-coding genetic variation. Here we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays in order to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements and promoters, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than dilated convolutional neural networks (CNNs), the current state-of-the-art deep learning approach for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both CNNs and the recently proposed Activity-by-Contact model. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.","tags":null,"title":"Chromatin interaction aware gene regulatory modeling with graph attention networks","type":"publication"},{"authors":null,"categories":null,"content":"Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting non-coding genetic variation. Here we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays in order to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements and promoters, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than dilated convolutional neural networks (CNNs), the current state-of-the-art deep learning approach for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both CNNs and the recently proposed Activity-by-Contact model. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.\n","date":1617321600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1617321600,"objectID":"61ad11cbbe898eba8a8cbdff23af0f52","permalink":"https://karbalayghareh.github.io/project/internal-project-1/","publishdate":"2021-04-02T00:00:00Z","relpermalink":"/project/internal-project-1/","section":"project","summary":"Chromatin interaction aware gene regulatory modeling with graph attention networks","tags":["GraphReg"],"title":"GraphReg","type":"project"},{"authors":["Alireza Karbalayghareh","吳恩達"],"categories":["Demo","教程"],"content":"Overview  The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site The template can be modified and customised to suit your needs. It\u0026rsquo;s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more    The template is mobile first with a responsive design to ensure that your site looks stunning on every device.  Get Started  👉 Create a new site 📚 Personalize your site 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Guide and Release Notes  Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to become a sponsor and help support Wowchemy\u0026rsquo;s future ❤️ As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨\nEcosystem  Hugo Academic CLI: Automatically import publications from BibTeX  Inspiration Check out the latest demo of what you\u0026rsquo;ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\nFeatures  Page builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 34+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files.  Themes Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1607817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607817600,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://karbalayghareh.github.io/post/getting-started/","publishdate":"2020-12-13T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Welcome 👋 We know that first impressions are important, so we've populated your new site with some initial content to help you get familiar with everything in no time.","tags":["Academic","开源"],"title":"Welcome to Wowchemy, the website builder for Hugo","type":"post"},{"authors":null,"categories":["R"],"content":"  R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.\nYou can embed an R code chunk like this:\nsummary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00 fit \u0026lt;- lm(dist ~ speed, data = cars) fit ## ## Call: ## lm(formula = dist ~ speed, data = cars) ## ## Coefficients: ## (Intercept) speed ## -17.579 3.932  Including Plots You can also embed plots. See Figure 1 for example:\npar(mar = c(0, 1, 0, 1)) pie( c(280, 60, 20), c(\u0026#39;Sky\u0026#39;, \u0026#39;Sunny side of pyramid\u0026#39;, \u0026#39;Shady side of pyramid\u0026#39;), col = c(\u0026#39;#0292D8\u0026#39;, \u0026#39;#F7EA39\u0026#39;, \u0026#39;#C4B632\u0026#39;), init.angle = -50, border = NA )  Figure 1: A fancy pie chart.   ","date":1606875194,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606875194,"objectID":"bf1eb249db79f10ace7d22321494165a","permalink":"https://karbalayghareh.github.io/post/2020-12-01-r-rmarkdown/","publishdate":"2020-12-01T21:13:14-05:00","relpermalink":"/post/2020-12-01-r-rmarkdown/","section":"post","summary":"R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.","tags":["R Markdown","plot","regression"],"title":"Hello R Markdown","type":"post"},{"authors":null,"categories":null,"content":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.\nOn this page, you\u0026rsquo;ll find some examples of the types of technical content that can be rendered with Academic.\nExamples Code Academic supports a Markdown extension for highlighting code syntax. You can enable this feature by toggling the highlight option in your config/_default/params.toml file.\n```python import pandas as pd data = pd.read_csv(\u0026quot;data.csv\u0026quot;) data.head() ```  renders as\nimport pandas as pd data = pd.read_csv(\u0026quot;data.csv\u0026quot;) data.head()  Charts Academic supports the popular Plotly chart format.\nSave your Plotly JSON in your page folder, for example chart.json, and then add the {{\u0026lt; chart data=\u0026quot;chart\u0026quot; \u0026gt;}} shortcode where you would like the chart to appear.\nDemo:\n  (function() { let a = setInterval( function() { if ( typeof window.Plotly === 'undefined' ) { return; } clearInterval( a ); Plotly.d3.json(\"./line-chart.json\", function(chart) { Plotly.plot('chart-835261974', chart.data, chart.layout, {responsive: true}); }); }, 500 ); })();  You might also find the Plotly JSON Editor useful.\nMath Academic supports a Markdown extension for $\\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.toml file.\nTo render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.\nExample math block:\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |} {\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2}$$  renders as\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left |\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right |^2}$$\nExample inline math $\\nabla F(\\mathbf{x}_{n})$ renders as $\\nabla F(\\mathbf{x}_{n})$.\nExample multi-line math using the \\\\\\\\ math linebreak:\n$$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\\\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$  renders as\n$$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\\n1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$\nDiagrams Academic supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.\nAn example flowchart:\n```mermaid graph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] ```  renders as\ngraph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2]  An example sequence diagram:\n```mermaid sequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! ```  renders as\nsequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good!  An example Gantt diagram:\n```mermaid gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d ```  renders as\ngantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d  An example class diagram:\n```mermaid classDiagram Class01 \u0026lt;|-- AveryLongClass : Cool \u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01 Class09 --\u0026gt; C2 : Where am i? Class09 --* C3 Class09 --|\u0026gt; Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla class Class10 { \u0026lt;\u0026lt;service\u0026gt;\u0026gt; int id size() } ```  renders as\nclassDiagram Class01 \u0026lt;|-- AveryLongClass : Cool \u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01 Class09 --\u0026gt; C2 : Where am i? Class09 --* C3 Class09 --|\u0026gt; Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla class Class10 { \u0026lt;\u0026lt;service\u0026gt;\u0026gt; int id size() }  An example state diagram:\n```mermaid stateDiagram [*] --\u0026gt; Still Still --\u0026gt; [*] Still --\u0026gt; Moving Moving --\u0026gt; Still Moving --\u0026gt; Crash Crash --\u0026gt; [*] ```  renders as\nstateDiagram [*] --\u0026gt; Still Still --\u0026gt; [*] Still --\u0026gt; Moving Moving --\u0026gt; Still Moving --\u0026gt; Crash Crash --\u0026gt; [*]  Todo lists You can even write your todo lists in Academic too:\n- [x] Write math example - [x] Write diagram example - [ ] Do something else  renders as\n Write math example Write diagram example Do something else  Tables Represent your data in tables:\n| First Header | Second Header | | ------------- | ------------- | | Content Cell | Content Cell | | Content Cell | Content Cell |  renders as\n   First Header Second Header     Content Cell Content Cell   Content Cell Content Cell    Callouts Academic supports a shortcode for callouts, also referred to as asides, hints, or alerts. By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.\n{{% callout note %}} A Markdown aside is useful for displaying notices, hints, or definitions to your readers. {{% /callout %}}  renders as\n A Markdown aside is useful for displaying notices, hints, or definitions to your readers.   Spoilers Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.\n{{\u0026lt; spoiler text=\u0026quot;Click to view the spoiler\u0026quot; \u0026gt;}} You found me! {{\u0026lt; /spoiler \u0026gt;}}  renders as\nClick to view the spoiler You found me!\n Icons Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.\nHere are some examples using the icon shortcode to render icons:\n{{\u0026lt; icon name=\u0026quot;terminal\u0026quot; pack=\u0026quot;fas\u0026quot; \u0026gt;}} Terminal {{\u0026lt; icon name=\u0026quot;python\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} Python {{\u0026lt; icon name=\u0026quot;r-project\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} R  renders as\n  Terminal\n Python\n R\nDid you find this page helpful? Consider sharing it 🙌 ","date":1562889600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562889600,"objectID":"07e02bccc368a192a0c76c44918396c3","permalink":"https://karbalayghareh.github.io/post/writing-technical-content/","publishdate":"2019-07-12T00:00:00Z","relpermalink":"/post/writing-technical-content/","section":"post","summary":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.","tags":null,"title":"Writing technical content in Academic","type":"post"},{"authors":["Alireza Karbalayghareh"],"categories":[],"content":"from IPython.core.display import Image Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')     print(\u0026quot;Welcome to Academic!\u0026quot;)  Welcome to Academic!  Install Python and JupyterLab Install Anaconda which includes Python 3 and JupyterLab.\nAlternatively, install JupyterLab with pip3 install jupyterlab.\nCreate or upload a Jupyter notebook Run the following commands in your Terminal, substituting \u0026lt;MY-WEBSITE-FOLDER\u0026gt; and \u0026lt;SHORT-POST-TITLE\u0026gt; with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:\nmkdir -p \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ cd \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ jupyter lab index.ipynb  The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.\nEdit your post metadata The first cell of your Jupter notebook will contain your post metadata (front matter).\nIn Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:\n--- title: My post's title date: 2019-09-01 # Put any other Academic metadata here... ---  Edit the metadata of your post, using the documentation as a guide to the available options.\nTo set a featured image, place an image named featured into your post\u0026rsquo;s folder.\nFor other tips, such as using math, see the guide on writing content with Academic.\nConvert notebook to Markdown jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.  Example This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567641600,"objectID":"6e929dc84ed3ef80467b02e64cd2ed64","permalink":"https://karbalayghareh.github.io/post/jupyter/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/post/jupyter/","section":"post","summary":"Learn how to blog in Academic using Jupyter notebooks","tags":[],"title":"Display Jupyter Notebooks with Academic","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\n Features  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides   Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E   Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;)   Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}}  Press Space to play!\nOne  Two  Three \n A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears   Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}}  Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view    Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links    night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links   Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/media/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}   Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; }   Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://karbalayghareh.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework, in the homogeneous transfer learning scenario, where the source and target domains are related through the joint prior density of the model parameters. The modeling of joint prior densities enables better understanding of the “transferability” between domains. We define a joint Wishart distribution for the precision matrices of the Gaussian feature-label distributions in the source and target domains to act like a bridge that transfers the useful information of the source domain to help classification in the target domain by improving the target posteriors. Using several theorems in multivariate statistics, the posteriors and posterior predictive densities are derived in closed forms with hypergeometric functions of matrix argument, leading to our novel closed-form and fast Optimal Bayesian Transfer Learning (OBTL) classifier. Experimental results on both synthetic and real-world benchmark data confirm the superb performance of the OBTL compared to the other state-of-the-art transfer learning and domain adaptation methods.\n","date":1531612800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1531612800,"objectID":"51384dd3295e19f026154afcb9dc52b2","permalink":"https://karbalayghareh.github.io/project/internal-project-2/","publishdate":"2018-07-15T00:00:00Z","relpermalink":"/project/internal-project-2/","section":"project","summary":"Optimal Bayesian Transfer Learning","tags":["OBTL"],"title":"OBTL","type":"project"},{"authors":["Alireza Karbalayghareh","Xiaoning Qian","Edward R. Dougherty"],"categories":null,"content":"","date":1531612800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1531612800,"objectID":"966884cc0d8ac9e31fab966c4534e973","permalink":"https://karbalayghareh.github.io/publication/journal-article/","publishdate":"2018-07-15T00:00:00Z","relpermalink":"/publication/journal-article/","section":"publication","summary":"Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework, in the homogeneous transfer learning scenario, where the source and target domains are related through the joint prior density of the model parameters. The modeling of joint prior densities enables better understanding of the “transferability” between domains. We define a joint Wishart distribution for the precision matrices of the Gaussian feature-label distributions in the source and target domains to act like a bridge that transfers the useful information of the source domain to help classification in the target domain by improving the target posteriors. Using several theorems in multivariate statistics, the posteriors and posterior predictive densities are derived in closed forms with hypergeometric functions of matrix argument, leading to our novel closed-form and fast Optimal Bayesian Transfer Learning (OBTL) classifier. Experimental results on both synthetic and real-world benchmark data confirm the superb performance of the OBTL compared to the other state-of-the-art transfer learning and domain adaptation methods.","tags":null,"title":"Optimal Bayesian transfer learning","type":"publication"},{"authors":["Alireza Karbalayghareh","Ulisses Braga-Neto","Edward R. Dougherty"],"categories":null,"content":"","date":1488672000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488672000,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://karbalayghareh.github.io/publication/conference-paper/","publishdate":"2017-03-05T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"This paper studies the classification of gene regulatory networks (GRNs) modeled by probabilistic Boolean networks (PBNs). After observing Gaussian expression values of n genes at m consecutive time points, with consideration of missing data, an algorithm based on expectation maximization (EM) is proposed to estimate the parameters and infer the unknown parts of the networks in the maximum likelihood (ML) sense. Then the estimated values are plugged in to the Bayes classifier, which is optimal, and the performance of the classifier is investigated through various simulations.","tags":null,"title":"Classification of Gaussian trajectories with missing data in Boolean gene regulatory networks","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"f26b5133c34eec1aa0a09390a36c2ade","permalink":"https://karbalayghareh.github.io/admin/config.yml","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/admin/config.yml","section":"","summary":"","tags":null,"title":"","type":"wowchemycms"}]