poole · enryH · Jun 26, 2023 · Jun 26, 2023 · Jun 26, 2023 · Jun 26, 2023
diff --git a/1research.md b/1research.md
@@ -0,0 +1,102 @@
+---
+layout: page
+title: Research
+permalink: /research/
+---
+
+# Projects
+
+## Knowledge Graphs
+
+**Building High-quality Knowledge Graphs.** Using and developing Knowledge Graph technologies and methods to structured data and to connect them to existing biological knowledge. These structures facilitate analysis and interpretation of complex data. We are contributing to a groundbreaking field by developing tools and methods to build, assess and investigate Knowledge Graphs and applying them to solve challenges in biology and health.
+
+
+<details>
+  <summary class="research">MicW2Graph</summary>  
+  <div style="text-align: center; margin-bottom: 20px;">
+    <a href="https://github.com/Multiomics-Analytics-Group/MicW2Graph" target="_blank">
+      <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/MicW2Graph/main/images/MicW2Graph_logo.svg" alt="MicW2Graph_logo" width="250px">
+    </a>
+  </div>
+  <p>In this project, we investigated the microbiome of the <b>wastewater treatment</b> (WWT) process to build <b>MicW2Graph</b>, an open-source <b>knowledge graph</b> that integrates metagenomic and metatranscriptomic information with their biological context, including biological processes, environmental and phenotypic features, chemical compounds, and additional metadata. We developed a workflow to collect meta-omics datasets from <a href="https://www.ebi.ac.uk/metagenomics" target="_blank">MGnify</a> and infer potential interactions among microorganisms through <b>microbial association networks</b>. MicW2Graph enables the investigation of research questions related to WWT, focusing on aspects such as microbial connections, community memberships, and potential ecological functions.</p>
+  <p>The following figure shows the general workflow of the MicW2Graph project:</p>
+  <a href="#zoom-MicW2Graph-workflow">
+    <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/MicW2Graph/main/images/Methods_MicW2Graph.svg"
+        alt="MicW2Graph Workflow"
+        style="cursor: zoom-in; max-width: 100%;">
+  </a>
+  <!-- Zoom overlay -->
+  <div id="zoom-MicW2Graph-workflow" class="zoom-overlay">
+    <a href="#MicW2Graph-workflow" style="text-decoration: none;">
+      <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/MicW2Graph/main/images/Methods_MicW2Graph.svg"
+          alt="Zoomed_MicW2Graph Workflow"
+          style="max-width: 90%; max-height: 90%; cursor: zoom-out;
+                  transform: translate(5%, -5%);">
+    </a>
+  </div>
+</details>
+
+
+<details>
+  <summary class="research">KGBioGraphy</summary>  
+  <p><b>KGBioGraphy</b> is a manually-curated knowledge graph that contains information of the data sources and usecases of published biological/biomedical knowledge graphs (BKGs). Currently, there are 69 BKGs summarized within KGBioGraphy. Each BKG within KGBioGraphy is represented by an ego network, linking the BKG to its publication, data sources (e.g., databases and ontologies), node and relationship types it contains, and usecases.</p>
+  <p>We incorporated the open access BKG publications and KGBioGraphy into a corrective Retrievcal-Augmented Generation (RAG) model which provides a large language model (LLM) with a context-rich prompt to improve LLM response performance. <i>Coming soon:</i> Users will soon be able to interact with the KG-BioGraphy RAG through a streamlit interface and API.</p>
+  <p>The following is a flow diagram of the Retrieval-Augmented Generation (RAG) model and KG-BioGraphy:</p>
+  <a href="#zoom-KGBioGraphy-workflow">
+    <img src="{{ site.baseurl }}/public/assets/BKGR LLM-RAG neo4j model.png"
+        alt="KGBioGraphy Workflow"
+        style="cursor: zoom-in; max-width: 100%;">
+  </a>
+  <!-- Zoom overlay -->
+  <div id="zoom-KGBioGraphy-workflow" class="zoom-overlay">
+    <a href="#KGBioGraphy-workflow" style="text-decoration: none;">
+      <img src="{{ site.baseurl }}/public/assets/BKGR LLM-RAG neo4j model.png"
+          alt="Zoomed_KGBioGraphy Workflow"
+          style="max-width: 90%; max-height: 90%; cursor: zoom-out;
+                  transform: translate(5%, -5%);">
+    </a>
+  </div>
+  <p class="figure">(A) RAG Architecture workflow.</p> 
+  <p class="figure">(B) A vector database (DB) comprising vector representations of text snippets from the publications included in the review.</p>
+  <p class="figure">(C) KG-BioGraphy Neo4j DB.</p>
+  <p class="figure">(D) The retrieved contexts are used to query 1) the LLM and 2) the KG-BioGraphy DB to generate a text and subgraph response, respectively, which is outputed to the user.</p>
+  <p class="figure" style="font-size: 0.6rem;">This figure was created on Biorender.com.</p>
+</details>
+
+------------------------
+
+## Graph Machine Learning
+
+**Developing and Applying Novel Methods on Graphs.** Unleashing the power of Machine Learning on Graphs, a cutting-edge approach to extracting valuable insights from network data. We explore how this fusion of machine learning and graph theory helps to recognize patterns, generate predictions, and discovering new knowledge across a multitude of applications, including biological and medical networks.
+
+------------------------
+
+## Microbial Communities
+
+**Exploring Microbial Communities and their Environments.** Integrating multiple biological resources to unravel the assembly, interaction and adaptation mechanisms of microbial networks, offering insights into their functions and inpact on ecosystems, and how changes affect those communities.
+
+
+<iframe src="{{ site.baseurl }}/public/cluster_8.html" width="600" height="450" style="border:0;"></iframe>
+
+
+
+------------------------
+
+## Multimodal Data
+
+**Implementing tools to process, integrate, and analyse multimodal data.** Diving into the benefits of harmonising multimodal data that converge to provide a comprehensive view of complex biological systems. Specifically we are interested in high-throughput multi-omics data generated using Mass spectrometry technology (proteomics and metabolomics) and metaomics data (metagenomics and metaproteomics).
+
+------------------------
+
+## Open Science
+
+**Data Science Democratisation.** Focusing on data literacy training as a means to reduce inequality, and promoting open science by making all research, data content, and software open and accessible.
+
+
+****
+****
+
+
+# Publications
+
+Ayala-Ruano, S., Webel, H., & Santos, A. (2025). _VueGen: Automating the generation of scientific reports_. bioRxiv. [https://doi.org/10.1101/2025.03.05.641152](https://doi.org/10.1101/2025.03.05.641152)
diff --git a/2ds_platform.md b/2ds_platform.md
@@ -0,0 +1,27 @@
+---
+layout: page
+title: DS Platform
+permalink: /data-science-platform/
+---
+
+# Data Science Platform
+
+<span style="color:grey">
+    Supporting and Promoting Data Science
+</span>
+
+The primary objective of the Data Science Platform (DSP) is to make data science more accessible and inclusive across the Centre and the DTU community. 
+
+The DSP is built on four pillars: **support, education, innovation, and tool development**
+
+- **Support** provide researchers and core functions at the Centre with assistance in areas such as statistics, programming, analytics, and machine learning. DSP will adopt a collaborative approach to understand the research questions and then provide the best assistance. This collaboration will be defined as consultancy, training, or implementation, depending on the needs. Whenever possible, DSP will prioritize consultancy and training to empower researchers, especially young ones, to learn data science skills that are crucial for their careers.
+
+- **Education** develop a training program focused on data science literacy, guiding researchers through different levels to become data-aware and proficient in data science. The training program includes initiatives such a data science club that holds periodic meetings to help researchers develop their data literacy skills, data science and bioinformatics workshops, and pop-up meetings to present trending technologies.
+
+- **Innovation** seeks to introduce researchers to new computational biology methods and technologies, and modernize the data science landscape at the Centre. This pillar aims to help researchers stay ahead of the curve by learning the latest technologies and techniques.
+
+- **Tool development** aims to transform the outputs of the other pillars into standard tools that can be shared across the Centre and open sourced for the entire community to use. This will create a comprehensive set of tools easily accessible to researchers.
+
+## Data Science Platform Notes
+
+We have our own notes repository which you can find at [biosustain.github.io/dsp_notes/](https://biosustain.github.io/dsp_notes/). It contains technical notes related to our work and links to our teaching materials.
diff --git a/3tools.md b/3tools.md
@@ -0,0 +1,123 @@
+---
+layout: page
+title: Tools
+permalink: /tools/
+---
+
+<details open>
+  <summary>Clinical Knowledge Graph - CKG</summary>
+  <h3>Integrating and Interpreting MS-based Proteomics Data</h3>
+  <p>The Clinical Knowledge Graph (CKG) is a graph database with millions of nodes and relationships representing experimental data, databases, and scientific literature. Further, CKG is an open-source platform that allows easy expansion with new databases and experimental data types and it includes statistical and machine learning tools for faster proteomics analysis.</p>
+  <p>We are currently working on a new version that will be more flexible and generalizable. <b>Stay tuned!</b></p>
+  <a href="#zoom-ckg-workflow">
+    <img src="{{ site.baseurl }}/public/ckg.jpeg"
+        alt="ckg Workflow"
+        style="cursor: zoom-in; max-width: 100%;">
+  </a>
+  <!-- Zoom overlay -->
+  <div id="zoom-ckg-workflow" class="zoom-overlay">
+    <a href="#ckg-workflow" style="text-decoration: none;">
+      <img src="{{ site.baseurl }}/public/ckg.jpeg"
+          alt="zoomed ckg Workflow"
+          style="max-width: 90%; max-height: 90%; cursor: zoom-out;
+                  transform: translate(5%, -5%);">
+    </a>
+  </div>
+</details>
+
+
+<details open>
+  <summary>OrthoHPI 2.0</summary>
+  <h2>Orthology–based Host–Parasite Protein–protein Interactions <a href="https://orthohpi.streamlit.app/">[link]</a></h2>
+  <p>Understanding host-parasite interactions is vital for tackling infectious diseases. Due to the challenges in experimental identification, we propose a computational method to predict protein interactions between human and 18 eukaryotic parasites. Our approach leverages an orthology-based transfer of interactions, focusing on parasite secretomes and human membrane proteins. We also filter the host proteome based on the parasites' specific tissue tropisms. In this version, we added  cell-type specific expression annotations to provide further resolution of the host-parasite predicted interactions and we support interactions with structural information.</p>
+  <h3>Giardia intestinalis Predicted PPI</h3>
+  <iframe src="{{ site.baseurl }}/public/Gi_network.html" width="600" height="450" style="border:0;"></iframe>
+</details>
+
+
+<details open>
+  <summary>Analytics Core (Acore) Library</summary>  
+  <p>Acore, short for analytics core, is an open-source Python library to preprocess and analyse multi-omics data. It includes functionality related to preprocessing, e.g. for data normalization, missing value imputation or feature selection, and functionality for statistical data analysis, e.g. an analysis of covariance.</p> 
+  <p>Acore is designed to be user-friendly and flexible, allowing users to easily apply different analysis strategies, testing effects of choosing specific steps.</p>
+  <h4>Check out current recipes and the core libary documentation at <a href="https://analytics-core.readthedocs.io/">analytics-core.readthedocs.io</a></h4>
+</details>
+
+
+<details open>
+  <summary>VueGen</summary>  
+  <div style="text-align: center; margin-bottom: 20px;">
+    <a href="https://github.com/Multiomics-Analytics-Group/vuegen" target="_blank">
+      <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/vuegen/main/docs/images/vuegen_logo.svg" alt="VueGen" width="250px">
+    </a>
+  </div>
+  <p><b>VueGen</b> is a tool that automates the creation of <b>reports</b> from bioinformatics outputs, allowing researchers with minimal coding experience to communicate their results effectively. With VueGen, users can produce reports by simply specifying a directory containing output files, such as plots, tables, networks, Markdown text, HTML components, and API calls, along with the report format. Supported formats include <b>documents</b> (PDF, HTML, DOCX, ODT), <b>presentations</b> (PPTX, Reveal.js), <b>Jupyter notebooks</b> and <a href="https://streamlit.io/"><b>Streamlit</b></a> <b>web applications</b>.</p>
+  <p>An overview of the VueGen workflow is shown in the figure below:</p>
+  <a href="#zoom-vuegen-workflow">
+    <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/vuegen/main/docs/images/vuegen_graph_abstract.png"
+        alt="VueGen Workflow"
+        style="cursor: zoom-in; max-width: 100%;">
+  </a>
+  <!-- Zoom overlay -->
+  <div id="zoom-vuegen-workflow" class="zoom-overlay">
+    <a href="#vuegen-workflow" style="text-decoration: none;">
+      <img src="https://raw.githubusercontent.com/Multiomics-Analytics-Group/vuegen/main/docs/images/vuegen_graph_abstract.png"
+          alt="Zoomed_VueGen Workflow"
+          style="max-width: 90%; max-height: 90%; cursor: zoom-out;
+                  transform: translate(5%, -5%);">
+    </a>
+  </div>
+  <p>VueGen offers various implementation options for both non-technical and experienced users. It is available as a <a href="https://pypi.org/project/vuegen/">Python package</a>, <a href="https://quay.io/repository/dtu_biosustain_dsp/vuegen">Docker image</a>, <a href="https://github.com/Multiomics-Analytics-Group/nf-vuegen/">nf-core module</a>, and <a href="https://github.com/Multiomics-Analytics-Group/vuegen/releases/tag/v0.3.2">cross-platform desktop application</a> with a user-friendly interface, making it accessible and customizable for different user needs and expertise levels.</p>
+  <p>The documentation is available at <a href="https://vuegen.readthedocs.io/">vuegen.readthedocs.io</a>, where you can find detailed information of the package’s classes and functions, installation and execution instructions, and case studies to demonstrate its functionality.</p>
+</details>
+
+
+<details open>
+  <summary>FermentDB</summary>
+  <h3>A Standard Data Model for Precision Fermentation</h3>
+  <div style="text-align: justify; margin: 2em 0;">
+  <b>FermentDB</b> is a platform for bioprocess data <i>Integration</i>, <i>Analysis</i> and <i>Visualization</i>.<br><br>
+    It is designed to address key challenges in precision fermentation by establishing a community standard for biofoundries. It streamlines the integration of high-throughput bioprocess data, supporting the development and scaling of biosustainable production processes.<br><br>
+    As an open-source database, FermentDB addresses the main obstacles related to FAIR data principles. By providing a standardized data model, it offers the scientific community a powerful computational tool for bioprocess data integration.<br><br>
+    It also supports the upscaling of research through descriptive analytics for fermentation monitoring, enabling more efficient scaling of biomanufacturing and reducing time to market for new bioproducts.<br><br> 
+    By enabling seamless integration of new datasets from collaborators, including omics data and in silico testing and optimization, FermentDB reduces time and resource demands and accelerates innovation in biomanufacturing.
+  </div>
+  <p>A graphical image of FermentDB workflow is shown in the figure below:</p>
+  <a href="#zoom-fermentdb-workflow">
+    <img src="{{ site.baseurl }}/public/assets/graphical_abst_fermentDB.png"
+        alt="Graphical_Abstract"
+        style="cursor: zoom-in; max-width: 100%;">
+  </a>
+  <!-- Zoom overlay -->
+  <div id="zoom-fermentdb-workflow" class="zoom-overlay">
+    <a href="#fermentdb-workflow" style="text-decoration: none;">
+      <img src="{{ site.baseurl }}/public/assets/graphical_abst_fermentDB.png"
+          alt="Zoomed_Image"
+          style="max-width: 90%; max-height: 90%; cursor: zoom-out;
+                  transform: translate(5%, -5%);">
+    </a>
+  </div>
+  <p>FermentDB is freely available on the website<a href="fermentdb.streamlit.app">fermentdb.streamlit.app</a></p>
+  <p style="text-align: justify;">The application code is publicly  available in GitGub: <a href="https://github.com/Multiomics-Analytics-Group/FermentDB"> FermentDB Graph </a> and <a href="https://github.com/Multiomics-Analytics-Group/fermentdb_api"> FermentDB API</a>.</p>
+</details>
+
+
+<details open>
+  <summary>gdbcore</summary>
+  <h3>A Graph Database Builder python library</h3>
+  <p><b>gdbcore</b> is a python library that can take your tables and transform them into graph databases in Networkx, graph-tool or Neo4j (v4). gdbcore is available for installation from <a href="https://pypi.org/project/gdbcore/">PyPI</a>.</p>
+  <p>We are currently improving the project and the documentation. <b>Stay tuned!</b></p>
+</details>
+
+
+<!-- <details open>
+  <summary>Additional Tool (Template)</summary>  
+
+  <img src="url or path to img" width="10%" alt="example icon">
+
+  <p>Paragraph of text inside</p>
+
+  <h2>This is a subheader in the collapsable section.</h2>
+
+  <p>More text here if you want</p>
+
+</details> -->