Human_DNARNA_motif_mining_using_deep_learning_methods_a_scoping.pdf
2.3 MB
📃 Human DNA/RNA motif mining using deep-learning methods: a scoping review
📘 Journal: Network Modeling Analysis in Health Informatics and Bioinformatics (I.F=1.077)
🗓 Publish year: 2023
🧑💻Authors: Rajashree Chaurasia & Udayan Ghose
🏢Universities: Guru Gobind Singh Indraprastha University
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📲Channel: @ComplexNetworkAnalysis
#paper #motif #education #review #DNA #RNA #deep_learning
📘 Journal: Network Modeling Analysis in Health Informatics and Bioinformatics (I.F=1.077)
🗓 Publish year: 2023
🧑💻Authors: Rajashree Chaurasia & Udayan Ghose
🏢Universities: Guru Gobind Singh Indraprastha University
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📲Channel: @ComplexNetworkAnalysis
#paper #motif #education #review #DNA #RNA #deep_learning
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📄Graph Neural Networks
💥In this video, you will learn the application of neural networks on graphs.
💥Graph Neural Networks (GNNs) have recently gained increasing popularity in both applications and research, including domains such as social networks, knowledge graphs, recommender systems, and bioinformatics. While the theory and math behind GNNs might first seem complicated, the implementation of those models is quite simple and helps in understanding the methodology. Therefore, this webinar will discuss the implementation of basic network layers of a GNN, namely graph convolutions, and attention layers. Finally, we will apply a GNN on a node-level, edge-level, and graph-level tasks.
🎞Watch: part1 part2
👨💻Code
📲Channel: @ComplexNetworkAnalysis
#Video #Graph #code #python #Colab #GNN
💥In this video, you will learn the application of neural networks on graphs.
💥Graph Neural Networks (GNNs) have recently gained increasing popularity in both applications and research, including domains such as social networks, knowledge graphs, recommender systems, and bioinformatics. While the theory and math behind GNNs might first seem complicated, the implementation of those models is quite simple and helps in understanding the methodology. Therefore, this webinar will discuss the implementation of basic network layers of a GNN, namely graph convolutions, and attention layers. Finally, we will apply a GNN on a node-level, edge-level, and graph-level tasks.
🎞Watch: part1 part2
👨💻Code
📲Channel: @ComplexNetworkAnalysis
#Video #Graph #code #python #Colab #GNN
YouTube
Tutorial 7: Graph Neural Networks (Part 1)
In this tutorial, we will discuss the application of neural networks on graphs. Graph Neural Networks (GNNs) have recently gained increasing popularity in both applications and research, including domains such as social networks, knowledge graphs, recommender…
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📃 Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
🗓 Publish year: 2023
🧑💻Authors: Fang Li, Yi Nian, Zenan Sun, Cui Tao
🏢Universities: the University of Texas Health Science Center at Houston
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📲Channel: @ComplexNetworkAnalysis
#paper #Biomedicine #GRL
🗓 Publish year: 2023
🧑💻Authors: Fang Li, Yi Nian, Zenan Sun, Cui Tao
🏢Universities: the University of Texas Health Science Center at Houston
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📲Channel: @ComplexNetworkAnalysis
#paper #Biomedicine #GRL
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📃 The importance of graph databases and graph learning for clinical applications
📗 Journal: The Journal of Biological Databases & Curation (I.F=4.6)
🗓 Publish year: 2023
🧑💻Authors: Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth, David Broneske, Gunter Saake, Robert Heyer
🏢Universities: Otto von Guericke University
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📲Channel: @ComplexNetworkAnalysis
#paper #clinical_applications #graph_learning
📗 Journal: The Journal of Biological Databases & Curation (I.F=4.6)
🗓 Publish year: 2023
🧑💻Authors: Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth, David Broneske, Gunter Saake, Robert Heyer
🏢Universities: Otto von Guericke University
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📲Channel: @ComplexNetworkAnalysis
#paper #clinical_applications #graph_learning
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📄Network graph
💥Technical Paper
💥 A network graph is a chart that displays relations between elements (nodes) using simple links. Network graph allows us to visualize clusters and relationships between the nodes quickly; the chart is often used in industries such as life science, cybersecurity, intelligence, etc.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Visualisation
💥Technical Paper
💥 A network graph is a chart that displays relations between elements (nodes) using simple links. Network graph allows us to visualize clusters and relationships between the nodes quickly; the chart is often used in industries such as life science, cybersecurity, intelligence, etc.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Visualisation
Highcharts Blog | Highcharts
Network graph – Highcharts Blog | Highcharts
Learn how to create an interactive network graph using Highcharts.
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📃 Graph-Theoretical Analysis of Biological Networks: A Survey
📘 Journal: Computation (I.F=2.2)
🗓 Publish year: 2023
🧑💻Author: Kayhan Erciyes
🏢University: Marmara University
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Biological #Survey
📘 Journal: Computation (I.F=2.2)
🗓 Publish year: 2023
🧑💻Author: Kayhan Erciyes
🏢University: Marmara University
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Biological #Survey
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📃 Understanding Graph Embedding Methods and Their Applications
📗 Journal: Society for Industrial and Applied Mathematic (I.F=1.698)
🗓 Publish year: 2021
🧑💻Authors: Mengjia Xu
🏢Universities: Massachusetts Institute of Technology
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📲Channel: @ComplexNetworkAnalysis
#paper #Applications #graph_Embedding
📗 Journal: Society for Industrial and Applied Mathematic (I.F=1.698)
🗓 Publish year: 2021
🧑💻Authors: Mengjia Xu
🏢Universities: Massachusetts Institute of Technology
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📲Channel: @ComplexNetworkAnalysis
#paper #Applications #graph_Embedding
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📃 Network analytics: an introduction and illustrative applications in health data science
📘 Journal: Journal of Information Technology Case and Application Research
🗓 Publish year: 2023
🧑💻Authors: Pankush Kalgotra, Ramesh Sharda
🏢Universities: Auburn University, Oklahoma State University
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📱Channel: @ComplexNetworkAnalysis
#paper #applications #health #data_science
📘 Journal: Journal of Information Technology Case and Application Research
🗓 Publish year: 2023
🧑💻Authors: Pankush Kalgotra, Ramesh Sharda
🏢Universities: Auburn University, Oklahoma State University
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📱Channel: @ComplexNetworkAnalysis
#paper #applications #health #data_science
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📃 A survey on bipartite graphs embedding
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Edward Giamphy, Jean‑Loup Guillaume, Antoine Doucet, Kevin Sanchis
🏢Universities: La Rochelle University
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📲Channel: @ComplexNetworkAnalysis
#paper #bipartite #graph_Embedding #survey
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Edward Giamphy, Jean‑Loup Guillaume, Antoine Doucet, Kevin Sanchis
🏢Universities: La Rochelle University
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📲Channel: @ComplexNetworkAnalysis
#paper #bipartite #graph_Embedding #survey
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📃 A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
📘Conference: International Conference on Neural Information Processing
🗓 Publish year: 2021
🧑💻Authors: Yankai Chen, Yaozu Wu, Shicheng Ma, Irwin King
🏢University: The Chinese University of Hong Kong
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Biomedical #review
📘Conference: International Conference on Neural Information Processing
🗓 Publish year: 2021
🧑💻Authors: Yankai Chen, Yaozu Wu, Shicheng Ma, Irwin King
🏢University: The Chinese University of Hong Kong
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Biomedical #review
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📃 A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Mudasir Malla, Adil ; Banka, Asif Ali
🏢Universities: Islamic University of Science & Technology
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📲Channel: @ComplexNetworkAnalysis
#paper #Bioinformatics #Deep_GNN #Review
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Mudasir Malla, Adil ; Banka, Asif Ali
🏢Universities: Islamic University of Science & Technology
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📲Channel: @ComplexNetworkAnalysis
#paper #Bioinformatics #Deep_GNN #Review
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📃 Graph neural networks for clinical risk prediction based on electronic health records: A survey
📘 Journal: Journal of Biomedical Informatics (I.F=4.5)
🗓 Publish year: 2024
🧑💻Authors: Heloísa Oss Boll, Ali Amirahmadi, Mirfarid Musavian Ghazani, Wagner Ourique de Morais, Edison Pignaton de Freitas, Amira Soliman, Farzaneh Etminani, Stefan Byttner, Mariana Recamonde-Mendoza
🏢Universities: Universidade Federal do Rio Grande do Sul, Halmstad University
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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #risk #prediction #electronic #health #survey
📘 Journal: Journal of Biomedical Informatics (I.F=4.5)
🗓 Publish year: 2024
🧑💻Authors: Heloísa Oss Boll, Ali Amirahmadi, Mirfarid Musavian Ghazani, Wagner Ourique de Morais, Edison Pignaton de Freitas, Amira Soliman, Farzaneh Etminani, Stefan Byttner, Mariana Recamonde-Mendoza
🏢Universities: Universidade Federal do Rio Grande do Sul, Halmstad University
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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #risk #prediction #electronic #health #survey
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🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
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📃 A Bibliometric Analysis of Recent Developments and Trends in Knowledge Graph Research (2013–2022)
📗 Journal: IEEE ACCESS (I.F=3.9)
🗓 Publish year: 2024
🧑💻Authors: GANG WANG, JING HE
🏢Universities: Chaohu University
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📲Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Knowledge_Graph
📗 Journal: IEEE ACCESS (I.F=3.9)
🗓 Publish year: 2024
🧑💻Authors: GANG WANG, JING HE
🏢Universities: Chaohu University
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📲Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Knowledge_Graph
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🎞 Machine Learning with Graphs - Node Embeddings
💥SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
💥SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
YouTube
Machine Learning with Graphs - Node Embeddings
SDML is partnering with Houston Machine Learning on a series about machine learning with graphs. The content will be mainly based on the Stanford course: https://web.stanford.edu/class/cs224w/
Series schedule:
Introduction; Machine Learning for Graphs
Traditional…
Series schedule:
Introduction; Machine Learning for Graphs
Traditional…
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📃 Artificial Intelligence for Complex Network: Potential, Methodology and Application
🗓 Publish year: 2024
🧑💻Authors: Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
🏢University: Tsinghua University
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📱Channel: @ComplexNetworkAnalysis
#paper #Artificial_Intelligence #Potential #Methodology #Application
🗓 Publish year: 2024
🧑💻Authors: Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
🏢University: Tsinghua University
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📱Channel: @ComplexNetworkAnalysis
#paper #Artificial_Intelligence #Potential #Methodology #Application
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📄Stanford Network Analysis Platform (SNAP)
💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.
💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.
💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.
💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Python #Tutorials #Dataset
💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.
💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.
💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.
💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Python #Tutorials #Dataset
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