📃Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods
📔Journal: Computational and Structural Biotechnology Journal (I.F.= 6)
🗓 Publish year: 2023
🧑💻Authors: Juliana Costa-Silva, Douglas S. Domingues, David Menotti, ...
🏢University: Federal University of Paraná, University of São Paulo, Universidade Tecnológica Federal do Paraná – UTFPR, Brzil
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📲Channel: @Bioinformatics
#review #rna_seq #gene_expression
📔Journal: Computational and Structural Biotechnology Journal (I.F.= 6)
🗓 Publish year: 2023
🧑💻Authors: Juliana Costa-Silva, Douglas S. Domingues, David Menotti, ...
🏢University: Federal University of Paraná, University of São Paulo, Universidade Tecnológica Federal do Paraná – UTFPR, Brzil
📎 Study the paper
📲Channel: @Bioinformatics
#review #rna_seq #gene_expression
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🎥 Analysis and Visualization of Protein-Ligand Interactions
🎞 Watch
📲Channel: @Bioinformatics
#video #protein #ligand
🎞 Watch
📲Channel: @Bioinformatics
#video #protein #ligand
YouTube
Analysis and Visualization of Protein-Ligand Interactions with PYMOL and PLIP
Welcome to Bioinformatics Insights. In this video, we will learn, How to analyze all types of protein-ligand interactions. I will also train you, How to visualize protein-ligand interactions using PYMOL. After watching this video, you will be able to analyze…
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📄 Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
📘Journal: Genomics, Proteomics and Bioinformatics (I.F.= 9.5)
🗓 Publish year: 2022
🧑💻Authors: Matthew Brendel, Chang Su, Zilong Bai, ...
🏢University: Cornell University - Temple University, USA
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📲Channel: @Bioinformatics
#review #deep_learning #single_cell #rna
📘Journal: Genomics, Proteomics and Bioinformatics (I.F.= 9.5)
🗓 Publish year: 2022
🧑💻Authors: Matthew Brendel, Chang Su, Zilong Bai, ...
🏢University: Cornell University - Temple University, USA
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📲Channel: @Bioinformatics
#review #deep_learning #single_cell #rna
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📃 Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
🗓 Publish year: 2024
🧑💻Authors: Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, ...
🏢University: Renmin University of China, University of Science and Technology of China, Microsoft Research
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📦 Related sources and contents
📲Channel: @Bioinformatics
#review #nlp #biomolecule #protein
🗓 Publish year: 2024
🧑💻Authors: Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, ...
🏢University: Renmin University of China, University of Science and Technology of China, Microsoft Research
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📦 Related sources and contents
📲Channel: @Bioinformatics
#review #nlp #biomolecule #protein
👍4❤2👌1
📑 Ten simple rules for designing graphical abstracts
📕Journal: Plos Computational Biology (I.F.=4.3)
🗓Publish year: 2024
🧑💻Authors: Helena Klara Jambor ,Martin Bornhäuser
🏢University: Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Germany
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📲Channel: @Bioinformatics
#graphical_abstract
📕Journal: Plos Computational Biology (I.F.=4.3)
🗓Publish year: 2024
🧑💻Authors: Helena Klara Jambor ,Martin Bornhäuser
🏢University: Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Germany
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📲Channel: @Bioinformatics
#graphical_abstract
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📑 Explainable artificial intelligence for omics data: a systematic mapping study
📗Journal: Briefings in Bioinformatics (I.F.=9.5)
🗓Publish year: 2024
🧑💻Authors: Philipp A Toussaint, Florian Leiser, Scott Thiebes, ...
🏢University: Department of Economics and Management - University of Augsburg , Germany
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📲Channel: @Bioinformatics
#review #explainable #ai #omics
📗Journal: Briefings in Bioinformatics (I.F.=9.5)
🗓Publish year: 2024
🧑💻Authors: Philipp A Toussaint, Florian Leiser, Scott Thiebes, ...
🏢University: Department of Economics and Management - University of Augsburg , Germany
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📲Channel: @Bioinformatics
#review #explainable #ai #omics
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📃 Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review
📗Journal: Mathematics (I.F.=2.4)
🗓Publish year: 2023
🧑💻Authors: Minhyeok Lee
🏢University: Chung-Ang University, Republic of Korea
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📲Channel: @Bioinformatics
#review #GAN #gene_expression
📗Journal: Mathematics (I.F.=2.4)
🗓Publish year: 2023
🧑💻Authors: Minhyeok Lee
🏢University: Chung-Ang University, Republic of Korea
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📲Channel: @Bioinformatics
#review #GAN #gene_expression
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Forwarded from Network Analysis Resources & Updates
🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology
💥Free recorded course by Prof. Marinka Zitnik
💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
💥Free recorded course by Prof. Marinka Zitnik
💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 18 - GNNs in Computational Biology
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XVImFC
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
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