📃The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI
📕 Journal: New Biotechnology (I.F.=4.5)
🗓 Publish year: 2023
🧑💻Authors: Mariangela Santorsola, Francesco Lescai
🏢Universities: University of Pavia, Italy
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📲Channel: @Bioinformatics
#review #explainability #deep_learning #omics
📕 Journal: New Biotechnology (I.F.=4.5)
🗓 Publish year: 2023
🧑💻Authors: Mariangela Santorsola, Francesco Lescai
🏢Universities: University of Pavia, Italy
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📲Channel: @Bioinformatics
#review #explainability #deep_learning #omics
👍4👏1
📃 Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
📕 Journal: Computational and Structural Biotechnology Journal (I.F.=4.5)
🗓 Publish year: 2025
🧑💻Authors: Aishwarya Budhkar, Qianqian Song, Jing Su, ...
🏢Universities: Indiana University Bloomington - University of Florida - Indiana University, USA
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📲Channel: @Bioinformatics
#review #explainability #ai
📕 Journal: Computational and Structural Biotechnology Journal (I.F.=4.5)
🗓 Publish year: 2025
🧑💻Authors: Aishwarya Budhkar, Qianqian Song, Jing Su, ...
🏢Universities: Indiana University Bloomington - University of Florida - Indiana University, USA
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📲Channel: @Bioinformatics
#review #explainability #ai
🎥 Explainable Machine Learning in Early Cancer Diagnosis
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📲Channel: @Bioinformatics
#video #machine_learning #explainability #cancer
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📲Channel: @Bioinformatics
#video #machine_learning #explainability #cancer
YouTube
Forerunners of Cancer Hallmarks: Explainable Machine Learning in Early Cancer Diagnosis
Judith Cerit, Technical University of Munich
August 19, 2024
The Mathematics of the Hallmarks of Cancer (http://www.fields.utoronto.ca/activities/24-25/oncology-hallmarks)
August 19, 2024
The Mathematics of the Hallmarks of Cancer (http://www.fields.utoronto.ca/activities/24-25/oncology-hallmarks)
❤3👍1
Forwarded from Network Analysis Resources & Updates
📄 Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
🗓 Publish year: 2025
🧑💻Authors: Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, ...
🏢Universities: Clemson University, USA - University of New England & UNSW Sydney, Australia - Chinese Academy of Sciences, China.
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⚡️Channel: @ComplexNetworkAnalysis
#review #multimodal #biomedical #interpretable #graph_machine_learning #explainability
🗓 Publish year: 2025
🧑💻Authors: Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, ...
🏢Universities: Clemson University, USA - University of New England & UNSW Sydney, Australia - Chinese Academy of Sciences, China.
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⚡️Channel: @ComplexNetworkAnalysis
#review #multimodal #biomedical #interpretable #graph_machine_learning #explainability
❤5🙏1