📄Deep learning shapes single-cell data analysis
💥Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep learning methods for analysing single-cell data.
📘Journal: Nature Reviews Molecular Cell Biology (I.F.=94.444)
🗓Publish year: 23 February 2022
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📲Channel: @Bioinformatics
💥Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep learning methods for analysing single-cell data.
📘Journal: Nature Reviews Molecular Cell Biology (I.F.=94.444)
🗓Publish year: 23 February 2022
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📲Channel: @Bioinformatics
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📃Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis
📘Journal: Procedia Computer Science
🗓Publish year: 2021
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📲Channel: @Bioinformatics
📘Journal: Procedia Computer Science
🗓Publish year: 2021
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📲Channel: @Bioinformatics
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Bioinformatics Applications in Tackling COVID-19 Pandemic.gif
34.1 MB
👨🏻💻2 weeks Bioinformatics Virtual Workshop by Dollar Education
💥 Bioinformatics Applications in Tackling COVID-19 Pandemic💥
🗓 Date: 14 March - 24 March, 2022
⏰ Time: 6:00 pm IST
✍️ Registration Link
https://www.dollareducation.org
💲 Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
💥Topics:
▫️Bioinformatics Resources
▫️Quick designs of vaccines
▫️Determining pre-existing immunity
▫️SARS-CoV2 Genomics
▫️Detecting lineages and variants of concerns
▫️Genomic evaluation
▫️Drug-repurposing
ℹ️Contact and More information:
https://news.1rj.ru/str/+qCL0SBRZSLZmMDE1
📲Channel: @Bioinformatics
💥 Bioinformatics Applications in Tackling COVID-19 Pandemic💥
🗓 Date: 14 March - 24 March, 2022
⏰ Time: 6:00 pm IST
✍️ Registration Link
https://www.dollareducation.org
💲 Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
💥Topics:
▫️Bioinformatics Resources
▫️Quick designs of vaccines
▫️Determining pre-existing immunity
▫️SARS-CoV2 Genomics
▫️Detecting lineages and variants of concerns
▫️Genomic evaluation
▫️Drug-repurposing
ℹ️Contact and More information:
https://news.1rj.ru/str/+qCL0SBRZSLZmMDE1
📲Channel: @Bioinformatics
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🎓 PhD Thesis, Rice University
💥The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes 💥
Abstract: Recent years have witnessed a surge in the application of graph theory to complex biological systems. The ability of graph theory to extract essential knowledge from the plethora of information embedded in a complex system has proven rewarding in many disciplines ranging from evolutionary biology to cancer prediction. The modular structure of complex networks, a branch of graph theory, is the focus of this text. Its guiding hypothesis, derived from statistical physics, states that modularity correlates with performances of complex biological systems and that the direction of correlation is mediated by environmental stress. This text tests and expands the theory of modularity in three main contexts - gene co-expression networks, human brain networks, and genome-scale metabolic networks. It is demonstrated that modularity of cancer-associated gene co-expression network is predictive of cancer aggressiveness, that modularity of resting-state functional connectivity in healthy young adults correlates with cognitive performance and the correlation is mediated by task complexity, and that modularity of human brain metabolic network not only predicts risk for Alzheimer’s disease but also defines the brain regions where metabolism correlates with dementia-risk gene expression. In addition, definition of modularity and maximization algorithm for bipartite, directed, and weighted networks are proposed and subsequently tested on a genome-scale bacterial metabolic network under different levels of survival stress. Overall, results presented here support the hypothesis of modularity’s role as a performance predictor for complex systems. The existing theory of modularity has been validated in numerous scenarios and expanded with the concept of ”network fragmentation”. Modularity can be applied to clinical settings for risk evaluation, and even contribute to individualized therapy. It can also help understand the mechanism of biological processes that are currently poorly understood. Of course, future research is needed to further the understanding of the emergence of modularity in complex systems and its application. Better definition of modularity, faster and more functionally appropriate clustering algorithm, and the collection of larger amount of higher quality data are crucial for the advancement of the field.
🌐 Study the full document
📲Channel: @Bioinformatics
💥The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes 💥
Abstract: Recent years have witnessed a surge in the application of graph theory to complex biological systems. The ability of graph theory to extract essential knowledge from the plethora of information embedded in a complex system has proven rewarding in many disciplines ranging from evolutionary biology to cancer prediction. The modular structure of complex networks, a branch of graph theory, is the focus of this text. Its guiding hypothesis, derived from statistical physics, states that modularity correlates with performances of complex biological systems and that the direction of correlation is mediated by environmental stress. This text tests and expands the theory of modularity in three main contexts - gene co-expression networks, human brain networks, and genome-scale metabolic networks. It is demonstrated that modularity of cancer-associated gene co-expression network is predictive of cancer aggressiveness, that modularity of resting-state functional connectivity in healthy young adults correlates with cognitive performance and the correlation is mediated by task complexity, and that modularity of human brain metabolic network not only predicts risk for Alzheimer’s disease but also defines the brain regions where metabolism correlates with dementia-risk gene expression. In addition, definition of modularity and maximization algorithm for bipartite, directed, and weighted networks are proposed and subsequently tested on a genome-scale bacterial metabolic network under different levels of survival stress. Overall, results presented here support the hypothesis of modularity’s role as a performance predictor for complex systems. The existing theory of modularity has been validated in numerous scenarios and expanded with the concept of ”network fragmentation”. Modularity can be applied to clinical settings for risk evaluation, and even contribute to individualized therapy. It can also help understand the mechanism of biological processes that are currently poorly understood. Of course, future research is needed to further the understanding of the emergence of modularity in complex systems and its application. Better definition of modularity, faster and more functionally appropriate clustering algorithm, and the collection of larger amount of higher quality data are crucial for the advancement of the field.
🌐 Study the full document
📲Channel: @Bioinformatics
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📑Computational methods for cancer driver discovery: A survey
📘Journal: Theranostics (I.F.=11.556)
🗓Publish year: 2021
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📲Channel: @Bioinformatics
📑Horizon Scanning: Teaching Genomics and Personalized Medicine in the Digital Age
💥From abstract: This expert review offers an analysis of the bottlenecks that affect and issues that need to be addressed to catalyze genomics and personalized medicine education in the digital era. In addition, we summarize and critically discuss the various educational and awareness opportunities that presently exist to catalyze the delivery of genomics knowledge in ways closely attuned to the emerging field of digital health.
📘Journal: OMICS: A Journal of Integrative Biology (I.F.=3.374)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
💥From abstract: This expert review offers an analysis of the bottlenecks that affect and issues that need to be addressed to catalyze genomics and personalized medicine education in the digital era. In addition, we summarize and critically discuss the various educational and awareness opportunities that presently exist to catalyze the delivery of genomics knowledge in ways closely attuned to the emerging field of digital health.
📘Journal: OMICS: A Journal of Integrative Biology (I.F.=3.374)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
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📑DNA Computing: Principle, Construction, and Applications in Intelligent Diagnostics
📘Journal: Small Structures Journal
🗓Publish year: 2021
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📲Channel: @Bioinformatics
📘Journal: Small Structures Journal
🗓Publish year: 2021
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📲Channel: @Bioinformatics
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🎓 PhD Thesis, Faculty of Pharmacy, Uppsala University
💥Approaches for Distributing Large Scale Bioinformatic Analyses💥
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📲Channel: @Bioinformatics
💥Approaches for Distributing Large Scale Bioinformatic Analyses💥
📎 Study full thesis
📲Channel: @Bioinformatics
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👨🏻💻Free Online hands-on Workshop
💥Genomics Data Carpentry Workshop💥
🗓 Date: March 23-25, 2022
⌚️ Time: 9:00 am - 1:00 pm EST
▫️You don't need to have any previous knowledge of the tools that will be presented at the workshop
ℹ️ More information
✍🏻 Registration
📲Channel: @Bioinformatics
💥Genomics Data Carpentry Workshop💥
🗓 Date: March 23-25, 2022
⌚️ Time: 9:00 am - 1:00 pm EST
▫️You don't need to have any previous knowledge of the tools that will be presented at the workshop
ℹ️ More information
✍🏻 Registration
📲Channel: @Bioinformatics
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🎬 Free webinar
💥Multi-Omics Integration: Problems, Potential and Promise💥
🗓 Date: Mar 21, 2022
🕖 Time: 01:00 PM in Eastern Time (US and Canada)
📍 Location: Online (ZOOM)
✍🏻 Registration & More information
📲Channel: @Bioinformatics
💥Multi-Omics Integration: Problems, Potential and Promise💥
🗓 Date: Mar 21, 2022
🕖 Time: 01:00 PM in Eastern Time (US and Canada)
📍 Location: Online (ZOOM)
✍🏻 Registration & More information
📲Channel: @Bioinformatics
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📑Applications of Explainable Artificial Intelligence (XAI) in Diagnosis and Surgery
📘Journal: Diagnostics (I.F.=3.706)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
📘Journal: Diagnostics (I.F.=3.706)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
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📑Overview of current state of research on the application of artificial intelligence techniques for COVID-19
📘Journal: PeerJ Computer Science (I.F.=1.39)
🗓Publish year: May, 2021
💥In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.
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📲Channel: @Bioinformatics
📘Journal: PeerJ Computer Science (I.F.=1.39)
🗓Publish year: May, 2021
💥In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.
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📲Channel: @Bioinformatics
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📑A Review of Cell-Based Computational Modeling in Cancer Biology
📘Journal: JCO Clinical Cancer Informatics
🗓Publish year: 2019
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📲Channel: @Bioinformatics
📘Journal: JCO Clinical Cancer Informatics
🗓Publish year: 2019
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📲Channel: @Bioinformatics
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📄Machine learning methods for prediction of cancer driver genes: a survey paper
💥From Abstract: This survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
🗓Publish year: 2021
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📲Channel: @Bioinformatics
💥From Abstract: This survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
🗓Publish year: 2021
📎 Study the paper
📲Channel: @Bioinformatics
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