π£ Q&A - 6th Bioinformatics Conference π£
πͺπΈ Training and professional perspectives in Bioinformatics in Spain
π Date: 25 February 2022
π Time: From 19:00h to 20:30h, CET
βΉοΈ Registration + information:
π https://buff.ly/3IcaBlt
π²Channel: @Bioinformatics
πͺπΈ Training and professional perspectives in Bioinformatics in Spain
π Date: 25 February 2022
π Time: From 19:00h to 20:30h, CET
βΉοΈ Registration + information:
π https://buff.ly/3IcaBlt
π²Channel: @Bioinformatics
π3
πDeep learning shapes single-cell data analysis
π₯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.
πJournal: Nature Reviews Molecular Cell Biology (I.F.=94.444)
πPublish year: 23 February 2022
π Study the paper
π²Channel: @Bioinformatics
π₯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.
πJournal: Nature Reviews Molecular Cell Biology (I.F.=94.444)
πPublish year: 23 February 2022
π Study the paper
π²Channel: @Bioinformatics
π3
πMachine Learning Algorithms For Breast Cancer Prediction And Diagnosis
πJournal: Procedia Computer Science
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
πJournal: Procedia Computer Science
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
π4
Bioinformatics Applications in Tackling COVID-19 Pandemic.gif
34.1 MB
π¨π»βπ»2 weeks Bioinformatics Virtual Workshop by Dollar Education
π₯ Bioinformatics Applications in Tackling COVID-19 Pandemicπ₯
π Date: 14 March - 24 March, 2022
β° Time: 6:00 pm IST
βοΈ Registration Link
https://www.dollareducation.org
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Topics:
β«οΈBioinformatics Resources
β«οΈQuick designs of vaccines
β«οΈDetermining pre-existing immunity
β«οΈSARS-CoV2 Genomics
β«οΈDetecting lineages and variants of concerns
β«οΈGenomic evaluation
β«οΈDrug-repurposing
βΉοΈContact and More information:
https://t.iss.one/+qCL0SBRZSLZmMDE1
π²Channel: @Bioinformatics
π₯ Bioinformatics Applications in Tackling COVID-19 Pandemicπ₯
π Date: 14 March - 24 March, 2022
β° Time: 6:00 pm IST
βοΈ Registration Link
https://www.dollareducation.org
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Topics:
β«οΈBioinformatics Resources
β«οΈQuick designs of vaccines
β«οΈDetermining pre-existing immunity
β«οΈSARS-CoV2 Genomics
β«οΈDetecting lineages and variants of concerns
β«οΈGenomic evaluation
β«οΈDrug-repurposing
βΉοΈContact and More information:
https://t.iss.one/+qCL0SBRZSLZmMDE1
π²Channel: @Bioinformatics
π6β€2
π PhD Thesis, Rice University
π₯The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes π₯
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 Alzheimerβs 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 modularityβs 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 βnetwork fragmentationβ. 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.
π Study the full document
π²Channel: @Bioinformatics
π₯The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes π₯
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 Alzheimerβs 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 modularityβs 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 βnetwork fragmentationβ. 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.
π Study the full document
π²Channel: @Bioinformatics
π5π₯1
πComputational methods for cancer driver discovery: A survey
πJournal: Theranostics (I.F.=11.556)
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
π¬How to develop models for cancer using Convolution Neural Networks
π Watch
π²Channel: @Bioinformatics
π Watch
π²Channel: @Bioinformatics
YouTube
Cancer Detection Using Deep Learning | Deep Learning Projects | Deep Learning Training | Edureka
π₯Edureka Deep Learning With TensorFlow (ππ¬π ππ¨ππ: πππππππππ): https://www.edureka.co/ai-deep-learning-with-tensorflow
This Edureka video on πππ§πππ« πππππππ’π¨π§ ππ¬π’π§π ππππ© ππππ«π§π’π§π , will help you understand how to develop models using Convolution Neural Networks.β¦
This Edureka video on πππ§πππ« πππππππ’π¨π§ ππ¬π’π§π ππππ© ππππ«π§π’π§π , will help you understand how to develop models using Convolution Neural Networks.β¦
π5
πHorizon Scanning: Teaching Genomics and Personalized Medicine in the Digital Age
π₯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.
πJournal: OMICS: A Journal of Integrative Biology (I.F.=3.374)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
π₯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.
πJournal: OMICS: A Journal of Integrative Biology (I.F.=3.374)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
π2
πDNA Computing: Principle, Construction, and Applications in Intelligent Diagnostics
πJournal: Small Structures Journal
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
πJournal: Small Structures Journal
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
π3
π PhD Thesis, Faculty of Pharmacy, Uppsala University
π₯Approaches for Distributing Large Scale Bioinformatic Analysesπ₯
π Study full thesis
π²Channel: @Bioinformatics
π₯Approaches for Distributing Large Scale Bioinformatic Analysesπ₯
π Study full thesis
π²Channel: @Bioinformatics
π4β€1
π¨π»βπ»Free Online hands-on Workshop
π₯Genomics Data Carpentry Workshopπ₯
π Date: March 23-25, 2022
βοΈ Time: 9:00 am - 1:00 pm EST
β«οΈYou don't need to have any previous knowledge of the tools that will be presented at the workshop
βΉοΈ More information
βπ» Registration
π²Channel: @Bioinformatics
π₯Genomics Data Carpentry Workshopπ₯
π Date: March 23-25, 2022
βοΈ Time: 9:00 am - 1:00 pm EST
β«οΈYou don't need to have any previous knowledge of the tools that will be presented at the workshop
βΉοΈ More information
βπ» Registration
π²Channel: @Bioinformatics
β€5π3
π¬ Free webinar
π₯Multi-Omics Integration: Problems, Potential and Promiseπ₯
π Date: Mar 21, 2022
π Time: 01:00 PM in Eastern Time (US and Canada)
π Location: Online (ZOOM)
βπ» Registration & More information
π²Channel: @Bioinformatics
π₯Multi-Omics Integration: Problems, Potential and Promiseπ₯
π Date: Mar 21, 2022
π Time: 01:00 PM in Eastern Time (US and Canada)
π Location: Online (ZOOM)
βπ» Registration & More information
π²Channel: @Bioinformatics
π4
πApplications of Explainable Artificial Intelligence (XAI) in Diagnosis and Surgery
πJournal: Diagnostics (I.F.=3.706)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
πJournal: Diagnostics (I.F.=3.706)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
π2
πOverview of current state of research on the application of artificial intelligence techniques for COVID-19
πJournal: PeerJ Computer Science (I.F.=1.39)
πPublish year: May, 2021
π₯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.
π Study the paper
π²Channel: @Bioinformatics
πJournal: PeerJ Computer Science (I.F.=1.39)
πPublish year: May, 2021
π₯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.
π Study the paper
π²Channel: @Bioinformatics
π3
πA Review of Cell-Based Computational Modeling in Cancer Biology
πJournal: JCO Clinical Cancer Informatics
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
πJournal: JCO Clinical Cancer Informatics
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
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