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
π1
πMachine learning methods for prediction of cancer driver genes: a survey paper
π₯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.
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
π₯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.
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
π2
π¨βπ« Registration is open for One month Bioinformatics Workshop
βοΈ Plant Genomics and BioinformaticsβοΈ
π Duration: 19 March - 17 April, 2022
βοΈ Registration Link
https://decodelife.co.in
π²Fees: Rupees 1200 for Indian Participants / USD 25 for international Participants
π₯Key Features :
β«οΈ 20 sessions with approximately 35 hrs of learning.
β«οΈE- Certificate of Participation
βNote: Workshop is suitable for all bioinformatics enthusiasts and not restricted to plant bioinformatics.
π²Channel: @Bioinformatics
βοΈ Plant Genomics and BioinformaticsβοΈ
π Duration: 19 March - 17 April, 2022
βοΈ Registration Link
https://decodelife.co.in
π²Fees: Rupees 1200 for Indian Participants / USD 25 for international Participants
π₯Key Features :
β«οΈ 20 sessions with approximately 35 hrs of learning.
β«οΈE- Certificate of Participation
βNote: Workshop is suitable for all bioinformatics enthusiasts and not restricted to plant bioinformatics.
π²Channel: @Bioinformatics
π2
π¬ Exploring Gene Ontology GO annotations tools and resources
π Watch
π²Channel: @Bioinformatics
π Watch
π²Channel: @Bioinformatics
YouTube
Exploring Gene Ontology GO annotations tools and resources
The Gene Ontology (GO) resource is the world's most comprehensive source of information about the function of gene products (proteins and non-coding RNAs). The GO consists of two main components: an ontology of terms that describe biological phenomena andβ¦
π8
πArtificial intelligence for precision oncology: beyond patient stratification
πJournal: Nature Precision Oncology (I.F.=8.254)
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
πJournal: Nature Precision Oncology (I.F.=8.254)
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
π2