πArtificial Intelligence in Healthcare: Review and Prediction Case Studies
πJournal: Engineering (I.F.=12.834)
πPublish year: 2020
π Study the paper
π²Channel: @Bioinformatics
#review #ai #machine_learning #bimedical #healthcare
πJournal: Engineering (I.F.=12.834)
πPublish year: 2020
π Study the paper
π²Channel: @Bioinformatics
#review #ai #machine_learning #bimedical #healthcare
π6π1
πBioinformatics Guide
π₯A guide covering the Bioinformatics including the applications, libraries and tools that will make you a better and more efficient with Bioinformatics development.
π Last update: Jul 26, 2021
π Study the paper
π²Channel: @Bioinformatics
#github #programming
π₯A guide covering the Bioinformatics including the applications, libraries and tools that will make you a better and more efficient with Bioinformatics development.
π Last update: Jul 26, 2021
π Study the paper
π²Channel: @Bioinformatics
#github #programming
π19β€6π1
π The ASCA has the website (ascanet.org) and two telegram channels:
πΈNews (https://t.iss.one/ascanews), where you can get information about new methods in single-cell analysis, different meetings, bright single-cell researches and outstanding results, etc.
πΈChat (https://t.iss.one/ascatalk), where you can get technical support in single-cell analysis (just ask your question here) and communicate with other ASCA members.
π₯If you have an interest in single-cell analysis, please join the ASCA and Telegram channels.
π²Channel: @Bioinformatics
πΈNews (https://t.iss.one/ascanews), where you can get information about new methods in single-cell analysis, different meetings, bright single-cell researches and outstanding results, etc.
πΈChat (https://t.iss.one/ascatalk), where you can get technical support in single-cell analysis (just ask your question here) and communicate with other ASCA members.
π₯If you have an interest in single-cell analysis, please join the ASCA and Telegram channels.
π²Channel: @Bioinformatics
π13
π2021 Bioinformatics and Translational Informatics Best Papers
πSource: Yearbook of Medical Informatics
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
πSource: Yearbook of Medical Informatics
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
π2
πA bibliometric analysis of natural language processing in medical research
πJournal: BMC Medical Informatics and Decision Making (I.F.=3.298)
πPublish year: 2018
π Study the paper
π²Channel: @Bioinformatics
#review #nlp #medical
πJournal: BMC Medical Informatics and Decision Making (I.F.=3.298)
πPublish year: 2018
π Study the paper
π²Channel: @Bioinformatics
#review #nlp #medical
π5
πComputational Methods for Prediction of Human Protein-Phenotype Associations: A Review
πJournal: Phenomics
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #protein #phenotype
πJournal: Phenomics
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #protein #phenotype
π5
π A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
πJournal: Journal of Integrative Bioinformatics
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
#review #gene_prioritization
πJournal: Journal of Integrative Bioinformatics
πPublish year: 2019
π Study the paper
π²Channel: @Bioinformatics
#review #gene_prioritization
π8
πProceeding of The 11th International Conference on Health Information Science (HIS 2022)
π Date: October 28-30, 2022
π Location: Biarritz, France
π Study
π²Channel: @Bioinformatics
#conference #health
π Date: October 28-30, 2022
π Location: Biarritz, France
π Study
π²Channel: @Bioinformatics
#conference #health
π3β€1
π Functional characterisation of genetic variants influencing human food preferences using bioinformatics and in vivo models
πPhD Thesis from The University of Edinburgh, Scotland
πPublish year: 2022
π Study thesis
π²Channel: @Bioinformatics
#thesis #GWAS #food
πPhD Thesis from The University of Edinburgh, Scotland
πPublish year: 2022
π Study thesis
π²Channel: @Bioinformatics
#thesis #GWAS #food
π8
π Machine learning tools for biomarker discovery
πPhD Thesis from Sorbonne University, France
πPublish year: 2020
π Study thesis
π²Channel: @Bioinformatics
#thesis #biomarker #ml
πPhD Thesis from Sorbonne University, France
πPublish year: 2020
π Study thesis
π²Channel: @Bioinformatics
#thesis #biomarker #ml
π5π2
πGraph representation learning in bioinformatics: trends, methods and applications
πJournal: Briefings in Bioinformatics (I.F.=11.622)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #graph_representation_learning
πJournal: Briefings in Bioinformatics (I.F.=11.622)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #graph_representation_learning
π11β€2
Forwarded from Network Analysis Resources & Updates
π Co-expression network analysis using RNA-Seq data
π₯Free recorded tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland β College Park (June 15 2016).
πΉThis tutorial provide a simple overview of co-expression network analysis, with an emphasis on the use of RNA-Seq data.A motivation for the use of co-expression network analysis is provided and compared to other common types of RNA-Seq analyses such as differential expression analysis and gene set enrichment analysis. The use of adjacency matrices to represent networks is explored for several different types of networks and a small synthetic dataset is used to demonstrate each of the major steps in co-expression network construction and module detection. The tutorial portion of the presentation then applies some of these principles using a real dataset containing ~3000 genes, after filtering.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Co_expression_network #RNA_Seq
π₯Free recorded tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland β College Park (June 15 2016).
πΉThis tutorial provide a simple overview of co-expression network analysis, with an emphasis on the use of RNA-Seq data.A motivation for the use of co-expression network analysis is provided and compared to other common types of RNA-Seq analyses such as differential expression analysis and gene set enrichment analysis. The use of adjacency matrices to represent networks is explored for several different types of networks and a small synthetic dataset is used to demonstrate each of the major steps in co-expression network construction and module detection. The tutorial portion of the presentation then applies some of these principles using a real dataset containing ~3000 genes, after filtering.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Co_expression_network #RNA_Seq
YouTube
DC ISCB Workshop 2016 - Co-expression network analysis using RNA-Seq data (Keith Hughitt)
Overview
---------------
Tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland - College Park (June 15 2016).
Abstract
--------------
In this presentation, I provideβ¦
---------------
Tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland - College Park (June 15 2016).
Abstract
--------------
In this presentation, I provideβ¦
π9β€3
πMining Proteome Research Reports: A Birdβs Eye View
πJournal: Proteomes
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #proteome
πJournal: Proteomes
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #proteome
π8
πA review of deep learning applications in human genomics using next-generation sequencing data
πJournal: Human Genomics (I.F.=6.481)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #genomics #ngs
πJournal: Human Genomics (I.F.=6.481)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #genomics #ngs
π13
πΉ Biomarkers Identification using Machine Learning
π₯Recorded workshop
π Watch
π²Channel: @Bioinformatics
#video #biomarker #ml
π₯Recorded workshop
π Watch
π²Channel: @Bioinformatics
#video #biomarker #ml
YouTube
TCGA Biomarkers Identification using Machine Learning | Complete Walkthrough
Well, mostly doing this since people have been asking to connect the database with some basic machine learning script , so I might as well capitalized on this. Anyhow, I mostly wrote this with the mindset on education and not really on research so the codeβ¦
π12β€7π3