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
πArtificial intelligence in cancer target identification and drug discovery
πJournal: Signal Transduction and Targeted Therapy (I.F.=38.104)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #ai #cancer #drug
πJournal: Signal Transduction and Targeted Therapy (I.F.=38.104)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #ai #cancer #drug
π9β€1
π¨βπ« Registration is open to Three week International Bioinformatics Workshop by DE<code>LIFE
π₯ Data Science with Python - 3rd Ed, 2023π₯
π Duration: 9 January 2023 - 04 February, 2023
βοΈ Registration Link:
https://decodelife.co.in
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Key Features:
β«οΈ 18 sessions with approximately 30 hrs of learning.
β«οΈE- Certificate of Participation.
βοΈNote: video recording will be shared with participants immediately after each session along with relevant material.
βFrequently asked questions: https://decodelife.co.in/faq/
π²Channel: @Bioinformatics
π₯ Data Science with Python - 3rd Ed, 2023π₯
π Duration: 9 January 2023 - 04 February, 2023
βοΈ Registration Link:
https://decodelife.co.in
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Key Features:
β«οΈ 18 sessions with approximately 30 hrs of learning.
β«οΈE- Certificate of Participation.
βοΈNote: video recording will be shared with participants immediately after each session along with relevant material.
βFrequently asked questions: https://decodelife.co.in/faq/
π²Channel: @Bioinformatics
π11
πLearning Protein Evolution and Structure
πPhD Thesis from Stockholm University, Sweden
π₯Describing several technical concepts in a simple manner
πPublish year: 2022
π Study thesis
π²Channel: @Bioinformatics
#thesis #protein
πPhD Thesis from Stockholm University, Sweden
π₯Describing several technical concepts in a simple manner
πPublish year: 2022
π Study thesis
π²Channel: @Bioinformatics
#thesis #protein
π6π2
π§βπ»Free Bioinformatics seminar talk from Translational Data Analytics Institute (The Ohio State University)
π Date: January 26, 2023
π Time: 1:00PM - 2:00PM Eastern Time (US and Canada)
π Location: Hybrid 301 (Pomerene Hall, Zoom)
βπ» Details and Registration
π²Channel: @Bioinformatics
#webinar
π Date: January 26, 2023
π Time: 1:00PM - 2:00PM Eastern Time (US and Canada)
π Location: Hybrid 301 (Pomerene Hall, Zoom)
βπ» Details and Registration
π²Channel: @Bioinformatics
#webinar
tdai.osu.edu
Seminar: Dr. Dongjun Chung (bioinformatics)
TDAI Affiliate Faculty member Dr. Dongjun Chung, Associate Professor of Bioinformatics, will give a seminar talk entitled "Statistical Approaches for Spatial Transcriptomics Data Analysis" on Thursday, January 26, at 1:00 p.m.
π5β€2π₯2π1π1
π₯Introduction to EMBL-EBI for teachers and trainersπ₯
π§βπ»Free webinar from EMBL-EBI training
π Date: January 18, 2022
π Time: 15:30 - 16:30 ( GMT )
π Location: Online, Zoom
βπ» Details and Registration
π²Channel: @Bioinformatics
#webinar
π§βπ»Free webinar from EMBL-EBI training
π Date: January 18, 2022
π Time: 15:30 - 16:30 ( GMT )
π Location: Online, Zoom
βπ» Details and Registration
π²Channel: @Bioinformatics
#webinar
π8β€1
πIdentifying disease-associated genes based on artificial intelligence
πPhD Thesis from University of Saskatchewan, Canada
πPublish year: 2019
π Study thesis
π²Channel: @Bioinformatics
#thesis #disease #gene
πPhD Thesis from University of Saskatchewan, Canada
πPublish year: 2019
π Study thesis
π²Channel: @Bioinformatics
#thesis #disease #gene
π8β€1
π»One of the easiest molecular docking solutions online:
π₯Draw your ligand, select your target and click on Dock!
π Watch the clip
π²Channel: @Bioinformatics
#video #docking
π₯Draw your ligand, select your target and click on Dock!
π Watch the clip
π²Channel: @Bioinformatics
#video #docking
YouTube
How to do 1 click docking: mcule
#PymolBiomoleculesTutorials
Mcule.com is the online drug discovery platform. It offers a unique solution for pharma and biotech companies by providing molecular modeling tools, hit identification, hit expansion, lead optimization. and a database of 100+ millionβ¦
Mcule.com is the online drug discovery platform. It offers a unique solution for pharma and biotech companies by providing molecular modeling tools, hit identification, hit expansion, lead optimization. and a database of 100+ millionβ¦
π7β€2π1π₯1