π Introduction to Weighted Gene Co-expression Network Analysis (WGCNA)
π½ Part 1 (Introduction)
π½ Part 2 (Detailed workflow)
π²Channel: @Bioinformatics
#video #WGCNA
π½ Part 1 (Introduction)
π½ Part 2 (Detailed workflow)
π²Channel: @Bioinformatics
#video #WGCNA
YouTube
Introduction to Weighted Gene Co-expression Network Analysis (WGCNA) | Bioinformatics 101
Weighted Gene Co-expression Network Analysis (WGCNA) is a commonly used unsupervised method to cluster genes based on their expression profiles. In this video I go over the idea behind WGCNA and provide a high-level overview of various steps that go intoβ¦
π13β€2π1
πBlockchain for Genomics: A Systematic Literature Review
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #blockchain #genomics
πPublish year: 2021
π Study the paper
π²Channel: @Bioinformatics
#review #blockchain #genomics
π15β€1π₯1
π¬A Day in the Life of a Bioinformatician
π Watch the interview
π²Channel: @Bioinformatics
#video
π Watch the interview
π²Channel: @Bioinformatics
#video
YouTube
A Day in the Life of a.... bioinformatician
Discover how bioinformatician Melpi Kasapiβs AI research could help in understanding groups of heart diseases better.
Melpi Kasapi is a PhD student whose research focusses on the use of AI and machine learning methods to combine genomic and imaging dataβ¦
Melpi Kasapi is a PhD student whose research focusses on the use of AI and machine learning methods to combine genomic and imaging dataβ¦
π4
πPhD position of Computational Biology
at UNSW Sydney
π£Deadline: September 25, 2022
π²Channel: @Bioinformatics
#phd #position
at UNSW Sydney
π£Deadline: September 25, 2022
π²Channel: @Bioinformatics
#phd #position
π4
π Online Tools for Teaching Cancer Bioinformatics
π Journal: Journal of Microbiology & Biology Education
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#teaching #cancer
π Journal: Journal of Microbiology & Biology Education
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#teaching #cancer
π7π2π€1
π½6 hours Bioinformatics lectures
π Part 1 (Genomics)
π Part 2 (Transcriptomics)
π Part 3 (Epigenetics)
π²Channel: @Bioinformatics
#video #Genomics #Transcriptomics #Epigenetics
π Part 1 (Genomics)
π Part 2 (Transcriptomics)
π Part 3 (Epigenetics)
π²Channel: @Bioinformatics
#video #Genomics #Transcriptomics #Epigenetics
β€15π8π₯8π1π€1
π Deep learning for drug repurposing: Methods, databases, and applications
πJournal: WIREs Computational Molecular Science (I.F.=11.5)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#drug #reproposing #deep_learning
πJournal: WIREs Computational Molecular Science (I.F.=11.5)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#drug #reproposing #deep_learning
π4π1
π Bioinformatics for genomics and gene editing
π₯From the UniversitΓ© de MontrΓ©al
π½ Watch
π²Channel: @Bioinformatics
#video #genomics #editing
π₯From the UniversitΓ© de MontrΓ©al
π½ Watch
π²Channel: @Bioinformatics
#video #genomics #editing
π6
πTen quick tips for biomarker discovery and validation analyses using machine learning
πJournal: PLOS Computational Biology (I.F.=4.779)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
πJournal: PLOS Computational Biology (I.F.=4.779)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
π1
πDeep learning-based clustering approaches for bioinformatics
π Journal:Briefings in Bioinformatics (I.F=13.994)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#review #deep_learning #clustering
π Journal:Briefings in Bioinformatics (I.F=13.994)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#review #deep_learning #clustering
π6π2
π The History of Genomics Told Through Machine Learning
π₯From NIH National Human Genome Research Institute
π½ Watch
π²Channel: @Bioinformatics
#video #genomics #machine_learning
π₯From NIH National Human Genome Research Institute
π½ Watch
π²Channel: @Bioinformatics
#video #genomics #machine_learning
YouTube
The History of Genomics Told Through Machine Learning
To commemorate the 10th anniversary of the NHGRI History of Genomics Program, NHGRI hosted a virtual lecture titled βThe history of genomics told through machine learning: A celebration of 10 years of the NHGRI history program.β LuΓs Amaral and Spencer Hongβ¦
π8β€1
πEvolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction
π Journal: Current Genomics (I.F=2.689)
πPublish year: 2020
π Study paper
π²Channel: @Bioinformatics
#review #PPI
π Journal: Current Genomics (I.F=2.689)
πPublish year: 2020
π Study paper
π²Channel: @Bioinformatics
#review #PPI
π7β€3π2
πData Science in Undergraduate Life Science Education
π Journal: BioScience (I.F=11.566)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#education #data_science
π Journal: BioScience (I.F=11.566)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#education #data_science
π7
πMachine Learning for Genomic Data
πBSc thesis from University of NUS, Singapore
πPublish year: 2019
π₯Abstract: This report explores the application of machine learning techniques on short timeseries gene expression data. Although standard machine learning algorithms work well on longer time-seriesβ, they often fail to find meaningful insights from fewer timepoints.
In this report, we explore model-based clustering techniques. We combine popular unsupervised learning techniques like K-Means, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture Models are fairly standard, while Hidden Markov Model and Bayesian Networks clustering are more novel ideas that suit time-series gene expression data.
π Study thesis
π²Channel: @Bioinformatics
#thesis #genomic #machine_learning
πBSc thesis from University of NUS, Singapore
πPublish year: 2019
π₯Abstract: This report explores the application of machine learning techniques on short timeseries gene expression data. Although standard machine learning algorithms work well on longer time-seriesβ, they often fail to find meaningful insights from fewer timepoints.
In this report, we explore model-based clustering techniques. We combine popular unsupervised learning techniques like K-Means, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture Models are fairly standard, while Hidden Markov Model and Bayesian Networks clustering are more novel ideas that suit time-series gene expression data.
π Study thesis
π²Channel: @Bioinformatics
#thesis #genomic #machine_learning
π8
π R Workshop: RNA-Seq From Raw to Processed Data
π½ Watch
π²Channel: @Bioinformatics
#video #workshop #rna-seq
π½ Watch
π²Channel: @Bioinformatics
#video #workshop #rna-seq
YouTube
R Workshop Series Part 1 - RNA-Seq: From Raw to Processed Data
As part of GrasPods Welcome Week 2021, weβre delighted to bring you Part 1 of a step-by-step RNA-seq data analysis workshop, in association with the BC Childrenβs Hospital Research Instituteβs Trainee Omics Group (TOG).
TOG is the resident graduate traineeβ¦
TOG is the resident graduate traineeβ¦
π24β€5
π Determining ProteinβProtein Interaction Using Support Vector Machine: A Review
π Journal: IEEE Access (I.F=3.476)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#review #PPI #SVM
π Journal: IEEE Access (I.F=3.476)
πPublish year: 2021
π Study paper
π²Channel: @Bioinformatics
#review #PPI #SVM
π9
π¨βπ« Registration is open to one month International Bioinformatics Workshop by DE<code>LIFE
π₯ Data science and Machine learning with - "R" π₯
π Duration: 26 September - 22 October, 2022
βοΈ Registration Link
https://decodelife.co.in
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Key Features :
β«οΈ 20 sessions with approximately 30 hrs of learning.
β«οΈE- Certificate of Participation.
Note: 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 and Machine learning with - "R" π₯
π Duration: 26 September - 22 October, 2022
βοΈ Registration Link
https://decodelife.co.in
π² Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants
π₯Key Features :
β«οΈ 20 sessions with approximately 30 hrs of learning.
β«οΈE- Certificate of Participation.
Note: 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
π8
π Introducing Programming Skills for Life Science Students
π Journal: Biochemistry and Molecular Biology Education (I.F=1.369)
πPublish year: 2019
π Study paper
π²Channel: @Bioinformatics
#programming
π Journal: Biochemistry and Molecular Biology Education (I.F=1.369)
πPublish year: 2019
π Study paper
π²Channel: @Bioinformatics
#programming
π10
πChanging Trends in Computational Drug Repositioning
π Journal: Pharmaceuticals (I.F=5.215)
πPublish year: 2018
π Study paper
π²Channel: @Bioinformatics
#review #drug #repositioning
π Journal: Pharmaceuticals (I.F=5.215)
πPublish year: 2018
π Study paper
π²Channel: @Bioinformatics
#review #drug #repositioning
π6π1
πBest practices for the interpretation and reporting of clinical whole genome sequencing
π Journal: npj Genomic Medicine (I.F=6.083)
πPublish year: 2022
π Study paper
π²Channel: @Bioinformatics
#wgs
π Journal: npj Genomic Medicine (I.F=6.083)
πPublish year: 2022
π Study paper
π²Channel: @Bioinformatics
#wgs
π6β€1
πA review of deep learning applications in human genomics using next-generation sequencing data
π Journal: Human Genomics (I.F=6.481)
πPublish year: 2022
π Study paper
π²Channel: @Bioinformatics
#review #deep_learning #genomics #ngs
π Journal: Human Genomics (I.F=6.481)
πPublish year: 2022
π Study paper
π²Channel: @Bioinformatics
#review #deep_learning #genomics #ngs
π5β€1