👨‍🏫 Registration is open to one month International Bioinformatics Workshop by DE<code>LIFE
💥 Plant genomics - 3rd Edition, 2022 💥

🗓 Duration: 27 August- 19 September, 2022

✍️ Registration Link:
https://decodelife.co.in

💲 Fees: Rupees 1200 for Indian Participants /USD 25 for international Participants

💥Key Features:
▫️ 20 sessions with approximately 30 hrs of learning.
▫️E- Certificate of Participation.

❔Frequently asked questions:
https://decodelife.co.in/faq/

📲Channel: @Bioinformatics

📑A review of methods and databases for metagenomic classification and assembly

📘 Journal:Briefings in Bioinformatics (I.F=13.994)

🗓Publish year: 2019

📎 Study paper

📲Channel: @Bioinformatics

#review #metagenomic

🏢 The 1st International Workshop on Data Analysis in Life Science
💥Online Workshop

🗓 Date: September 19-23, 2022

💣 Paper submission deadline: 31 August 2022

🖇Website: http://www.bioinformatics.deib.polimi.it/DALS2022/

📲Channel: @Bioinformatics

#workshop #online

📄Modularity in Biological Networks

📘Journal: Frontiers in Genetics (I.F= 4.772 )

🗓Publish year: 2021

📎 Study the paper

📲Channel: @ComplexNetworkAnalysis
#review #modularity

📑Advanced machine-learning techniques in drug discovery

📘 Journal: Drug Discovery Today (I.F=8.369)

🗓Publish year: 2021

📎 Study paper

📲Channel: @Bioinformatics

#review #drug #machine_learning

🎞 Introduction to Weighted Gene Co-expression Network Analysis (WGCNA)

📽 Part 1 (Introduction)
📽 Part 2 (Detailed workflow)

📲Channel: @Bioinformatics
#video #WGCNA

📃Blockchain for Genomics: A Systematic Literature Review

🗓Publish year: 2021

📎 Study the paper

📲Channel: @Bioinformatics

#review #blockchain #genomics

🎬A Day in the Life of a Bioinformatician

🌐 Watch the interview

📲Channel: @Bioinformatics
#video

🎓PhD position of Computational Biology
at UNSW Sydney

💣Deadline: September 25, 2022

📲Channel: @Bioinformatics
#phd #position

📑 Online Tools for Teaching Cancer Bioinformatics

📘 Journal: Journal of Microbiology & Biology Education

🗓Publish year: 2021

📎 Study paper

📲Channel: @Bioinformatics

#teaching #cancer

📽6 hours Bioinformatics lectures

🎞 Part 1 (Genomics)
🎞 Part 2 (Trannoscriptomics)
🎞 Part 3 (Epigenetics)

📲Channel: @Bioinformatics
#video #Genomics #Trannoscriptomics #Epigenetics

📃 Deep learning for drug repurposing: Methods, databases, and applications

📘Journal: WIREs Computational Molecular Science (I.F.=11.5)
🗓Publish year: 2022

📎 Study the paper

📲Channel: @Bioinformatics

#drug #reproposing #deep_learning

🎞 Bioinformatics for genomics and gene editing
💥From the Université de Montréal

📽 Watch

📲Channel: @Bioinformatics
#video #genomics #editing

📄Ten quick tips for biomarker discovery and validation analyses using machine learning

📘Journal: PLOS Computational Biology (I.F.=4.779)
🗓Publish year: 2022

📎 Study the paper

📲Channel: @Bioinformatics

📑Deep learning-based clustering approaches for bioinformatics

📘 Journal:Briefings in Bioinformatics (I.F=13.994)

🗓Publish year: 2021

📎 Study paper

📲Channel: @Bioinformatics
#review #deep_learning #clustering

🎞 The History of Genomics Told Through Machine Learning
💥From NIH National Human Genome Research Institute

📽 Watch

📲Channel: @Bioinformatics
#video #genomics #machine_learning

📄Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

📘 Journal: Current Genomics (I.F=2.689)

🗓Publish year: 2020

📎 Study paper

📲Channel: @Bioinformatics
#review #PPI

📃Data Science in Undergraduate Life Science Education

📘 Journal: BioScience (I.F=11.566)

🗓Publish year: 2021

📎 Study paper

📲Channel: @Bioinformatics
#education #data_science

🎓Machine Learning for Genomic Data

📘BSc thesis from University of NUS, Singapore

🗓Publish year: 2019

💥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-series’, 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.

📎 Study thesis

📲Channel: @Bioinformatics
#thesis #genomic #machine_learning

🎞 R Workshop: RNA-Seq From Raw to Processed Data

📽 Watch

📲Channel: @Bioinformatics
#video #workshop #rna-seq

📑 Determining Protein–Protein Interaction Using Support Vector Machine: A Review

📘 Journal: IEEE Access (I.F=3.476)

🗓Publish year: 2021

📎 Study paper

📲Channel: @Bioinformatics
#review #PPI #SVM