π’ 1st Central Asia Genomics Symposium
π Date: December 9-10, 2021
πLocation: Virtual via Zoom and In person at National University of Uzbekistan
βοΈRegistration link (Free)
π£ Abstract submission deadline: November 19, 2021
πWebsite: https://www.centralasiagenomics.com/
π²Channel: @Bioinformatics
π Date: December 9-10, 2021
πLocation: Virtual via Zoom and In person at National University of Uzbekistan
βοΈRegistration link (Free)
π£ Abstract submission deadline: November 19, 2021
πWebsite: https://www.centralasiagenomics.com/
π²Channel: @Bioinformatics
π₯Good tutorial of Cytoscape for biological network visualization
π An Introduction to Network Analysis and Cytoscape
π Study the article
π²Channel: @Bioinformatics
π An Introduction to Network Analysis and Cytoscape
π Study the article
π²Channel: @Bioinformatics
πͺπΈRegion specific post
π£ BIOINFO CLUB OCTUBRE 2021 π£
RNA: una paleta de mΓ‘s de 170 colores
π 26 de Octubre
π De 19h a 20:30h, CEST
π Imparte:
π Ricardo LebrΓ³n, PhD (UAL, CIAMBITAL)
βΉοΈ Registro + informaciΓ³n:
https://t.co/9fPuBduhDh?amp=1
π²Channel: @Bioinformatics
π£ BIOINFO CLUB OCTUBRE 2021 π£
RNA: una paleta de mΓ‘s de 170 colores
π 26 de Octubre
π De 19h a 20:30h, CEST
π Imparte:
π Ricardo LebrΓ³n, PhD (UAL, CIAMBITAL)
βΉοΈ Registro + informaciΓ³n:
https://t.co/9fPuBduhDh?amp=1
π²Channel: @Bioinformatics
Eventbrite
RNA: una paleta de mΓ‘s de 170 colores | BioInfo Club
Revisaremos las principales modificaciones que afectan al ARN, sus implicaciones y posible utilidad en el estudio de enfermedades.
π Free webinar
*Multi-Omics approach to infectious diseases: Current status and perspectives*
π Date: October 23 2021
π Time: 5.00 - 6.00 PM (IST)
βπ» Registrations
π²Channel: @Bioinformatics
*Multi-Omics approach to infectious diseases: Current status and perspectives*
π Date: October 23 2021
π Time: 5.00 - 6.00 PM (IST)
βπ» Registrations
π²Channel: @Bioinformatics
πVisual Analytics of Genomic and Cancer Data: A Systematic Review
π₯From abstract: ... This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data...
π Study the paper
π²Channel: @Bioinformatics
π₯From abstract: ... This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data...
π Study the paper
π²Channel: @Bioinformatics
πProgramming for Biology
π₯Online course with full materials
ππ» Website: http://programmingforbiology.org
π Associate Github including all course materials
π²Channel: @Bioinformatics
π₯Online course with full materials
ππ» Website: http://programmingforbiology.org
π Associate Github including all course materials
π²Channel: @Bioinformatics
π1
πRepresentation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities
π Study the paper
π²Channel: @Bioinformatics
π Study the paper
π²Channel: @Bioinformatics
π Free webinar
Fundamentals of Next Generation Sequencing: A Sneak Peek into Genomics Lab
π Date: 28 October 2021
π Time: 11:00 AM
βπ» Registrations
π²Channel: @Bioinformatics
Fundamentals of Next Generation Sequencing: A Sneak Peek into Genomics Lab
π Date: 28 October 2021
π Time: 11:00 AM
βπ» Registrations
π²Channel: @Bioinformatics
π1
π’ 5th International Symposium on Bioinformatics (InSyB) 2021
π Date: December 15-17, 2021
πLocation: Virtually in Turkey with Bezmialem VakΔ±f University
βοΈRegistration and submissions are FREE!
π£ Abstract submission deadline: 15.11.2021
πWebsite: https://insyb2021.bezmialem.edu.tr/
π²Channel: @Bioinformatics
π Date: December 15-17, 2021
πLocation: Virtually in Turkey with Bezmialem VakΔ±f University
βοΈRegistration and submissions are FREE!
π£ Abstract submission deadline: 15.11.2021
πWebsite: https://insyb2021.bezmialem.edu.tr/
π²Channel: @Bioinformatics
π« 5 tips for getting into computational biology
π Study the article
π²Channel: @Bioinformatics
π Study the article
π²Channel: @Bioinformatics
ARCHIVE
5 tips for getting into computational biology
By Fatima Vayani, Kingβs College London I discovered computational biology (or bioinformatics, as it is also known) by chance during an internship when I was 17. I have always been a curious personβ¦
π§ͺMachine Learning in Enzyme Engineering
Abstract: Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.
π Study the paper
π²Channel: @Bioinformatics
Abstract: Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.
π Study the paper
π²Channel: @Bioinformatics
π1
π§°Resources to become a computational biologist outside of academia
π Study
π²Channel: @Bioinformatics
π Study
π²Channel: @Bioinformatics
π° A good list of papers about machine learning for proteins
π Study
π²Channel: @Bioinformatics
π Study
π²Channel: @Bioinformatics
GitHub
GitHub - yangkky/Machine-learning-for-proteins: Listing of papers about machine learning for proteins.
Listing of papers about machine learning for proteins. - GitHub - yangkky/Machine-learning-for-proteins: Listing of papers about machine learning for proteins.
π Guide to go from Computer Science background to Bioinformatics
π₯ Watch
π²Channel: @Bioinformatics
π₯ Watch
π²Channel: @Bioinformatics
YouTube
Going from CS to bioinformatics
Answering some common questions I get from computer science/software people about going into bioinformatics:
* "I'm a computer science major. Is it possible for me to get into bioinformatics since I don't really have a biology background?"
* "I'm really interestedβ¦
* "I'm a computer science major. Is it possible for me to get into bioinformatics since I don't really have a biology background?"
* "I'm really interestedβ¦
π¨βπ«Introduction to Biomedical Data Science and Health Informatics
π₯Yale University Full archive July 2020
Join us for an introduction to basic biomedical data science knowledge and health informatics skills. This course is targeted for beginners in informatics. No previous experience is required.
π Go to online course
π²Channel: @Bioinformatics
π₯Yale University Full archive July 2020
Join us for an introduction to basic biomedical data science knowledge and health informatics skills. This course is targeted for beginners in informatics. No previous experience is required.
π Go to online course
π²Channel: @Bioinformatics