πA survey on literature based discovery approaches in biomedical domain
π Read the paper
π²Channel: @Bioinformatics
π Read the paper
π²Channel: @Bioinformatics
πBiological Computation and Computational Biology: Survey, Challenges, and Discussion
π₯From abstract: We present a comprehensive review showcasing how biology and computer science can guide and benefit each other, resulting in improved understanding of biological processes and at the same time advances in the design of algorithms. Unfortunately, integration between biology and computer science is often challenging, especially due to the cultural idiosyncrasies of these two communities. In this study, we aim at highlighting how nature has inspired the development of various algorithms and techniques in computer science, and how computational techniques and mathematical modeling have helped to better understand various fields in biology. We identified existing gaps between biological computation and computational biology and advocate for bridging this gap between "wet" and "dry" research.
π Read the paper
π²Channel: @Bioinformatics
π₯From abstract: We present a comprehensive review showcasing how biology and computer science can guide and benefit each other, resulting in improved understanding of biological processes and at the same time advances in the design of algorithms. Unfortunately, integration between biology and computer science is often challenging, especially due to the cultural idiosyncrasies of these two communities. In this study, we aim at highlighting how nature has inspired the development of various algorithms and techniques in computer science, and how computational techniques and mathematical modeling have helped to better understand various fields in biology. We identified existing gaps between biological computation and computational biology and advocate for bridging this gap between "wet" and "dry" research.
π Read the paper
π²Channel: @Bioinformatics
π¬Free virtual workshop
π§βπ»Network analysis in Cytoscape
π Date:
Oct 20, 2021 04:00 PM
Time shows in Eastern Time (US and Canada)
πLocation: Virtually via Zoom
β«οΈMeeting ID: 952 1280 2221
β«οΈMeeting link to join
βΉοΈ More information
π²Channel: @Bioinformatics
π§βπ»Network analysis in Cytoscape
π Date:
Oct 20, 2021 04:00 PM
Time shows in Eastern Time (US and Canada)
πLocation: Virtually via Zoom
β«οΈMeeting ID: 952 1280 2221
β«οΈMeeting link to join
βΉοΈ More information
π²Channel: @Bioinformatics
π’ 2nd Bioinformatics and Biodiversity Conferences (BBC 2021)
π₯"Applied Bioinformatics in Molecular Medicine and Conservation Biology"
π Date: 27-28 November 2021
πLocation: Virtual via Zoom
βοΈRegistration link
πWebsite: https://bbc.akademisi.co.id/
π²Channel: @Bioinformatics
π₯"Applied Bioinformatics in Molecular Medicine and Conservation Biology"
π Date: 27-28 November 2021
πLocation: Virtual via Zoom
βοΈRegistration link
πWebsite: https://bbc.akademisi.co.id/
π²Channel: @Bioinformatics
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.