📃Network embedding in biomedical data science

📘Journal: Briefings in Bioinformatics (I.F.=11.622)
🗓Publish year: 2020

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📲Channel: @Bioinformatics

👨‍🏫 An Open-Source and Interactive Learning Environment for Bioinformatics

💥From Abstract: Lockdowns and “stay-at-home” orders, starting in March 2020, shuttered bench and field dependent research across the world as a consequence of the global COVID-19 pandemic. The pandemic continues to have an impact on research progress and career development, especially for graduate students and early career researchers, as strict social distance limitations stifle ongoing research and impede in-person educational programs. The goal of the Bioinformatics Virtual Coordination Network (BVCN) was to reduce some of these impacts by helping research biologists learn new skills and initiate computational projects as alternative ways to carry out their research. The BVCN was founded in April 2020...

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📲Channel: @Bioinformatics

🗃 A curated list of Bioinformatics software, resources, and libraries
Mostly command line based, and free or open-source

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📲Channel: @Bioinformatics

@llbschool NyBerMan Bioinformatics Europe organizing 2 days Virtual Workshop on Post Molecular Dynamics Simulation analysis in GROMACS

🧑‍🏫 Trainer :
Dr. Shiv bharadwaj & Team
Prague, Czech Republic.

The workshop focuses on the following
🪧 Post-MD simulations analysis tools using GROMACS and covers the basics to advanced tools such as Stability analysis, Interaction analysis, Principle Component Analysis, MMGPSA calculations, etc.
🪧Will also help you frame the right questions and hypotheses while analyzing your MD trajectories.
🪧 All the analysis will be taught using both Protein and Protein-Ligand complex.

🪑Total Available seats : 40

✍️ Registration link :
https://tinyurl.com/nybermd

Benefits of the workshop
📄 A chance to write an MD analysis protocol and publish it in our https://loopsnstrands.com/
📄The Merit certificates are provided with mentioning grades, only upon strict evolutions of every individual written pipeline.

Any queries contact us via telegram


📲Channel: @Bioinformatics

🎞Exploring genotype phenotype data using the GWAS Catalog

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📲Channel: @Bioinformatics

📑Data Integration Challenges for Machine Learning in Precision Medicine

📘Journal: Frontiers in Medicine (I.F.=5.091)
🗓Publish year: 2022

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📲Channel: @Bioinformatics

📑Collaborative analysis of genomic data: vision and challenges

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📲Channel: @Bioinformatics

📣 Q&A - 6th Bioinformatics Conference 📣
🇪🇸 Training and professional perspectives in Bioinformatics in Spain

🗓 Date: 25 February 2022
🕖 Time: From 19:00h to 20:30h, CET

ℹ️ Registration + information:
📎 https://buff.ly/3IcaBlt

📲Channel: @Bioinformatics

📄Deep learning shapes single-cell data analysis

💥Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep learning methods for analysing single-cell data.

📘Journal: Nature Reviews Molecular Cell Biology (I.F.=94.444)
🗓Publish year: 23 February 2022

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📲Channel: @Bioinformatics

📃Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis

📘Journal: Procedia Computer Science
🗓Publish year: 2021

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📲Channel: @Bioinformatics

👨🏻‍💻2 weeks Bioinformatics Virtual Workshop by Dollar Education

💥 Bioinformatics Applications in Tackling COVID-19 Pandemic💥

🗓 Date: 14 March - 24 March, 2022
⏰ Time: 6:00 pm IST

✍️ Registration Link
https://www.dollareducation.org

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

💥Topics:
▫️Bioinformatics Resources
▫️Quick designs of vaccines
▫️Determining pre-existing immunity
▫️SARS-CoV2 Genomics
▫️Detecting lineages and variants of concerns
▫️Genomic evaluation
▫️Drug-repurposing

ℹ️Contact and More information:
https://news.1rj.ru/str/+qCL0SBRZSLZmMDE1

📲Channel: @Bioinformatics

🎓 PhD Thesis, Rice University

💥The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes 💥

Abstract: Recent years have witnessed a surge in the application of graph theory to complex biological systems. The ability of graph theory to extract essential knowledge from the plethora of information embedded in a complex system has proven rewarding in many disciplines ranging from evolutionary biology to cancer prediction. The modular structure of complex networks, a branch of graph theory, is the focus of this text. Its guiding hypothesis, derived from statistical physics, states that modularity correlates with performances of complex biological systems and that the direction of correlation is mediated by environmental stress. This text tests and expands the theory of modularity in three main contexts - gene co-expression networks, human brain networks, and genome-scale metabolic networks. It is demonstrated that modularity of cancer-associated gene co-expression network is predictive of cancer aggressiveness, that modularity of resting-state functional connectivity in healthy young adults correlates with cognitive performance and the correlation is mediated by task complexity, and that modularity of human brain metabolic network not only predicts risk for Alzheimer’s disease but also defines the brain regions where metabolism correlates with dementia-risk gene expression. In addition, definition of modularity and maximization algorithm for bipartite, directed, and weighted networks are proposed and subsequently tested on a genome-scale bacterial metabolic network under different levels of survival stress. Overall, results presented here support the hypothesis of modularity’s role as a performance predictor for complex systems. The existing theory of modularity has been validated in numerous scenarios and expanded with the concept of ”network fragmentation”. Modularity can be applied to clinical settings for risk evaluation, and even contribute to individualized therapy. It can also help understand the mechanism of biological processes that are currently poorly understood. Of course, future research is needed to further the understanding of the emergence of modularity in complex systems and its application. Better definition of modularity, faster and more functionally appropriate clustering algorithm, and the collection of larger amount of higher quality data are crucial for the advancement of the field.

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📲Channel: @Bioinformatics

🎬How to develop models for cancer using Convolution Neural Networks

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📲Channel: @Bioinformatics