💊Bioinformatics and Drug Discovery
From Abstract: High-throughput data such as genomic, epigenetic, genome architecture, cistromic, trannoscriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery & drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules & metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data & software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
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📲Channel: @Bioinformatics
From Abstract: High-throughput data such as genomic, epigenetic, genome architecture, cistromic, trannoscriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery & drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules & metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data & software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
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📲Channel: @Bioinformatics
🧬A Beginner’s Guide to Analysis of RNA Sequencing Data
From Abstract: Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially. With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.
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📲Channel: @Bioinformatics
From Abstract: Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially. With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.
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📲Channel: @Bioinformatics
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📑Network bioinformatics analysis provides insight into drug repurposing for COVID-19
💥New paper with inspiring process method
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📲Channel: @Bioinformatics
💥New paper with inspiring process method
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📲Channel: @Bioinformatics
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👨🏫 Free Online Course: Statistical Inference and Modeling for High-throughput Experiments
from Harvard University supporting by NIH Grant
🗓 Duration: 4 weeks long
🕦 Time Commitment: 2-4 hours per week
✍️ Level: Intermediate, self paced
💥What you'll learn:
▫️Organizing high throughput data
▫️Multiple comparison problem
▫️Family Wide Error Rates
▫️False Discovery Rate
▫️Error Rate Control procedures
▫️Bonferroni Correction
🔰Open till September 14, 2021
ℹ️ More information and Participation
📲Channel: @Bioinformatics
from Harvard University supporting by NIH Grant
🗓 Duration: 4 weeks long
🕦 Time Commitment: 2-4 hours per week
✍️ Level: Intermediate, self paced
💥What you'll learn:
▫️Organizing high throughput data
▫️Multiple comparison problem
▫️Family Wide Error Rates
▫️False Discovery Rate
▫️Error Rate Control procedures
▫️Bonferroni Correction
🔰Open till September 14, 2021
ℹ️ More information and Participation
📲Channel: @Bioinformatics
Harvard University
Statistical Inference and Modeling for High-throughput Experiments | Harvard University
A focus on the techniques commonly used to perform statistical inference on high throughput data.
🗄Cancer Analysis recorded workshop
🎞 Some sessions:
▫️Data, formats and Databases
▫️Genome Alignment
▫️Somatic Genomic Alteration
▫️Single Cell Genomics - DNA
▫️Gene Expression Profiling
▫️Genes to Pathways
▫️Genes to Networks
▫️Multi-omic data integration
▫️Integration of Clinical Data
📲Channel: @Bioinformatics
🎞 Some sessions:
▫️Data, formats and Databases
▫️Genome Alignment
▫️Somatic Genomic Alteration
▫️Single Cell Genomics - DNA
▫️Gene Expression Profiling
▫️Genes to Pathways
▫️Genes to Networks
▫️Multi-omic data integration
▫️Integration of Clinical Data
📲Channel: @Bioinformatics
📑On the use of Networks in Biomedicine
💥From Abstract: Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks.
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📲Channel: @Bioinformatics
💥From Abstract: Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks.
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📲Channel: @Bioinformatics
📹 Free webcast from nature
Applications of single-cell multi-omics techniques in molecular biology, genetics and cancer research
🗓 Date: September 22, 2021
🕐 Time: 9am PDT / 12pm EDT / 5pm BST / 6pm CEST
✍️ Registration link
🖇 More information
📲Channel: @Bioinformatics
Applications of single-cell multi-omics techniques in molecular biology, genetics and cancer research
🗓 Date: September 22, 2021
🕐 Time: 9am PDT / 12pm EDT / 5pm BST / 6pm CEST
✍️ Registration link
🖇 More information
📲Channel: @Bioinformatics
🥛Probiotics has no bad effect on any diseases
💥Twenty years review of probiotic meta-analyses articles: Effects on disease prevention and treatment
From abstract: Here, we collected close to 300 meta-analysis articles for 20 years, investigating the effect of probiotics in the prevention and treatment of diseases. The goal of this study is to provide an overview of all meta-analysis articles of the effects of probiotics on various human diseases. Papers studied and categorized and investigated in order to present valuable insights for researchers in the field. According to the results, most meta-analyses indicated probiotics were 79% effective in preventing or treating the diseases. Some articles have also reported no positive effects, but there is not any paper in our study confirming the detrimental influence of probiotic effect on human health.
🌐 Read the interesting review
📲Channel: @Bioinformatics
💥Twenty years review of probiotic meta-analyses articles: Effects on disease prevention and treatment
From abstract: Here, we collected close to 300 meta-analysis articles for 20 years, investigating the effect of probiotics in the prevention and treatment of diseases. The goal of this study is to provide an overview of all meta-analysis articles of the effects of probiotics on various human diseases. Papers studied and categorized and investigated in order to present valuable insights for researchers in the field. According to the results, most meta-analyses indicated probiotics were 79% effective in preventing or treating the diseases. Some articles have also reported no positive effects, but there is not any paper in our study confirming the detrimental influence of probiotic effect on human health.
🌐 Read the interesting review
📲Channel: @Bioinformatics
📑Review of machine learning methods for RNA secondary structure prediction
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📲Channel: @Bioinformatics
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📲Channel: @Bioinformatics
📑The Incredible Convergence Of Deep Learning And Genomics
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📲Channel: @Bioinformatics
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📲Channel: @Bioinformatics
👨🏻💻Accurate predictions of the structures of almost all human proteins
from Nature News
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📲Channel: @Bioinformatics
from Nature News
🌐 Read the article
📲Channel: @Bioinformatics
📖Free online tutorial
💥Running molecular dynamics simulations using GROMACS
🌐 Study
📲Channel: @Bioinformatics
💥Running molecular dynamics simulations using GROMACS
🌐 Study
📲Channel: @Bioinformatics
📘 The Biologist’s Guide to Computing
💥Free online book
This book introduces practical computing concepts to biologists without needing prior knowledge
🌐 Study the book
📲Channel: @Bioinformatics
💥Free online book
This book introduces practical computing concepts to biologists without needing prior knowledge
🌐 Study the book
📲Channel: @Bioinformatics
🎦 Medical Machine Learning recorded movies
1. Spotlight Session
2. The Difference Between Machine Learning and Statistics
3. Unsupervised Machine Learning
4. The Clustering Problem
5. Practice Session : Medical Datasets and Machine Learning Software
6. Learning from Mistakes and & the K-Means Algorithm
7. Practice Session : Using K-Means in Weka
8. From Unsupervised to Supervised Machine Learning
9. Learning from Those Who Should Teach & the K-Nearest..., Part 2
10. The KNN and Decision Tree Algorithms
📲Channel: @Bioinformatics
1. Spotlight Session
2. The Difference Between Machine Learning and Statistics
3. Unsupervised Machine Learning
4. The Clustering Problem
5. Practice Session : Medical Datasets and Machine Learning Software
6. Learning from Mistakes and & the K-Means Algorithm
7. Practice Session : Using K-Means in Weka
8. From Unsupervised to Supervised Machine Learning
9. Learning from Those Who Should Teach & the K-Nearest..., Part 2
10. The KNN and Decision Tree Algorithms
📲Channel: @Bioinformatics
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🧑🎨 Ten simple rules to colorize biological data visualization
Abstract: Methods for visualization of biological data continue to improve, but there is still a fundamental challenge in colorization of these visualizations (vis). Visual representation of biological data should not overwhelm, obscure, or bias the findings, but rather make them more understandable. This is often due to the challenge of how to use color effectively in creating visualizations. The recent global adoption of data vis has helped address this challenge in some fields, but it remains open in the biological domain. The visualization of biological data deals with the application of computer graphics, scientific visualization, and information visualization in various areas of the life sciences. This paper describes 10 simple rules to colorize biological data visualization.
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📲Channel: @Bioinformatics
Abstract: Methods for visualization of biological data continue to improve, but there is still a fundamental challenge in colorization of these visualizations (vis). Visual representation of biological data should not overwhelm, obscure, or bias the findings, but rather make them more understandable. This is often due to the challenge of how to use color effectively in creating visualizations. The recent global adoption of data vis has helped address this challenge in some fields, but it remains open in the biological domain. The visualization of biological data deals with the application of computer graphics, scientific visualization, and information visualization in various areas of the life sciences. This paper describes 10 simple rules to colorize biological data visualization.
📎Study the paper
📲Channel: @Bioinformatics
📺 Building a Successful Bioinformatics Academic Career
Recorded webinar
🌐 Watch
📲Channel: @Bioinformatics
Recorded webinar
🌐 Watch
📲Channel: @Bioinformatics
YouTube
Copy of NBGN Webinar: Building a Successful Bioinformatics Academic Career
With Professor Babatunde Salako