📓 A Little Book of R For Bioinformatics
By: Dr Avril Coghlan - from Sanger Institute
💥This is a simple introduction to bioinformatics, with a focus on genome analysis, using the R statistics software. To encourage research into neglected tropical diseases such as leprosy, Chagas disease, trachoma, schistosomiasis etc., most of the examples in this booklet are for analysis of the genomes of the organisms that cause these diseases.
🖇 Read the book
📲Channel: @Bioinformatics
By: Dr Avril Coghlan - from Sanger Institute
💥This is a simple introduction to bioinformatics, with a focus on genome analysis, using the R statistics software. To encourage research into neglected tropical diseases such as leprosy, Chagas disease, trachoma, schistosomiasis etc., most of the examples in this booklet are for analysis of the genomes of the organisms that cause these diseases.
🖇 Read the book
📲Channel: @Bioinformatics
👍1
📑Machine learning in medicine: a practical introduction to natural language processing
💥The methods of this paper is structured into four parts, which in turn cover:
1. Basic NLP techniques for data cleaning in open-text datasets
2. Positive and negative sentiment analysis of drug reviews, with a freely-available lexicon
3. Unsupervised machine learning to identify similarities and differences between drugs, based on the words used to describe them
4. Supervised machine learning (classification) to predict whether a free text drug review will be associated with a dichotomised “Good” or “Bad” numerical score
We present samples of code written using the R Statistical Programming Language within the paper to illustrate the methods described, and provide the full script as a supplementary file. At points in the analysis, we deliberately simplify and shorten the dataset so that these analyses can be reproduced in reasonable time on a personal desktop or laptop, although this would clearly be suboptimal for original research studies.
While this paper is intended for readers who are relatively new to the field, some basic familiarity with the R programming language and machine learning concepts will make this manuscript easier to follow.
🖇Study the paper
📲Channel: @Bioinformatics
💥The methods of this paper is structured into four parts, which in turn cover:
1. Basic NLP techniques for data cleaning in open-text datasets
2. Positive and negative sentiment analysis of drug reviews, with a freely-available lexicon
3. Unsupervised machine learning to identify similarities and differences between drugs, based on the words used to describe them
4. Supervised machine learning (classification) to predict whether a free text drug review will be associated with a dichotomised “Good” or “Bad” numerical score
We present samples of code written using the R Statistical Programming Language within the paper to illustrate the methods described, and provide the full script as a supplementary file. At points in the analysis, we deliberately simplify and shorten the dataset so that these analyses can be reproduced in reasonable time on a personal desktop or laptop, although this would clearly be suboptimal for original research studies.
While this paper is intended for readers who are relatively new to the field, some basic familiarity with the R programming language and machine learning concepts will make this manuscript easier to follow.
🖇Study the paper
📲Channel: @Bioinformatics
BioMed Central
Machine learning in medicine: a practical introduction to natural language processing - BMC Medical Research Methodology
Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text…
📁All about NCBI
▫️What is NCBI?
▫️NCBI Resources and Tools?
▫️NCBI's How To Tasks
▫️Quick Guide to Entrez Databases
🌐 See the page
📲Channel: @Bioinformatics
▫️What is NCBI?
▫️NCBI Resources and Tools?
▫️NCBI's How To Tasks
▫️Quick Guide to Entrez Databases
🌐 See the page
📲Channel: @Bioinformatics
🎞 Lessons from the Pandemic for Machine Learning and Medical Imaging
💥Presented on behalf of Isaac Newton Institute for Mathematical Sciences
🌐 Watch
📲Channel: @Bioinformatics
💥Presented on behalf of Isaac Newton Institute for Mathematical Sciences
🌐 Watch
📲Channel: @Bioinformatics
📕🎬Online tutorial
⚡️Introductory bioinformatics
A curated set of EMBL-EBI online courses
This curated pathway brings together a number of online tutorials and recorded webinars to provide an introduction to bioinformatics, a brief tour of the resources available from EMBL-EBI and more details about some of those resources, including Ensembl, UniProt and Expression Atlas.
♦️What will you achieve?
By the end of the course you will be able to:
▫️Outline what bioinformatics is
▫️Describe the importance of data management
▫️Recall which resources are available from EMBL-EBI
▫️Know where to find information about genes
▫️View information on gene expression
▫️Search for protein information
▫️Know where to find out more about EMBL-EBI resources
🌐 Enter Course
📲Channel: @Bioinformatics
⚡️Introductory bioinformatics
A curated set of EMBL-EBI online courses
This curated pathway brings together a number of online tutorials and recorded webinars to provide an introduction to bioinformatics, a brief tour of the resources available from EMBL-EBI and more details about some of those resources, including Ensembl, UniProt and Expression Atlas.
♦️What will you achieve?
By the end of the course you will be able to:
▫️Outline what bioinformatics is
▫️Describe the importance of data management
▫️Recall which resources are available from EMBL-EBI
▫️Know where to find information about genes
▫️View information on gene expression
▫️Search for protein information
▫️Know where to find out more about EMBL-EBI resources
🌐 Enter Course
📲Channel: @Bioinformatics
📽 Analysis of Viral Sequencing Data
💥Recorded lecture from Computational Genomics Summer Institute: CGSI
🌐 Watch
📲Channel: @Bioinformatics
💥Recorded lecture from Computational Genomics Summer Institute: CGSI
🌐 Watch
📲Channel: @Bioinformatics
YouTube
Alex Zelikovsky | Analysis of Viral Sequencing Data | CGSI 2019
Speaker: Alex Zelikovsky
Talk: " Analysis of Viral Sequencing Data"
Location: Mong Auditorium, 7/18/19
Talk: " Analysis of Viral Sequencing Data"
Location: Mong Auditorium, 7/18/19
👨🏫 Introduction to R for Health Data Science
💥Free online course from University of Manchester
🌐 Enter the course
📲Channel: @Bioinformatics
💥Free online course from University of Manchester
🌐 Enter the course
📲Channel: @Bioinformatics
📕Reproducible Bioinformatics Research for Biologists
🔽 Download book chapter
📲Channel: @Bioinformatics
🔽 Download book chapter
📲Channel: @Bioinformatics
📑Machine learning in medicine: a practical introduction
💥We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.
🌐 Read the paper
📲Channel: @Bioinformatics
💥We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.
🌐 Read the paper
📲Channel: @Bioinformatics
🎬Free webinar
🧑💻Designing and Optimizing RNA-Seq Experiments
Learning Objectives:
▫️Know Available RNA sequencing technologies and their applications
▫️Optimizing library preparation depending on the sequencing platform
▫️How to obtain the proper sample quality required for each RNA sequencing technology
▫️The importance of replicate numbers to obtain reliable results
🗓 Date:
Monday, September 27, 2021
🕚 Time:
11:00 AM - 12:00 PM Eastern Time
✍️ Registration and more details
📲Channel: @Bioinformatics
🧑💻Designing and Optimizing RNA-Seq Experiments
Learning Objectives:
▫️Know Available RNA sequencing technologies and their applications
▫️Optimizing library preparation depending on the sequencing platform
▫️How to obtain the proper sample quality required for each RNA sequencing technology
▫️The importance of replicate numbers to obtain reliable results
🗓 Date:
Monday, September 27, 2021
🕚 Time:
11:00 AM - 12:00 PM Eastern Time
✍️ Registration and more details
📲Channel: @Bioinformatics
📖📺Genomics Boot Camp (Online Book + YouTube channel)
💥The Genomics Boot Camp is a resource that helps you to start your journey in practical analysis of genomic data, with a focus on SNP data. The chapters follow the same structure all the time: provide background information and practical insight to the topic, and when appropriate exercises to reinforce the obtained knowledge. The Genomics Boot Camp as a whole was designed to cater to various learning preferences with written text, video demonstrations, and the possibility of hands-on exercises. There is a certain overlap between the book and the YouTube channel contents, but each has unique pieces of information as well. So for the full experience, I suggest checking out both.
🎦 Movies
📓Online Tutorial
📲Channel: @Bioinformatics
💥The Genomics Boot Camp is a resource that helps you to start your journey in practical analysis of genomic data, with a focus on SNP data. The chapters follow the same structure all the time: provide background information and practical insight to the topic, and when appropriate exercises to reinforce the obtained knowledge. The Genomics Boot Camp as a whole was designed to cater to various learning preferences with written text, video demonstrations, and the possibility of hands-on exercises. There is a certain overlap between the book and the YouTube channel contents, but each has unique pieces of information as well. So for the full experience, I suggest checking out both.
🎦 Movies
📓Online Tutorial
📲Channel: @Bioinformatics
🔥1
📑Bioinformatics core competencies for undergraduate life sciences education
From Paper Abstract: Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year...
🌐 Read the paper
📲Channel: @Bioinformatics
From Paper Abstract: Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year...
🌐 Read the paper
📲Channel: @Bioinformatics
2021_Differential_Expression_Analysis_of_RNA_Seq_Data_and_Co_expression.pdf
9.8 MB
📑Practical Differential Expression Analysis of RNA-Seq Data and Co-expression Networks
📲Channel: @Bioinformatics
📲Channel: @Bioinformatics
🎞 📔 Pathways and Network Analysis 2021
👨🏻💻3 days full workshop details
🌐 Workshop details
📲Channel: @Bioinformatics
👨🏻💻3 days full workshop details
🌐 Workshop details
📲Channel: @Bioinformatics
👷Job opportunity
👨🏫Eligibility: Master or PhD degree in Bioinformatics, Computational Biology or similar area.
📑Contract type: Pemanent contract
🕦Work Hours: Full time - 40h/week
📍Location: Strassen (Luxembourg)
🗓 Start date: immediate
ℹ️ More info and apply
📲Channel: @Bioinformatics
👨🏫Eligibility: Master or PhD degree in Bioinformatics, Computational Biology or similar area.
📑Contract type: Pemanent contract
🕦Work Hours: Full time - 40h/week
📍Location: Strassen (Luxembourg)
🗓 Start date: immediate
ℹ️ More info and apply
📲Channel: @Bioinformatics
💵 25 awarded funds across 30 U.S. research sites
💥NIH providing $185 million for research to advance understanding of how human genome functions
ℹ️ More information
📲Channel: @Bioinformatics
💥NIH providing $185 million for research to advance understanding of how human genome functions
ℹ️ More information
📲Channel: @Bioinformatics
📹Introduction to Weighted Gene Co-expression Network Analysis (WGCNA)
💥recorded webinar
🎖 Goals:
▫️Introduction and motivation for co-expression network analysis
▫️Basics of weighted gene co-expression network analysis
▫️Step-by-step guide to WGCNA using the WGCNA package in R.
🌐 Watch
📲Channel: @Bioinformatics
💥recorded webinar
🎖 Goals:
▫️Introduction and motivation for co-expression network analysis
▫️Basics of weighted gene co-expression network analysis
▫️Step-by-step guide to WGCNA using the WGCNA package in R.
🌐 Watch
📲Channel: @Bioinformatics
YouTube
Webinar #7 – Introduction to Weighted Gene Co-expression Network Analysis
Goals of this webinar (molecular networks):
Introduction and motivation for co-expression network analysis
Basics of weighted gene co-expression network analysis
Step-by-step guide to WGCNA using the wgcna package in R.
Background reading available at: h…
Introduction and motivation for co-expression network analysis
Basics of weighted gene co-expression network analysis
Step-by-step guide to WGCNA using the wgcna package in R.
Background reading available at: h…