👨🏫 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.
🌐 Study the paper
📲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.
🌐 Study the paper
📲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
🌐 Study the paper
📲Channel: @Bioinformatics
🌐 Study the paper
📲Channel: @Bioinformatics
📑The Incredible Convergence Of Deep Learning And Genomics
🌐Study the article
📲Channel: @Bioinformatics
🌐Study the article
📲Channel: @Bioinformatics
👨🏻💻Accurate predictions of the structures of almost all human proteins
from Nature News
🌐 Read the article
📲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
👍1
🧑🎨 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.
📎Study the paper
📲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
🏢 VIII Bioinformatics Student Symposium
▫️ on behalf of ISCB Regional Student Group in Spain España
📍Online
🗓 Date: October 18-19, 2021
💣 Registration deadline: September 17, 2021
🎓 WHO?
Undergrads, MSc and Ph.D. students, Postdocs
🖇Website & more info.:
https://www.rsg-spain.iscbsc.org/viii-symposium/
📲Channel: @Bioinformatics
▫️ on behalf of ISCB Regional Student Group in Spain España
📍Online
🗓 Date: October 18-19, 2021
💣 Registration deadline: September 17, 2021
🎓 WHO?
Undergrads, MSc and Ph.D. students, Postdocs
🖇Website & more info.:
https://www.rsg-spain.iscbsc.org/viii-symposium/
📲Channel: @Bioinformatics
📓 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…