🥛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.
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📲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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📑The Incredible Convergence Of Deep Learning And Genomics
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👨🏻💻Accurate predictions of the structures of almost all human proteins
from Nature News
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📲Channel: @Bioinformatics
from Nature News
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📖Free online tutorial
💥Running molecular dynamics simulations using GROMACS
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📲Channel: @Bioinformatics
💥Running molecular dynamics simulations using GROMACS
🌐 Study
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📘 The Biologist’s Guide to Computing
💥Free online book
This book introduces practical computing concepts to biologists without needing prior knowledge
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📲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.
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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.
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📲Channel: @Bioinformatics
📺 Building a Successful Bioinformatics Academic Career
Recorded webinar
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📲Channel: @Bioinformatics
Recorded webinar
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📲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.
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📲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 noscript 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 manunoscript easier to follow.
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📲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 noscript 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 manunoscript 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
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▫️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
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💥Presented on behalf of Isaac Newton Institute for Mathematical Sciences
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📲Channel: @Bioinformatics