👨🏫 Registration is open for three week Genome Informatics Workshop
✍️ Registration Link
https://decodelife.org
💲 Fees: Rupees 1000 For Indian Participant. / Dollar 20 (USA) for Foreign Participant.
We have kept nominal fees in order to ensure that only serious candidates participate.
💥Key Features :
▫️E- Certificate of participation
▫️Global Instructors
▫️Videos access for all sessions
📲Channel: @Bioinformatics
✍️ Registration Link
https://decodelife.org
💲 Fees: Rupees 1000 For Indian Participant. / Dollar 20 (USA) for Foreign Participant.
We have kept nominal fees in order to ensure that only serious candidates participate.
💥Key Features :
▫️E- Certificate of participation
▫️Global Instructors
▫️Videos access for all sessions
📲Channel: @Bioinformatics
👨🏫 Free online Training course in single-cell spring school
🗓 Time: One week course starts on 4.04.2021
👄 Language: English
✍️ Registration (deadline: 31.03.2021):
https://forms.gle/H3zXb1ofaSvZ3bKg8
ℹ️More information:
https://genomics.org.ua/2021/03/training-course-in-single-cell-biology/
📲Channel: @Bioinformatics
🗓 Time: One week course starts on 4.04.2021
👄 Language: English
✍️ Registration (deadline: 31.03.2021):
https://forms.gle/H3zXb1ofaSvZ3bKg8
ℹ️More information:
https://genomics.org.ua/2021/03/training-course-in-single-cell-biology/
📲Channel: @Bioinformatics
💾 Using Big data (large, complex datasets) for diabetes research
https://www.healio.com/news/endocrinology/20210321/niddk-big-data-programs-help-to-enhance-share-new-research-in-diabetes
📲Channel: @Bioinformatics
https://www.healio.com/news/endocrinology/20210321/niddk-big-data-programs-help-to-enhance-share-new-research-in-diabetes
📲Channel: @Bioinformatics
Healio News
NIDDK big data programs help to enhance, share new research in diabetes
New digital tools and technologies are being used to parse through big data and allow researchers to study diabetes in unprecedented ways, according to a speaker at the ENDO annual meeting. Griffin P. Rodgers, MD, MACP, director of the National Institute…
📎 New Review Paper:
Incorporating Machine Learning into Established Bioinformatics Frameworks
https://www.mdpi.com/1422-0067/22/6/2903/htm
Here, authors review recently developed methods that incorporate machine learning with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. They outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges
📲Channel: @Bioinformatics
Incorporating Machine Learning into Established Bioinformatics Frameworks
https://www.mdpi.com/1422-0067/22/6/2903/htm
Here, authors review recently developed methods that incorporate machine learning with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. They outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges
📲Channel: @Bioinformatics
👨🏫 Spring 2021 MIT course on
Computational Systems Biology: Deep Learning in Life Science
🎞 With lecture videos, slides and other course materials
https://mit6874.github.io/
This courses introduces foundations and state-of-the-art machine learning challenges in genomics and the life sciences more broadly. It introduces both deep learning and classical machine learning approaches to key problems, comparing and contrasting their power and limitations. It seeks to enable students to evaluate a wide variety of solutions to key problems we face in this rapidly developing field, and to execute on new enabling solutions that can have large impact. Students will program using Python 3 and TensorFlow 2 in Jupyter Notebooks, a nod to the importance of carefully documenting their work so it can be precisely reproduced by others.
📲Channel: @Bioinformatics
Computational Systems Biology: Deep Learning in Life Science
🎞 With lecture videos, slides and other course materials
https://mit6874.github.io/
This courses introduces foundations and state-of-the-art machine learning challenges in genomics and the life sciences more broadly. It introduces both deep learning and classical machine learning approaches to key problems, comparing and contrasting their power and limitations. It seeks to enable students to evaluate a wide variety of solutions to key problems we face in this rapidly developing field, and to execute on new enabling solutions that can have large impact. Students will program using Python 3 and TensorFlow 2 in Jupyter Notebooks, a nod to the importance of carefully documenting their work so it can be precisely reproduced by others.
📲Channel: @Bioinformatics
❤1👍1
💣 Genomics and Bioweapons
https://www.military.com/daily-news/opinions/2021/03/23/will-genomics-become-next-arena-of-china-us-military-competition.html
📲Channel: @Bioinformatics
https://www.military.com/daily-news/opinions/2021/03/23/will-genomics-become-next-arena-of-china-us-military-competition.html
📲Channel: @Bioinformatics
Military.com
Will Genomics Become the Next Arena of China-US Military Competition?
Genomics offers benefits to medical science and human health, but its weaponization could make it another arena of Sino-American competition.
🧠 'Zombie' genes? Research shows some genes come to life in the brain after death
In the hours after we die, certain cells in the human brain are still active. Some cells even increase their activity and grow to gargantuan proportions, according to new research from the University of Illinois Chicago.
In a newly published study in the journal Scientific Reports, the UIC researchers analyzed gene expression in fresh brain tissue — which was collected during routine brain surgery — at multiple times after removal to simulate the post-mortem interval and death. They found that gene expression in some cells actually increased after death.
Study the paper here:
DOI: 10.1038/s41598-021-85801-6
📲Channel: @Bioinformatics
In the hours after we die, certain cells in the human brain are still active. Some cells even increase their activity and grow to gargantuan proportions, according to new research from the University of Illinois Chicago.
In a newly published study in the journal Scientific Reports, the UIC researchers analyzed gene expression in fresh brain tissue — which was collected during routine brain surgery — at multiple times after removal to simulate the post-mortem interval and death. They found that gene expression in some cells actually increased after death.
Study the paper here:
DOI: 10.1038/s41598-021-85801-6
📲Channel: @Bioinformatics
❓How to Become a Bioinformatician
We’ve got the lowdown on the training you’ll need to pursue this career path, and a handy list of resources to get you started on your learning.
✍️ Level: Elementary
https://bitesizebio.com/38236/how-to-become-a-bioinformatician/
📲Channel: @Bioinformatics
We’ve got the lowdown on the training you’ll need to pursue this career path, and a handy list of resources to get you started on your learning.
✍️ Level: Elementary
https://bitesizebio.com/38236/how-to-become-a-bioinformatician/
📲Channel: @Bioinformatics
Bitesize Bio
8 Must-have Skills for Budding Bioinformaticians
Interested in a career in bioinformatics? Read on for some important career information on how to become a bioinformatician and the skills you'll need to prepare for this career.
👩🎓Postdoctoral and PhD Positions in Medical Bioinformatics
Saez-Rodriguez group – Heidelberg University
💥Position Summary
Postdoctoral and PhD positions are open in the group of Julio Saez-Rodriguez at Heidelberg University. The positions are in the context of various national and international collaborations to study multi-omics data sets, including single-cell data, to develop and apply computational methods to better understand and treat cancer and kidney disease. This work builds on recent and ongoing work in our group, and you will join an international and interdisciplinary group of scientists.
Candidates interested in using bioinformatics, machine learning, and mathematical modeling to analyze big data to advance personalized medicine are encouraged to apply. You are expected to hold a degree in statistics, mathematics, physics, engineering or computer science, or a degree in biological science with substantial experience in computational and statistical work.
Candidates should email their CV (including names of three references) and a letter of interest to jobs.saez@bioquant.uni-heidelberg.de. The letter of interest has to be tailored to our group, mentioning projects or articles of our group that you find interesting, and explaining how you would fit here and in the topic mentioned above. Please also provide a pointer to a code repository if possible.
There is no strict deadline, but priority will be given to applications by April 8th 2020. The starting date is fairly flexible within 2021.
🕸 for more information visit
www.saezlab.org
📲Channel: @Bioinformatics
Saez-Rodriguez group – Heidelberg University
💥Position Summary
Postdoctoral and PhD positions are open in the group of Julio Saez-Rodriguez at Heidelberg University. The positions are in the context of various national and international collaborations to study multi-omics data sets, including single-cell data, to develop and apply computational methods to better understand and treat cancer and kidney disease. This work builds on recent and ongoing work in our group, and you will join an international and interdisciplinary group of scientists.
Candidates interested in using bioinformatics, machine learning, and mathematical modeling to analyze big data to advance personalized medicine are encouraged to apply. You are expected to hold a degree in statistics, mathematics, physics, engineering or computer science, or a degree in biological science with substantial experience in computational and statistical work.
Candidates should email their CV (including names of three references) and a letter of interest to jobs.saez@bioquant.uni-heidelberg.de. The letter of interest has to be tailored to our group, mentioning projects or articles of our group that you find interesting, and explaining how you would fit here and in the topic mentioned above. Please also provide a pointer to a code repository if possible.
There is no strict deadline, but priority will be given to applications by April 8th 2020. The starting date is fairly flexible within 2021.
🕸 for more information visit
www.saezlab.org
📲Channel: @Bioinformatics
👨🏫 Biology and Data Science - Bioinformatics in action
Free webinar from Helsinki center for data science
🗓 Tuesday 30.3.2021, 9:00–11:00
✍️ Registration:
https://www.lyyti.in/hidata_bioinformatics
ℹ️Webinar program and more about the speakers:
https://www2.helsinki.fi/en/news/data-science-news/hidata-webinar-on-bioinformatics
📲Channel: @Bioinformatics
Free webinar from Helsinki center for data science
🗓 Tuesday 30.3.2021, 9:00–11:00
✍️ Registration:
https://www.lyyti.in/hidata_bioinformatics
ℹ️Webinar program and more about the speakers:
https://www2.helsinki.fi/en/news/data-science-news/hidata-webinar-on-bioinformatics
📲Channel: @Bioinformatics
📘 Free Ebook: Introduction to Biomedical Data Science
This ebook introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, it introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages.
⬇️ Download from here:
https://github.com/bioconnector/bims8382/raw/gh-pages/textbook.pdf
📲Channel: @Bioinformatics
This ebook introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, it introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages.
⬇️ Download from here:
https://github.com/bioconnector/bims8382/raw/gh-pages/textbook.pdf
📲Channel: @Bioinformatics
📚 Selected books/urls for bioinformatics/data science curriculum
From tommy weblog, A computational biologist working on (epi)genomics, single-cell transcriptomics
http://crazyhottommy.blogspot.com/2019/09/my-opinionated-selection-of-booksurls.html
📲Channel: @Bioinformatics
From tommy weblog, A computational biologist working on (epi)genomics, single-cell transcriptomics
http://crazyhottommy.blogspot.com/2019/09/my-opinionated-selection-of-booksurls.html
📲Channel: @Bioinformatics
🦴Machine learning Solutions for Osteoporosis – a Review
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis.
✍️Study the paper here:
https://asbmr.onlinelibrary.wiley.com/doi/abs/10.1002/jbmr.4292
📲Channel: @Bioinformatics
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis.
✍️Study the paper here:
https://asbmr.onlinelibrary.wiley.com/doi/abs/10.1002/jbmr.4292
📲Channel: @Bioinformatics
🌱 Gene transfer between plant and insect
https://www.nature.com/articles/d41586-021-00782-w
📲Channel: @Bioinformatics
https://www.nature.com/articles/d41586-021-00782-w
📲Channel: @Bioinformatics
Nature
First known gene transfer from plant to insect identified
Nature - Discovery that a whitefly uses a stolen plant gene to elude its host’s defences may offer a route to new pest-control strategies.
👨🏫Introduction to Bioinformatics and Computational Biology
Free course videos from Harvard University
Spring 2021
📑https://liulab-dfci.github.io/bioinfo-combio/
Contributors
♦️Xiaole Shirley Liu - Harvard University and Dana-Farber Cancer Institute
♦️Joshua Starmer - StatQuest
♦️Martin Hemberg - Sanger Institute
♦️Ting Wang - Washington University
♦️Feng Yue - Northwestern University
♦️Gad Getz - Harvard University and Broad Institute
📲Channel: @Bioinformatics
Free course videos from Harvard University
Spring 2021
📑https://liulab-dfci.github.io/bioinfo-combio/
Contributors
♦️Xiaole Shirley Liu - Harvard University and Dana-Farber Cancer Institute
♦️Joshua Starmer - StatQuest
♦️Martin Hemberg - Sanger Institute
♦️Ting Wang - Washington University
♦️Feng Yue - Northwestern University
♦️Gad Getz - Harvard University and Broad Institute
📲Channel: @Bioinformatics
✅ Ten Quick Tips for Deep Learning in Biology
https://benjamin-lee.github.io/deep-rules/manuscript.pdf
📲Channel: @Bioinformatics
https://benjamin-lee.github.io/deep-rules/manuscript.pdf
📲Channel: @Bioinformatics
📎 Applying NLP algorithms to the study of proteins
🗒New open access paper:
https://www.sciencedirect.com/science/article/pii/S2001037021000945
💥From research abstract:
Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods. We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning. We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research.
📲Channel: @Bioinformatics
🗒New open access paper:
https://www.sciencedirect.com/science/article/pii/S2001037021000945
💥From research abstract:
Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods. We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning. We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research.
📲Channel: @Bioinformatics
📕 Computational Genomics with R
2020-09-30 Online book from Altuna Akalin
https://compgenomr.github.io/book/
📲Channel: @Bioinformatics
2020-09-30 Online book from Altuna Akalin
https://compgenomr.github.io/book/
📲Channel: @Bioinformatics
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👨🏫 A guide to exploring genes and genomes with Ensembl
Free webinar from EMBL-EBI
This webinar is suitable to any researcher in life sciences who is interested in studying genes and genomes. No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.
🗓 Apr 14, 2021 03:30 PM in London
✍️ Register here
ℹ️ More information:
https://www.ebi.ac.uk/training/events/guide-exploring-genes-and-genomes-ensembl/#vf-tabs__section--tab1
📲Channel: @Bioinformatics
Free webinar from EMBL-EBI
This webinar is suitable to any researcher in life sciences who is interested in studying genes and genomes. No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.
🗓 Apr 14, 2021 03:30 PM in London
✍️ Register here
ℹ️ More information:
https://www.ebi.ac.uk/training/events/guide-exploring-genes-and-genomes-ensembl/#vf-tabs__section--tab1
📲Channel: @Bioinformatics