Omics Academy

http://www.decarvalholab.org/vacancies.html Postdoctoral Research Fellow: Computational Epigenetics The Ontario Cancer Institute (OCI) is a world leader in several cancer-related research fields, consistently publishing high impact discoveries. Its research activities are concentrated on epigenomics, cancer immunotherapy, cancer stem cells, image-guided therapeutics, patient survivorship, and personalized medicine. A bioinformatics Postdoctoral Fellow position is available immediately within our group. We offer an interdisciplinary research environment with exciting challenges on novel biological data and leading-edge expertise in one of today's most dynamic scientific disciplines. We are looking for people that are highly qualified and motivated to join our interdisciplinary team. Required Qualifications: • Recent PhD in Bioinformatics or related areas; • At least one first author paper in an international peer reviewed journal; • Experience in analyzing high throughpu

I came across this question several times. In my real life, I also encountered this question many times. Many time, my friend asked me: “Hey, can I learn Bioinformatics with your? Can you give me some materials? ” At the beginning, I would say: “Sure. Let me know if you need any help.” Then mostly nothing happened after I sent them the web links or e-books. Now if someone ask me the same question, I usually will say: “Are you sure? If you take it seriously, I’ll teach you.” My point here is take it seriously. What is Bioinformatics? Bioinformatics is the science of collection and analyzing complex biological data such as genetics codes. This is the definition given by the first result in Google search. Maybe it’s too abstract to understand. Based on my understanding, I want to talk about Bioinformatics in the following aspects: Software development Maybe the most popular tool is blast . When I was in college, I selected a course called “生物信息学”. The english name is “Bioinformati

谢谢你的经验推荐啊，flareon！ 能不能介绍一下： 1, machine learning和数统知识你是不是已经有了很强的背景了？ 2, data structure你是通过上什么课，或者做什么project来提高的？ 3, 在kaggle上有什么project推荐一下多看看？ 1. 我不强，但我智商够用也在努力。把你用在生物的1/10的精力放在cs上效果就很不同 我就只是自学了bishop的PRML，强迫自己学习抽象的数学； 你说的statistical learning更好，更亲民，PRML有时候就像在装B，不过实在高端 我觉得数学和CS不同在于CS skill某些可以短期获得，但数学统计需要长期理解，我因 为过去搞过生物信息，所以 对于很多ML的东西我能从生物角度具体化帮助我理解，比如bayesian，EM，比如： http://www.nature.com/nbt/journal/v26/n8/full/nbt1406.html 这些东西放在code里都是现成的package，两行代码，顶多调参。但你要成为优秀DS， 或者励志吃这一碗饭，就必须学好。 ML过程中你会被迫补上multivariat calculus和linear algebra 数学，是一种素质 2. Data structure，推荐一个不错的python interactive： http://interactivepython.org/runestone/static/pythonds/Introduction/GettingStartedwithData.html 你多做点project，就会遇到pandas,numpy,自然要和string, list, dictionary, tuple, df, series, stack, queue 这些打交道；自然就会了。 当然我现在从找工作的角度看，去coursera混点certificate放到简历上有必要，如果 你没有cs degree 同时，course可以全面学习概念常识 最好的是Princeton algorithm但那个不给certificate，还是用java 3. kaggle最入门的就是titanic 推荐一个我喜欢的： https://www.k

K-mer analysis and genome size estimation K-mer analysis and genome size estimate: http://koke.asrc.kanazawa-u.ac.jp/HOWTO/kmer-genomesize.html Genome Size Estimation Tutorial http://bioinformatics.uconn.edu/genome-size-estimation-tutorial/