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Thread | Thread Starter | Forum | Replies | Last Post |
[NGS - analysis of gene expression data] Machine Learning + RNAseq data | Chuckytah | Bioinformatics | 7 | 03-05-2012 04:16 AM |
PhD position in machine learning and oncoviral genomics at CBS, Denmark | tsp | Academic/Non-Profit Jobs | 0 | 12-10-2011 01:53 AM |
Stanford open bioinformatics course | delinquentme | Bioinformatics | 0 | 08-16-2011 04:43 AM |
Research Technician at Stanford University | seqer | Academic/Non-Profit Jobs | 1 | 09-23-2010 10:33 AM |
ChIP-Seq: Application of machine learning methods to histone methylation ChIP-Seq dat | Newsbot! | Literature Watch | 0 | 07-27-2010 03:00 AM |
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#1 |
Member
Location: pittsburgh Join Date: Jul 2011
Posts: 12
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2 questions:
1) Anyone on this forum also taking the ml-class offered? 2) Specific examples of machine learning used in bioinformatics So I'm about half way through the class and I started with the specific intent of applying this to bioinformatics ... And while I understand what I'm working on ... I'd really like more practice! linear regression logistic regression neural networks support vector machines |
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#2 |
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Location: Edinburgh Join Date: Mar 2010
Posts: 16
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Shameless self promotion. Paper describing application of SVMs for the prediction of putative vaccine candidates from bacterial genome sequence.
Paul |
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#3 |
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Location: pittsburgh Join Date: Jul 2011
Posts: 12
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I'm ok with shameless!
sounds .... amazingly spot on... ! if( published? ) " where can I find one? " else " WHEN!? " end and I've got a class full of smart and optimistic kids looking to learn some things ... do you have any leads .. or like annoying pet projects you'd like to talk about? |
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#4 |
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Location: Edinburgh Join Date: Mar 2010
Posts: 16
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#5 |
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Location: Dublin Join Date: Mar 2010
Posts: 19
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Hi,
I am following the ml class. The techniques taught in the class are very useful in biology. Actually until I took the class, I didnot realize that the techniques used in microarray data analysis (or RNA-seq), for example, cluster analysis, PCA, clustering, are machine learning techniques. Plus, linear regression is used a lot in modeling gene expression and gene set analysis, e.g. limma, GSEAlm. Moreover, Octave, the programming environment used in the course (or a free version of Matlab), is also widely used in bioinformatics. You won't regret taking the course. Cheers, Jun |
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#6 |
Member
Location: pittsburgh Join Date: Jul 2011
Posts: 12
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Paul:
http://www.sciencedirect.com/science...64410X11012990 ^ Im posting this guy to reddit ![]() Jun: Might I ask how you came upon that fact? Where can I read more about it? |
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#7 | |
Member
Location: Dublin Join Date: Mar 2010
Posts: 19
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Multivariate analysis package for microarray (clustering, PCA, COA ...) Culhane AC, Thioulouse J, Perriere G, Higgins DG (2005) MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics 21:2789-2790. Linear model in microarray DE gene Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3. Linear model in gene set analysis Oron AP, Jiang Z, Gentleman R (2008) Gene set enrichment analysis using linear models and diagnostics. Bioinformatics 24:2586-2591. |
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