A lot of my ideas about Machine Learning come from Quantum Mechanical Perturbation Theory. To provide some context, we need to step back and understand that the familiar techniques of Machine Lear…
Category: Computational Genomics
Fancy a challenge? A DREAM of intra-tumour phylogenies
a big chance for dry labs
You are into tumor evolution? And got a fancy model? Want to battle with the best?
Then check out the ICGC-TCGA DREAM Somatic Mutation Calling – Tumour Heterogeneity Challenge (SMC-Het).
These are the days of Big Science, my friend. You can’t just have a short name …
View original post 286 more words
Inferring tumour evolution 2 – Comparison to classical phylogenetics
Quick recap: Last time we talked about tumor evolution and I presented a toy example to introduce key concepts. I also introduced the intra-tumor phylogeny problem: Given a sample of the genomes of clones in a tumour, reconstruct its `life history’. This problem consists of two sub-problems: (1)identification of clones, and (2) inferring evolutionary relationships between clones.
This problem falls into the general area of reconstructing phylogenetic trees — so how does inferring clonal trees compare to classical phylogenetic methods?
View original post 777 more words
Inferring tumour evolution 1 – The intra-tumour phylogeny problem
“Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer’s life history is encrypted in the somatic mutations present,”
write Nik-Zainal et al in the abstract of their 2012 Cell paper `The life history of 21 breast cancers‘. The key figure of their paper shows a phylogenetic tree of tumor development in a patient. The paper contains lots of computational work on analyzing and interpreting mutations based on deep-sequencing data, but –a big surprised but— the very last step of putting together the tree was done manually. Half the paper is describing the reasoning that Peter Campbell and his group used to condense all the evidence they had gathered from genomic data into the tree – but there is no algorithm.
View original post 951 more words
A new R package for network-based biomarker discovery released
A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:
- AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
- PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
- hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
- frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
- stSVM (network smoothed t-statistic) , Cun et al., PloS One,.
NetClass can be download from souceforg ( http://sourceforge.net/projects/netclassr/) or , CRAN (http://cran.r-project.org/web/packages/netClass/ ). For more detail of netClass, you can refer these four papers:
- Yupeng Cun, Holger Fröhlich (2014) netClass: An R-package for network based, integrative biomarker signature discovery. Bioinformatics; doi: 10.1093/bioinformatics/btu025
- Yupeng Cun, Holger Fröhlich (2013) Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics, PLoS ONE 8(9): e73074. doi:10.1371/journal.pone.0073074.
- Yupeng Cun, Holger Fröhlich (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure. arXiv:1212.3214 ( Link for R codes and Supplemental meterials)
- Yupeng Cun, Holger Fröhlich (2012) Prognostic Gene Signatures for Patient Stratification in Breast Cancer – Accuracy, Stability and Interpretability of Gene Selection Approaches Using Prior Knowledge on Protein-Protein Interactions. BMC Bioinformatics, 13:69
“Translational Bioinformatics” collection for PLOS cBio
A review collection in current approach in Translational Bioinformatics.
‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.
Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.
The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.
Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.
Download the full Translational Bioinformatics collection here: PDF
Collection URL: www.ploscollections.org/translationalbioinformatics
Introduction to Translational Bioinformatics Collection
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796
Chapter 1: Biomedical Knowledge Integration
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826
Chapter 2: Data-Driven View of Disease Biology
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816
Chapter 3: Small Molecules and Disease
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805
Chapter 4: Protein Interactions and Disease
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819
Chapter 5: Network Biology Approach to Complex Diseases
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820
Chapter 6: Structural Variation and Medical Genomics
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821
Chapter 7: Pharmacogenomics
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858
Chapter 9: Analyses Using Disease Ontologies
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827
Chapter 10: Mining Genome-Wide Genetic Markers
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828
Chapter 11: Genome-Wide Association Studies
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822
Chapter 12: Human Microbiome Analysis
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808
Chapter 13: Mining Electronic Health Records in the Genomics Era
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823
Chapter 14: Cancer Genome Analysis
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824
Use prior information to prognostic biomaker or not?
In our recent publication in BMC bioinformatics, we acompared a great deal of feature selection methods to finding prognostic biomakers in 6 breast cancer gene expresion data. No methods show significant performacne in prediction accuracy, feature selection stability and biogical interprety, which against previeous reseach results: current network-based appraoch did not show much benift in our analysis. Meanwhile, A group from NKI also show the simliar results in PloS One. The R codes for these algorithms in our paper is availiable as request.
Continue reading “Use prior information to prognostic biomaker or not?”
Welcome back! The last post discussed rules 1-3: the importance to do a postdoc, a concise CV and a unique research statement. Like the last post this one is inspired by a Career Development Workshop at ISMB 2012 that I contributed to (download the slides).
There is still one thing missing from a standard application pack:
4. Pretend you care! The teaching statement
Together with CV and research statement some places ask you to submit a teaching statement. So write one. But don’t be fooled, it’s pretty low on the priority list (for the hiring committee, even if maybe not for you). Academic employers want three things from you: money, papers, and … long time nothing … teaching. I’m not saying they won’t ask you to teach for many hours a week, but when it comes to you being evaluated its money and papers (in that order) which…
View original post 951 more words
Starting your own group is one of the most important steps in your scientific career — and one of the hardest.
Being invited to a Career Development Workshop at ISMB 2012 made me write down some of the advice that I had got when I was on the jobmarket a few years ago (and even put some of it on slides).
In a diverse and interdisciplinary field like computational biology it is very quite hard to come up with general rules that fit everyone. This is why I went down the self-indulgent route and revisited the CV and research statement I had prepared 4 years ago. (You’ll find a copy in the slides.) Some things are Ok, some things I would improve now — you will see, I’ll comment on this later. Let’s start with the basics:
View original post 1,266 more words
comutational courese in Plos Computational Biology
Short introduction paper in different ares in computational biology.
Fran Lewitter, Welcome to PLoS Computational Biology “Education”
Kenzie D MacIsaac, Ernest Fraenkel, Practical Strategies for Discovering Regulatory DNA Sequence Motifs, April 2006
Duncan Brown, Kimmen Sjölander, Functional Classification Using Phylogenomic Inference,June 2006
Philip E Bourne, Johanna McEntyre, Biocurators: Contributors to the World of Science,October 2006
Yuan Qi, Hui Ge, Modularity and Dynamics of Cellular Networks, December 2006