Publications by authors named "Calvin W Y Chan"

2 Publications

  • Page 1 of 1

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

Cell 2020 03 20;180(5):915-927.e16. Epub 2020 Feb 20.

Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06511, USA. Electronic address:

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.
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http://dx.doi.org/10.1016/j.cell.2020.01.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7210002PMC
March 2020

Wavelet frequency-temporal relative phase pattern analysis for intermuscular synchronization of dynamic surface EMG signals.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:5032-5

Electrical and Computer Engineering Department, Queen’s University, Kingston, Ontario K7L 3N6, Canada.

Cross-correlation is often used as the primary technique to compare two biological signals. Cross-correlation is an effective means to measure the synchronization of two signals assuming the relative phases of all frequencies are distributed linearly, that is, a group delay. The group delay assumption imposes an unfavorable restriction on signals with varying relative phase correlation at different frequencies. The traditional Fourier technique provides phase information for each frequency component, but it is not suitable for biological signals with non-stationary statistics. The application of a wavelet based phase analysis technique is discussed in this study. The frequency decomposition and temporally localized nature of the wavelet transform provides localized phase-frequency information for two signals. The merits and weaknesses of using the wavelet relative phase pattern for determining the synchronization of surface electromyographic signals from two muscle sites is discussed.
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http://dx.doi.org/10.1109/IEMBS.2011.6091220DOI Listing
June 2012