In preparation of my upcoming Preliminaries and Written Area Exams, I need to will myself to read as much as I can. The task, however, is long, arduous and pretty boring.
So to motivate myself to overcome this hurdle, I will incorporate something that I love and something that I’m not in love with.
Every day (Hopefully) over the next 60 days, I will critically read 2 academic papers and each day as a reward for doing so, I will watch 2 episodes of LOST (My favorite Netflix show).
This page will be use to annotate my progress, to keep me honest (Updated 05/23/2016 16:10):
| # |
Paper Citation |
Episode |
| 1 |
Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. M. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15, 162 (2014). |
LOST Episode #7 : The Moth |
| 2 |
Antharam, V. C. et al. An Integrated Metabolomic and Microbiome Analysis Identified Specific Gut Microbiota Associated with Fecal Cholesterol and Coprostanol in Clostridium difficile Infection. PLOS ONE 11, e0148824 (2016). |
LOST Episode #8: Confidence Man |
| 3 |
Tandon, D., Haque, M. M. & Mande, S. S. Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques. PLOS ONE 11, e0154493 (2016). |
LOST Episode #9: Solitary |
| 4 |
Breban, R., Vardavas, R. & Blower, S. Theory versus Data: How to Calculate R 0 ? PLOS ONE 2, e282 (2007). |
LOST Episode #10: Raised By Another |
| 5 |
Naulaerts, S. et al. A primer to frequent itemset mining for bioinformatics. Brief Bioinform 16, 216–231 (2015). |
LOST Episode #11: All The Best Cowboys Have Daddy Issues |
| 6 |
Cole, S. R., Chu, H. & Greenland, S. Maximum Likelihood, Profile Likelihood, and Penalized Likelihood: A Primer. Am. J. Epidemiol. 179, 252–260 (2014). (This paper is pretty dense, and was hard to follow.) |
LOST Episode #12: Whatever The Case May Be |
| 7 |
Shiokawa, Y., Misawa, T., Date, Y. & Kikuchi, J. Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements. Anal. Chem. 88, 2714–2719 (2016). |
LOST Episode #13: Hearts and Minds |
| 8 |
McMurdie, P. J. & Holmes, S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput Biol 10, e1003531 (2014). |
LOST Episode #14: Special |
| 9 |
Suarez-Alvarez, B., Rodriguez, R. M., Fraga, M. F. & López-Larrea, C. DNA methylation: a promising landscape for immune system-related diseases. Trends in Genetics 28, 506–514 (2012). |
LOST Episode #15: Homecoming |
| 10 |
Yang, T., Owen, J. L., Lightfoot, Y. L., Kladde, M. P. & Mohamadzadeh, M. Microbiota impact on the epigenetic regulation of colorectal cancer. Trends Mol Med 19, 714–725 (2013). |
LOST Episode #16: Outlaws |
| 11 |
Höfler, M. The Bradford Hill considerations on causality: a counterfactual perspective. Emerging Themes in Epidemiology 2, 11 (2005). |
LOST Episode #17: …In Translation |
| 12 |
Hill, A. B. The Environment and Disease: Association or Causation? Proc R Soc Med 58, 295–300 (1965). |
LOST Episode #18: Numbers |
| 13 |
Günther, O. P. et al. Novel Multivariate Methods for Integration of Genomics and Proteomics Data: Applications in a Kidney Transplant Rejection Study. OMICS: A Journal of Integrative Biology 18, 682–695 (2014). |
LOST Episode #19: Deus Ex Machina |
| 14 |
Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform bbv108 (2016). |
LOST Episode #20: Do No Harm |
| 15 |
Del Chierico, F. et al. Gut microbiota profiling of pediatric NAFLD and obese patients unveiled by an integrated meta-omics based approach. Hepatology n/a-n/a (2016). |
LOST Episode #21: The Greater Good |
| 16 |
Du, P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010). |
LOST Episode #22: Born To Run |
