Computational Bio/Bioinformatics Phd without calc/CS background - profile evaluation

[y123]

International student who lacks math/CS coursework looking at PhD bioinformatics programs. I have too many top tier programs in my list, and am looking for "safe schools" to add. However, given where I'm coming from, are any schools really "safe"? Also, any suggestions as to how I should proceed with my non-math/CS background are welcome.

 

Thanks for the help!

 

Undergrad Institution: International university, fairly average
Major(s): Life sciences, biochemistry
GPA: 3.5

Masters Institution: Top 5 private school, USA
Major(s): Molecular biology
GPA: 3.88

Type of Student: International Male

GRE Scores:
Q: 96%
V: 90%
W: 98%

Research Experience: 

2 summers of research during undergrad (~ 6 months total) at institutes equivalent to the NIH. in vitro and ex vivo studies (clinical research)

2 year project during Masters (preclinical drug discovery). 1 publication (average journal, impact factor ~4.5) and 1 more on the way (top tier journal). in vitro and in vivo studies

7 months (to date) after graduating (preclinical drug discovery and clinical research). in vitro, ex vivo, in vivo studies. additional experience in bioinformatics. 1 publication on the way

Programming languages: R (intermediate), Python(novice)
Any Other Info That Shows Up On Your App and Might Matter: Multiple leadership roles

Weaknesses: While I do not have formal coursework in math (linear algebra, calc, real analysis) or computer science, I have taken statistics classes in college. My undergrad institution did not allow for that flexibility and I could not take these classes during my masters. Learned those subjects (linear algebra, calc, r, python) through coursera/edX.

Recommendations: 1-2 extremely personalized recommendations, third should be average

Essay: Working on how to tie my background to my interests

Areas of interest: Bioinformatics/genomics/computational biology (Ideally biostat, but thats out of reach given my non math background in my opinion)

PhD programs:

1. Washington University in St. Louis - Human and statistical genetics

2. Johns Hopkins - Human genetics, Biomedical engineering, Pathobiology

3. Boston University - Bioinformatics

4. UNC Chapel Hill - BSSP

5. Tri-institutional program (Cornell, Sloan Kettering, Rockefeller) - Computational Biology

6. Columbia - Biomedical informatics + PIBS umbrella program

7. University of Pennsylvania - GCB

8. Emory - Biostatistics

9. University of Washington - Genome Sciences

10. NYU - Systems and computational biomedicine

11. CMU-Pitt - Computational biology

12. Harvard - BBS

[BeakerBreaker]

The challenge for your application is that the schools will want to know what the chance is that you can handle the quantitative courses and that you will be able to successfully do graduate level work in biostats/informatics/comp bio. On paper it is basically impossible to tell if you are capable of doing that sort of work, whereas you have better demonstrated you can handle molecular bio. Knowing a bit of programming languages and self-taught math is a start but won't measure up against people who spent their undergrad focusing on it, and those people will generally be your competition as an applicant to those schools. There is no such thing as a "safe" school, but certainly the ones you have listed are going to be among the most critical of your application.

You will have an uphill battle to prove you are capable of doing that sort of work. That said, you can certainly do it. I went the mol. bio -> bioinformatics route and worked hard to show I could handle statistics and computational approaches. Some of the umbrella programs like WashU will definitely allow you to work in whatever kind of lab you want, but that's only an option after you've been accepted.

[y123]

Thanks! I just saw this