Request for suggestions: ageing and data-mining

Imag­ine you had the fol­low­ing at your dis­posal:

  • A Ph.D. in a biolog­i­cal sci­ence, with a fair amount of read­ing and wet-lab work un­der your belt on the topic of ag­ing and longevity (but in hind­sight, noth­ing that turned out to lev­er­age any real mechanis­tic in­sights into ag­ing).

  • A M.S. in statis­tics. Sadly, the non-Bayesian kind for the most part, but along the way ac­quired the meta-skills nec­es­sary to read and un­der­stand most quan­ti­ta­tive pa­pers with life-sci­ence ap­pli­ca­tions.

  • Love of pro­gram­ming and data, the abil­ity to learn most new com­puter lan­guages in a cou­ple of weeks, and at least 8 years spent hack­ing R code.

  • Re­search ac­cess to large amounts of anonymized pa­tient data.

  • Op­ti­misti­cally, two decades re­main­ing in which to make it all count.

Imag­ine that your goal were to slow or pre­vent biolog­i­cal ag­ing...

  1. What would be the spe­cific ques­tions you would try to tackle first?

  2. What ad­di­tional skills would you add to your toolkit?

  3. How would you al­lo­cate your limited time be­tween the re­search ques­tions in #1 and the ac­qui­si­tion of new skills in #2?

Thanks for your in­put.

Update

I thank ev­ery­one for their in­put and apol­o­gize for how long it has taken me to post an up­date.

I met with Aubrey de Grey and he recom­mended us­ing the anonymized pa­tient data to look for novel uses for already-pre­scribed drugs. He also sug­gested I do a com­par­i­son of ex­ist­ing lon­gi­tu­di­nal stud­ies (e.g. Fram­ing­ham) and the equiv­a­lent data el­e­ments from our data ware­house. I asked him that if he runs into any re­searchers with promis­ing the­o­ries or meth­ods but for a mas­sive hu­man dataset to test them on, to send them my way.

My origi­nal ques­tion was a bit to broad in ret­ro­spect: I should have fo­cused more on how to best lev­er­age the ca­pa­bil­ities my pro­ject already has in place rather than a more gen­eral “what should I do with my­self” kind of ap­peal. On the other hand, at the time I might have been less con­fi­dent about the pro­ject’s suc­cess than I am now. Though the con­ver­sa­tion im­me­di­ately went off into prospec­tive ex­per­i­ments rather than an­a­lyz­ing ex­ist­ing data, there were some great ideas there that may yet be­come prac­ti­cal to im­ple­ment.

At any rate, a lot of this has been over­come by events. In the last six months I re­al­ized that be­fore we even get to the bifur­ca­tion point be­tween longevity and other re­search ar­eas, there are a crapload of tech­ni­cal, lo­gis­ti­cal, and or­ga­ni­za­tional prob­lems to solve. I no longer have any doubt that these real prob­lems are worth solv­ing, my team is well po­si­tioned to solve many of them, and the solu­tions will sig­nifi­cantly ac­cel­er­ate re­search in many ar­eas in­clud­ing longevity. We have in­sti­tu­tional sup­port, we have a cred­ible rev­enue stream, and no short­age of promis­ing di­rec­tions to pur­sue. The limit­ing fac­tor now is peo­ple-hours. So, we are re­cruit­ing.

Thanks again to ev­ery­one for their feed­back.