The SciNet approach is a little reminiscent of a predictive discovery engine called Random which we covered last year — also coming out of Finland — especially in an earlier incarnation, back in 2013, when it was called Futureful.
A product of Finland's largest, seed-stage startup accelerator, Startup Sauna, Futureful has spent the last three years quietly building a predictive discovery engine that co-founder Marko Anderson says describes as "part toy, part tool" and seeks to provide users with a personal filter for the Web's multifarious content.
Helsinki, Finland-based smarter web surfing startup Futureful, which launched in the U.S. on iPad back in January — and is best described as a predictive discovery engine (rather than a traditional web browser) — has extended its reach, launching onto the iPhone with a global app.
Futureful, the AI predictive discovery engine backed by Skype co-founder Janus Friis which we last covered back in August, has a new name and a new interface — evolving its proposition of serendipitous browsing to more clearly flag up the randomness angle.
PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy.
The study population consisted of 140 patients who participated in a pharmacogenomic predictive marker discovery study at the University of Texas M. D. Anderson Cancer Center (MDACC) [ 17].
However, because responses are confined to a subset of treated subjects, future development will focus upon rational combinatorial approaches and predictive biomarker discovery.
Whole-genome microarray gene expression profiling has rapidly become the gold standard platform for retrospective prognostic and predictive gene discovery in human cancers, but currently depends on fresh-frozen tissue (FT) as a source of high quality RNA.
Such cost-benefit considerations together with the economic climate have precipitated imminent changes to clinical trial design in cancer medicine through the consideration of health economic costs as well as clinical benefit rates [ 1], mandating the requirement for parallel predictive biomarker discovery approaches.
One of the major aims of the project is to study and catalog the variations in nearly thousand candidate genes related to diseases and drug response for predictive marker discovery, founder identification and also to address questions related to ethnic diversity, migrations, extent and relatedness with other world population.
