The product's overarching goal is to allow founders to make more informed product and business decisions by "utilizing a data-driven feedback loop," according to its mission statement.
In this paper, we develop a novel optimal tracking control scheme for a class of nonlinear discrete-time Markov jump systems (MJSs) by utilizing a data-based reinforcement learning method.
Out of its initial research came Startup Compass: A benchmarking tool that leverages the research project's data to enable startups and their founders to evaluate their progress compared to others in their space and help them make more informed product and business decisions by "utilizing a data-driven feedback loop," according to its mission statement.
Validation of the obtained results by utilizing an independent data set, demonstrated that values, predicted by the proposed read-out parameters, were also capable to differentiate between various therapies with a different mode of action.
Maternal genes present in the TBP-knockdown and miR-430 targets gene sets (Giraldez et al, 2006) were identified by utilizing an existing data set of transcripts accumulated in the unfertilised egg (Mathavan et al, 2005).
Consequently, computational methods play an important role in prioritization and identification of the most likely disease-associated genes by utilizing a variety of data sources such as gene expression [ 3, 4], functional annotations [ 4- 7], and protein-protein interactions (PPIs) [ 3, 8- 14].
By utilizing a new source of data, the 2012 Australian Public Service Commission data (n = 21,093), this paper is able to overcome these data constraints and provides one of the first studies focusing on the likelihood of innovative activity in the public sector.
In this paper, we report an integrative analysis linking HF gene expression and PPI network data by utilizing a publicly available ICM-related microarray data set [5].
The objective of this research was to address these limitations by utilizing a large, population-based data system containing comprehensive information about patients, their diseases, and their hospitalizations to identify variables independently associated with leaving hospital AMA.
In particular, breast cancer has been widely studied by utilizing a large amount of transcriptome data.
This computational pipeline performs a full analysis of MeDIP-seq data by utilizing a number of freely available software packages.
