FINET seeks to improve the precision (true positives/total true callings) in inferring interactions and it does, reaching 94% precision.
Please note: without stability-selection, elastic-net produced mostly noise. Increasing m subgroups in stablility selection (e.g. m=8) and frequency cutoff score (e.g. 0.95) dramatically improves selection precision, yet increasing iterations (n value) to a large number like 10000 does not help much as shown in our report
Numerous software have been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hampers their application to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity. The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with more than 91% true positive ratio (true positives/total true callings). The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET’s implementations with Julia, users without any background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.