We analyzed the cognate transcriptome data in the CATMA resource and carried out data-mining to characterize novel genes and improve gene models.
The proposed system has three key components: 1) an API (Application Programming Interface) that extracts location information using Google Street View platform, 2) a computer vision method that is capable of detecting and classifying multiple classes of traffic signs; and 3) a data mining method to characterize the data attributes related to clusters of traffic signs.
The results show that the system has been found successful in conveying the concept of extracting energy from coal mines and to characterize the general performance.
We performed text mining to better characterize genes that showed differential expression after Aza treatment.
The perfect agreement of the radical SAM family TIGR03962, the glycosyltranferase family TIGR03965, and peptide family TIGR03969 creates a reliable phylogenetic profile that enables subsequent data mining to further characterize the systems.
Multifactor Dimensionality Reduction (MDR) is a popular and successful data mining method developed to characterize and detect nonlinear complex gene-gene interactions (epistasis) that are associated with disease susceptibility.
The parameters, including temperature time histories, soot density, carbon monoxide and heat release rate, were simulated to characterize the mine fires at various ventilating speeds.
The parameters, including the temperature distribution, soot density, CO and heat release rate, are simulated to characterize the mine fires at different ventilating speeds.
Miner evacuation affected by mine fire smoke movement has been investigated, and the following conclusions are obtained: (1) The parameters, including the temperature distribution, soot density, CO and heat release rate, are simulated to characterize the mine fires at different ventilating speeds.
