The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART.
In this paper we describe an experiment using a new database model for one of tranSMART's microarray data tables that may be more suitable for high-dimensional data storage and querying than the relational model currently implemented in tranSMART.
Identification of novel splice sites is assisted by a probabilistic model, currently implemented as a maximum entropy model (Yeo and Burge, 2004), which uses frequencies of nucleotides neighboring a splice site to discriminate between true and false splice sites.
In this paper we have demonstrated that a key-value pair schema leads to an increase in performance when querying high-dimensional biological data, over the relational model currently implemented in tranSMART.
Using both real and simulated data (see Additional file 1), we confirm the capacity of the site-heterogeneous mixture model (CAT) under a Bayesian framework, currently implemented in software PhyloBayes [ 38, 56], to substantially avoid the LBA artefacts.
The results suggested that the use of amino acid sequences instead of DNA is more appropriated at the inter-ordinal level and that the use of the site-heterogeneous mixture model (CAT) under a Bayesian framework, currently implemented in the software PhyloBayes, substantially avoids LBA artefacts.
Furthermore, the way the model is currently implemented in the Forest Vegetation Simulator partly masks poor performance at the tree level, but likely amplifies error at the stand level, a particularly troubling result in many conceivable applications.
Comparison of the proposed model predictions with the results from two empirical models currently being implemented in design works, a neural network-based model from literature and also the field data shows that the EPR model is capable of capturing, predicting and generalizing predictions to unseen data cases, for lateral load bearing capacity of piles with very high accuracy.
Additionally, we also plan to implement other codon substitution models such as EMC (currently implemented in Selecton), which takes into account both mechanistic and empirical data and arguably better fits real sequence data [ 67].
