  In analyzing of modern biological data, we are often dealing with ill-posed
problems and missing data, mostly due to high dimensionality and
multicollinearity of the dataset. In this paper, we have proposed a system
based on matrix factorization (MF) and deep recurrent neural networks (DRNNs)
for genotype imputation and phenotype sequences prediction. In order to model
the long-term dependencies of phenotype data, the new Recurrent Linear Units
(ReLU) learning strategy is utilized for the first time. The proposed model is
implemented for parallel processing on central processing units (CPUs) and
graphic processing units (GPUs). Performance of the proposed model is compared
with other training algorithms for learning long-term dependencies as well as
the sparse partial least square (SPLS) method on a set of genotype and
phenotype data with 604 samples, 1980 single-nucleotide polymorphisms (SNPs),
and two traits. The results demonstrate performance of the ReLU training
algorithm in learning long-term dependencies in RNNs.
