Performance Evaluation and Optimal Detection of Relay-Assisted Diffusion-Based Molecular Communication With Drift.

In this paper, we consider a decode-and-forward (DF) relay-assisted diffusion-based molecular communication system inside one of the blood vessels of a human body with positive drift from transmitter to receiver. We use the normal approximation to the distribution of the number of received molecules and derive a closed-form expression for the end-to-end bit error probability of the system. We then propose an optimization problem that aims at minimizing the bit error probability of the system and solve it at the receiver nanomachine by an algorithm based on the bisection method to determine the optimal detection threshold. Furthermore, we study the impact of the system parameters, such as drift velocity, position of the relay node and number of allocated molecules on the performance of the system. The numerical results show that with a constant molecular budget, DF relying strategy can considerably improve the system performance.

Missing value imputation in DNA microarrays based on conjugate gradient method.

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods.