MatureBayes: A Probabilistic Algorithm for Identifying the Mature miRNA within Novel Precursors

BACKGROUND: MicroRNAs (miRNAs) are small, single stranded RNAs with a key role in post-transcriptional regulation of thousands of genes across numerous species. While several computational methods are currently available for identifying miRNA genes, accurate prediction of the mature miRNA remains a challenge. Existing approaches fall short in predicting the location of mature miRNAs but also in finding the functional strand(s) of miRNA precursors. METHODOLOGY/PRINCIPAL FINDINGS: Here, we present a computational tool that incorporates a Naive Bayes classifier to identify mature miRNA candidates based on sequence and secondary structure information of their miRNA precursors. We take into account both positive (true mature miRNAs) and negative (same-size non-mature miRNA sequences) examples to optimize sensitivity as well as specificity. Our method can accurately predict the start position of experimentally verified mature miRNAs for both human and mouse, achieving a significantly larger (often double) performance accuracy compared with two existing methods. Moreover, the method exhibits a very high generalization performance on miRNAs from two other organisms. More importantly, our method provides direct evidence about the features of miRNA precursors which may determine the location of the mature miRNA. We find that the triplet of positions 7, 8 and 9 from the mature miRNA end towards the closest hairpin have the largest discriminatory power, are relatively conserved in terms of sequence composition (mostly contain a Uracil) and are located within or in very close proximity to the hairpin loop, suggesting the existence of a possible recognition site for Dicer and associated proteins. CONCLUSIONS: This work describes a novel algorithm for identifying the start position of mature miRNA(s) produced by miRNA precursors. Our tool has significantly better (often double) performance than two existing approaches and provides new insights about the potential use of specific sequence/structural information as recognition signals for Dicer processing. Web Tool available at: http://mirna.imbb.forth.gr/MatureBayes.html

Dynamic Quantization using Spike Generation Mechanisms

This paper introduces a neuro-inspired co-ding/decoding mechanism of a constant real value by using a Spike Generation Mechanism (SGM) and a combination of two Spike Interpretation Mechanisms (SIM). One of the most efficient and widely used SGMs to encode a real value is the Leaky-Integrate and Fire (LIF) model which produces a spike train. The duration of the spike train is bounded by a given time constraint. Seeking for a simple solution of how to interpret the spike train and to reconstruct the input value, we combine two different kinds of SIMs, the time-SIM and the rate-SIM. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by couting the spikes. The resulting coding/decoding process, called the Dual-SIM Quantizer (Dual-SIMQ), is a non-uniform quantizer. It is shown that it coincides with a uniform scalar quantizer under certain assumptions. Finally, it is also shown that the time constraint can be used to control automatically the reconstruction accuracy of this time-dependent quantizer

Age of Incorrect Information With Hybrid ARQ Under a Resource Constraint for N-ary Symmetric Markov Sources

The Age of Incorrect Information (AoII) is a recently proposed metric for real-time remote monitoring systems. In particular, AoII measures the time the information at the monitor is incorrect, weighted by the magnitude of this incorrectness, thereby combining the notions of freshness and distortion. This paper addresses the definition of an AoII-optimal transmission policy in a discrete-time communication scheme with a resource constraint and a hybrid automatic repeat request (HARQ) protocol. Considering an $N$-ary symmetric Markov source, the problem is formulated as an infinite-horizon average-cost constrained Markov decision process (CMDP). The source model is characterized by the cardinality of the state space and the probability of staying at the same state. Interestingly, it is proved that under some conditions, the optimal transmission policy is to never transmit. This reveals that there exists a region of the source dynamics where communication is inadequate in reducing the AoII. Elsewhere, there exists an optimal transmission policy, which is a randomized mixture of two discrete threshold-based policies that randomize at one state. The optimal threshold and the randomization component are derived analytically. Numerical results illustrate the impact of source dynamics, channel conditions, and the resource constraint on the average AoII.Comment: This work has been submitted to the IEEE for possible publicatio

Reconstruction of ultrasound RF echoes modelled as stable random variables

International audienceThis paper introduces a new technique for reconstruction of biomedical ultrasound images from simulated compressive measurements, based on modeling data with stable distributions. The proposed algorithm exploits two types of prior information: on one hand, our proposed approach is based on the observation that ultrasound RF echoes are best characterized statistically by alpha-stable distributions. On the other hand, through knowledge of the acquisition process, the support of the RF echoes in the Fourier domain can be easily inferred. Together, these two facts form the basis of an ℓp minimization approach that employs the iteratively reweighted least squares (IRLS) algorithm, but in which the parameter p is judiciously chosen, by relating it to the characteristic exponent of the underlying alpha-stable distributed data. We demonstrate, through Monte Carlo simulations, that the optimal value of the parameter p is just below that of the characteristic exponent α, which we estimate from the data. Our reconstruction results show that the proposed algorithm outperforms previously proposed reconstruction techniques, both visually and in terms of two objective evaluation measures

Compressed sensing reconstruction of convolved sparse signals

Abstract—This paper addresses the problem of efficient sam-pling and reconstruction of sparse spike signals, which have been convolved with low-pass filters. A modified compressed sensing (CS) framework is proposed, termed dictionary-based deconvolution CS (DDCS) to achieve this goal. DDCS builds on the assumption that a low-pass filter can be represented sparsely in a dictionary of blurring atoms. Identification of both the sparse spike signal and the sparsely parameterized blurring function is performed by an alternating scheme that minimizes each variable independently, while keeping the other constant. Simulation results reveal that the proposed DDSS scheme achieves an improved reconstruction performance when compared to traditional CS recovery. I