José M. Granado-Criado’s research while affiliated with University of Extremadura and other places

At present, designing an RNA sequence that folds into a specific secondary structure is a problem that is not fully solved, due to its exponentially increasing complexity. To address this matter, many computational methods have been developed, but none of them has been able to completely and in an affordable time solve Eterna100, a widely recognized benchmark used to test the performance of RNA inverse folding algorithms. In previous publications we presented the m2dRNAs tool, a Multiobjective Evolutionary Algorithm, and its extension eM2dRNAs, which added a recursive decomposition of the target structure, thus simplifying the problem. At that time they successfully improved the ability to solve the RNA inverse folding problem, but were still unable to complete the Eterna100 benchmark. Here we introduce ES+eM2dRNAs, an improvement of eM2dRNAs that optimizes the decomposition process, as a drawback in its nature was identified.A comparative study of this new tool against its predecessors and other RNA design methods was performed using the two current versions of the Eterna100 benchmark. ES+eM2dRNAs was shown to be the best in all performance indicators considered (number of structures solved, success rate, and total run time). Moreover, it is able to solve two Eterna100 structures for which none of the compared methods had ever found a solution.

RNA design, also known as the RNA inverse folding problem, involves discovering a nucleotide sequence that folds into a target structure. This problem has been addressed from a wide number of approaches, improving the ability to solve it in a reasonable time over time. Despite all these efforts, today no method has completely solved the problem. We present GREED-RNA, a new RNA design algorithm, based on a simple greedy evolutionary strategy. The main feature is the use of several objective functions (Base-pair distance, Hamming distance, probability over ensemble, partition function, ensemble defect and GC-content) to select the best solution in each iteration, changing their weight according to the problem-solving conditions. The performance of GREED-RNA was tested using the Eterna100 benchmark, widely used in this area and never fully solved by any method. In addition, a comparative analysis against several published RNA design methods considering three metrics (solved structures, success rate and execution time), allowed us to verify that GREED-RNA performs better than previously developed methods, thus successfully improving the current ability to solve this problem. This tool also allows users to select a range within which the GC-content of the solution sequences must fall. Source code and results are available at https://github.com/iARN-unex/GREED-RNA.

Peer assessment has traditionally represented a key tool to enhance active learning and critical thinking. However, the success of this approach is governed by different factors, which have been accentuated in recent years. The implementation of peer assessment is consequently a challenging task in the current context. This work investigates peer assessment strategies in seven Computer Engineering courses. Students’ performance and assessment accuracy are analyzed throughout five academic years, covering the transition from offline to online methodologies according to the evolution of educational environments. More specifically, peer and lecturer’s grades are examined to identify correlations or deviations in two execution phases. The first phase involves the analysis of in-class offline peer assessments during four academic years, integrated as part of continuous assessment tasks. The second phase deals with the evaluation of online peer assessments in 2021/2022, considering different platforms to manage submissions and reviews. The offline experience denotes statistical correlations between the grades assigned by the peers and the lecturer, while also revealing patterns that affected the performance of students. In addition, the switch to online methodologies does not significantly affect the assessments in courses that adopted peer strategies in the past. Finally, comparable results are obtained under single-blind and double-blind models after careful training.

A number of research works support the relationship between Single Nucleotide Polymorphisms (SNPs) and neurodegenerative diseases (e.g. Alzheimer’s or Parkinson’s Disease). It has been proven that these neurodegenerative diseases are mainly caused by the interaction of different SNPs. The complexity of identifying genetic interactions increases exponentially with two factors: (i) the number of SNPs contained in the biological dataset under study and (ii) the number of SNPs involved in the interaction. Therefore, this paper proposes the application of two of the most successful multiobjective evolutionary algorithms to solve this problem: a Reference-point based Many-objective Fast Non-dominated Sorting Genetic Algorithm (NSGA-III) and a Multiobjective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA). These algorithms have been tested with four datasets (including a real dataset about Bipolar Disorder with 425,574 SNPs) and three interaction sizes: 2, 5, and 8 loci. In addition, they have been compared against well-known and relevant approaches published in the literature, in both multiobjective and biological terms. The results clearly show the advantages of the approach based on NSGA-III. Particularly, NSGA-III improves the results obtained by other algorithms in multiobjective terms (by means of Hypervolume and Set Coverage indicators) and in biological terms (by means of Power, Recall, Precision, and F-measure metrics). Moreover, it reveals new 2 and 5 loci interactions over a Bipolar Disorder real dataset. Therefore, NSGA-III represents a relevant approach to detect high-order genetic interactions.

This article details a fast and efficient implementation of the Montgomery Modular Multiplication by taking advantage of parallel multipliers and adders. This implementation was programmed in high-level synthesis language and tested on a FPGA device. In order to test the performance of the proposal, a sequential version of the algorithm was also implemented in hardware. Moreover, we compared the parallel implementation with a software version and with five contributions from the literature. This way, we found that our proposal improves the performance of all other implementations.

This paper starts proposing a complete recommender system implemented on reconfigurable hardware with the purpose of testing on-chip, low-energy embedded collaborative filtering applications. Although the computing time is lower than the one obtained from usual multicore microprocessors, this proposal has the advantage of providing an approach to solve any prediction problem based on collaborative filtering by using an off-line, highly-portable light computing environment. This approach has been successfully tested with state-of-the-art datasets. Next, as a result of improving certain tasks related to the on-chip recommender system, we propose a custom, fine-grained parallel circuit for quick matrix multiplication with floating-point numbers. This circuit was designed to accelerate the predictions from the model obtained by the recommender system, and tested with two small datasets for experimental purposes. The accelerator is built from two levels of parallelism. On the one hand, several predictions run in parallel through the simultaneous multiplication of different vectors of two matrices. On the other hand, the operation of each vector is executed in parallel by multiplying pairs of floating-point values to later add the corresponding results in parallel as well. This circuit was compared with other approaches designed for the same purpose: circuits built using automatized tools of high-level synthesis, a general-purpose microprocessor, and high-performance graphical processing units. The performance of the prediction accelerator in terms of time surpassed that of the other approaches. We also evaluated the scalability of the circuit to practical problems using the high-level synthesis approach, and confirmed that implementations based on reconfigurable hardware allow acceptable speedups of multi-core processors.

Multiple studies provide evidence on the impact of certain gene interactions in the occurrence of diseases. Due to the complexity of genotype–phenotype relationships, it is required the development of highly efficient algorithmic strategies that successfully identify high-order interactions attending to different evaluation criteria. This work investigates parallel evolutionary computation approaches for multiobjective gene interaction analysis. A multiobjective genetic algorithm, with novel optimized design features, is developed and parallelized under problem-independent and problem-dependent schemes. Experimental results show the relevant performance of the method for complex interaction orders, significantly accelerating execution time (up to 296×) with regard to other state-of-the-art multiobjective tools.