Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
Official Name
Martínez Ríos, Félix Orlando
Alternative Name
fmartin
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ORCID
Scopus Author ID
24339038500
Researcher ID
N-7502-2018

Research Output
Now showing 1 - 10 of 34
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Simulated Annealing for SAT Problems Using Dynamic Markov Chains with Linear Regression Equilibrium

2008 , Martínez Ríos, Félix Orlando , Frausto-Solís, Juan

Since the appearance of Simulated Annealing (SA) algorithm it has shown to be an efficient method to solve combinatorial optimization problems. This algorithm is based on two cycles: the external or temperature cycle and the internal or Metropolis Cycle. In this paper a new SA method named LRSA is presented. LRSA dynamically finds the equilibrium in the Metropolis cycle by using Linear Regression. Experimentation shows that the proposed method is more efficient than the classical one, since it obtains the same quality in the final solution with less processing time.

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Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials’ Discovery

2023 , Agüero-Chapin, Guillermin , Antunes, Agostinho , Mora, José R. , Pérez, Noel , Contreras-Torres, Ernesto , Valdes-Martini, José R. , Martínez Ríos, Félix Orlando , Zambrano, Cesar H. , Marrero-Ponce, Yovani

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks’ mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose. © 2023 by the authors.

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Packing algorithm inspired by gravitational and electromagnetic effects

2019 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

This paper introduces a faster and more efficient algorithm for solving a two-dimension packing problem. This common optimization problem takes a set of geometrical objects and tries to find the best form of packing them in a space with specific characteristics, called container. The visualization of nanoscale electromagnetic fields was the inspiration for this new algorithm, using the electromagnetic field between the previously placed objects, this paper explains how to determine the best positions for to place the remaining ones. Two gravitational phenomena are also simulated to achieve better results: shaken and gravity. They help to compact the objects to reduce the occupied space. This paper shows the executions of the packing algorithm for four types of containers: rectangles, squares, triangles, and circles. © Springer Nature

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μ𝜃-EGF: A New Multi-Thread and Nature-Inspired Algorithm for the Packing Problem

2020 , Martínez Ríos, Félix Orlando , Marmolejo Saucedo, José Antonio , García-Jacas, César R. , Murillo-Suarez, Alfonso

In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used. © Springer Nature

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A new hybridized algorithm based on Population-Based Simulated Annealing with an experimental study of phase transition in 3-SAT

2017 , Martínez Ríos, Félix Orlando

This paper is about experiments for satisfiability problem using a new algorithm (GR-MM-PBSA) that improves the algorithm Population-Based Simulated Annealing (PBSA). GR-MM-PBSA runs in a parallel way Simulated Annealing (SA) and Threshold Annealing (TA) algorithms with a Golden Ratio space search strategy and Markovian Model to select initial and final temperature. In this paper we execute differents hybridized Simulated Annealing (or Threshold Accepting) algorithms and compares the efficiency of these, using a metric based on transition phase effect. Simulated Annealing Algorithms (SAA) theoretically can reach the optimum if the control parameters and cooling scheme are chosen correctly. All algorithms are compared with a metric based on transition phase obtained for 3-SAT instances. This paper shows the results of SAA hybridizations are more efficient than the original algorithm, without increasing their computational complexity. We also show the experimental data about runs with 820 3-SAT instances with ratio clauses-variables between 2.0 to 6.0.

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StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks

2023 , Aguilera-Mendoza, Longendri , Ayala-Ruano, Sebastián , Martínez Ríos, Félix Orlando , Chávez, Edgar , García-Jacas, César R. , Brizuela, Carlos A. , Marrero-Ponce, Yovani

Motivation: Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs. Copyright © 2024 Oxford University Press

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Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques

2021 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena's behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms. © 2021 Elsevier B.V.. All rights reserved.

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Innovative Alignment-Based Method for Antiviral Peptide Prediction

2024 , Daniela de Llano García , Marrero Ponce, Yovani , Guillermin Agüero-Chapin , Francesc J. Ferri , Agostinho Antunes , Martínez Ríos, Félix Orlando , Hortensia Rodríguez

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models’ robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew’s correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.

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A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

2022 , Romero, Maylin , Marrero-Ponce, Yovani , Rodríguez, Hortensia , Agüero-Chapin, Guillermin , Antunes, Agostinho , Aguilera-Mendoza, Longendri , Martínez Ríos, Félix Orlando

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

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A new heuristic algorithm to solve Circle Packing problem inspired by nanoscale electromagnetic fields and gravitational effects

2018 , Martínez Ríos, Félix Orlando , Marmolejo Saucedo, José Antonio , Murillo-Suarez, Alfonso

In this paper, we present a new algorithm for the fast and efficient solution of the Packing problem in two dimensions. The packing problem consists in finding the best arrangement of objects (many geometrical forms) in a specific space called container.This new algorithm is inspired by the observations of nanometric scale electromagnetic fields. We use the electromagnetic theory of the electric field to calculate the best position to place a circular object in a configuration of other circular objects previously packing. Also, in this new algorithm we simulate two processes called »gravity» and »shaken» that compact the distribution of the objects placed in the container and allow to minimize the unoccupied space. © 2018 IEEE.

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