Article
Biology
Frederik Wieder, Martin Henk, Alexander Bockmayr
Summary: Elementary flux modes (EFMs) are important in the constraint-based analysis of metabolic networks, as they represent minimal functional units at steady-state. The distribution of EFMs in the face lattice of the flux cone of the metabolic network is analyzed, revealing that EFMs in the relative interior of the cone are rare. The concept of EFM degree is introduced to measure their level of elementary nature, providing insights into the decomposition of flux vectors and EFMs.
JOURNAL OF MATHEMATICAL BIOLOGY
(2023)
Article
Automation & Control Systems
Jianhua Jiang, Yutong Liu, Ziying Zhao
Summary: The paper introduces two tree migration mechanisms, Triple Tree-Seed Algorithm outperforms TSA on 30 test functions and proves its applicability in solving practical problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
R. J. Kuo, Y. R. Zheng, Thi Phuong Quyen Nguyen
Summary: Smart devices and technology applications are widely used in various fields, leading to a rapid increase in recorded and collected information, making data analysis, specifically clustering analysis, crucial for obtaining valuable information from datasets. This study proposes a possibilistic fuzzy k-modes (PFKM) algorithm, which combines the concept of possibility with the fuzzy k-modes (FKM) algorithm to improve clustering results for categorical data by addressing outliers. Additionally, the study employs three metaheuristics – a genetic algorithm (GA), a particle swarm optimization (PSO), and the sine-cosine algorithm (SCA) – to enhance clustering performance.
INFORMATION SCIENCES
(2021)
Article
Engineering, Chemical
Maxime Maton, Philippe Bogaerts, Alain Vande Wouwer
Summary: The derivation of minimal bioreaction models is crucial for monitoring and controlling the production of cell/microorganism cultures. These models are obtained by selecting elementary flux modes and utilizing multilayer perceptrons for dynamic modeling, resulting in promising prediction results for culture production.
Article
Computer Science, Interdisciplinary Applications
Geng Wang, Zhiqiang Lyu, Renjing Gao, Cao Tan
Summary: This study proposes an optimization method for the structural design of a moving-coil electromagnetic linear actuator (MCELA) with a Halbach permanent magnet array to minimize the volume of the structure and permanent magnet (PM) consumption while maintaining the output performance. An improved genetic algorithm-particle swarm optimization algorithm is presented to determine the optimal structural parameters of the MCELA. Experimental results demonstrate the effectiveness of the proposed method.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Masoud Dashtdar, Mohit Bajaj, Seyed Mohammad Sadegh Hosseinimoghadam, Hamed Mershekaer
Summary: This paper discusses the issue of fault location in distribution networks, proposing an optimization model based on smart meter data and power flow method to locate faults. The results show that the proposed method performs well under different fault conditions.
Article
Metallurgy & Metallurgical Engineering
Xu Zhe, Ni Wei-chen, Ji Yue-hui
Summary: Randomness is crucial in ensemble learning, and a common practice is to rotate feature space randomly. However, this requires a large number of trees and computing resources. The MGARF algorithm proposed in this paper utilizes multimodal genetic algorithm to select diverse and accurate base learners, outperforming random forest and random rotation methods on classification datasets.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Metallurgy & Metallurgical Engineering
Satyendra Kumar, Moina Ajmeri
Summary: In this work, a variable structure control technique is proposed to achieve satisfactory robustness for unstable processes, with optimal values of unknown parameters obtained using Whale optimization algorithm. The proposed control strategy is applied to three different types of unstable processes and shows superior performance over the recently reported VSC system. Additionally, the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Biochemistry & Molecular Biology
Donovan H. Parks, Maria Chuvochina, Christian Rinke, Aaron J. Mussig, Pierre-Alain Chaumeil, Philip Hugenholtz
Summary: The Genome Taxonomy Database (GTDB) provides a phylogenetically consistent taxonomy for prokaryotic genomes sourced from the NCBI database. It includes a large number of bacterial and archaeal genomes, highlights the importance of metagenome-assembled genomes, and discusses improvements to the GTDB website and the procedure for updating species clusters.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Wei Li, Xinyu Gao, Lei Wang
Summary: Multifactorial optimization is a widely studied optimization problem. This paper introduces an evolutionary multitasking optimization algorithm, EMT-ADT, which utilizes a decision tree to predict and select individuals for knowledge transfer, improving algorithm performance.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Environmental Sciences
Eleri Evans, Alexander D. Fraser, Sue Cook, Richard Coleman, Ian Joughin
Summary: Methods to estimate the calving flux of an ice shelf either capture the movement of the calving-front location or assume the calving front is stationary. Overcoming issues like temporal aliasing can be achieved by combining different methods.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Mechanics
Kamran Foroutan, Seyyed Mojtaba Varedi-Koulaei, Nguyen Dinh Duc, Habib Ahmadi
Summary: This paper investigates the non-linear static and dynamic buckling analyses of laminated composite cylindrical shells using swarm-based metaheuristics to optimize the fiber's angles. It is found that the optimization algorithms significantly improve the critical buckling load values, with the WOA algorithm performing the best among the ones tested.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS
(2022)
Article
Materials Science, Multidisciplinary
Jernej Vajda, Luka Banovic, Mihael Misko, Igor Drstvensek, Marko Milojevic, Uros Maver, Bostjan Vihar
Summary: Accuracy and precision are crucial in extrusion-based material handling, such as 3D bioprinting. To address the issue of discrepancies between piston motion and material extrusion, we propose an algorithmic approach that optimizes piston movements based on extrusion analysis. This approach improves the accuracy and precision of complex extrusion-based bioprinting systems.
MATERIALS & DESIGN
(2023)
Article
Automation & Control Systems
Shi Pu, Wei Shi, Jinming Xu, Angelia Nedic
Summary: In this article, a distributed convex optimization approach is introduced, which achieves minimal cost functions through the push-pull gradient method for information exchange between nodes in a network. Experimental results demonstrate that this algorithm exhibits linear convergence in various network architectures, especially showing significant performance in random-gossip settings.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Engineering, Civil
Duc-Kien Thai, Dai-Nhan Le, Quoc Hoan Doan, Thai-Hoan Pham, Dang-Nguyen Nguyen
Summary: This study develops machine learning models using tree-based algorithms and ensembles to classify the local damage levels of FRC panels subjected to missile impact load. Six different algorithms, including Decision Tree, Random Forest, Bagging, AdaBoost, XGBoost, and CatBoost, were trained and evaluated based on a dataset of 176 experiments. Bayesian Optimization algorithm and k-fold cross validation were utilized to improve prediction accuracy. The results show that the proposed models can predict the local damage levels with acceptable accuracy. Ensemble methods outperform single estimator models, and Random Forest is recommended for imbalanced datasets.
Article
Oncology
L. Tobalina, J. Armenia, E. Irving, M. J. O'Connor, J. Forment
Summary: Germline mutations in BRCA1 or BRCA2 genes predispose to hereditary breast and ovarian cancer. Tumours from these patients are sensitive to platinum drugs and PARPi treatments. Some patients develop reversion mutations capable of restoring BRCA protein expression, mainly in DNA repair pathways. Identifying and targeting these pathways could improve treatment durability and offer new therapeutic opportunities.
ANNALS OF ONCOLOGY
(2021)
Article
Oncology
Claudia Winkler, Joshua Armenia, Gemma N. Jones, Luis Tobalina, Matthew J. Sale, Tudor Petreus, Tarrion Baird, Violeta Serra, Anderson T. Wang, Alan Lau, Mathew J. Garnett, Patricia Jaaks, Elizabeth A. Coker, Andrew J. Pierce, Mark J. O'Connor, Elisabetta Leo
Summary: This study highlights the potential of SLFN11 as a biomarker for stratifying patients receiving DDA and/or DDRi therapies. SLFN11 was found to be associated with better response to DDA treatment, and combination therapies with DDRi targeting replication stress response showed efficacy in different cancer types.
BRITISH JOURNAL OF CANCER
(2021)
Article
Computer Science, Interdisciplinary Applications
Jesus M. Rodriguez-de-Vera, Gregorio Bernabe, Jose M. Garcia, Daniel Saura, Josefa Gonzalez-Carrillo
Summary: This study uses deep learning-based method to diagnose left ventricular non-compaction (LVNC). By training an artificial neural network to segment magnetic resonance imaging, the percentage of left ventricular hyper-trabeculation can be measured and automatically diagnosed. The results show that the deep learning method outperforms traditional tools in accuracy and clinical validity.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
J. E. Beasley
Summary: This paper addresses a practical problem in military mission planning, focusing on planning a path for robots to reach a target without being detected by enemy sensors. The robots can take actions to evade detection by knocking out or confusing sensors, with actions dependent on path and time.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Software Engineering
Pablo Antonio Martinez, Biagio Peccerillo, Sandro Bartolini, Jose Manuel Garcia, Gregorio Bernabe
Summary: This article discusses different technologies and approaches to address the performance portability problem, with focus on Intel's oneAPI solution. It uses the machine learning framework Caffe as a case study to explore the feasibility and advantages of using oneAPI for development.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Pablo Antonio Martinez, Biagio Peccerillo, Sandro Bartolini, Jose M. Garcia, Gregorio Bernabe
Summary: This paper discusses the application of the PHAST Library to improve the performance of the Caffe framework. By optimizing the source code of Caffe and the PHAST Library itself, the PHAST implementation achieves performance portability on both CPU and GPU. The results show that the PHAST version of Caffe performs better in various aspects compared to the original version.
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Pablo Antonio Martinez, Gregorio Bernabe, Jose Manuel Garcia
Summary: This paper presents HDNN, a proof-of-concept MLIR dialect for cross-platform computing specialized in deep neural networks. HDNN supports CPUs, GPUs and TPUs as target devices. The paper provides a comprehensive description of the HDNN dialect and explains how it solves the P-3 problem of parallel programming. HDNN is device-agnostic and is a domain-specific language that improves programming productivity.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Biochemical Research Methods
Inigo Apaolaza, Edurne San Jose-Eneriz, Luis V. Valcarcel, Xabier Agirre, Felipe Prosper, Francisco J. Planes
Summary: Synthetic lethality (SL) is a type of genetic interaction that results in cell death when the function of two genes is simultaneously lost. In cancer metabolism, SL can be used to predict genetic vulnerabilities and nutrient dependencies. Integrating both genetic and environmental factors, a computational approach is proposed to identify new metabolic synthetic lethal interactions and reveal potential vulnerabilities in different malignancies.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biochemical Research Methods
Luis J. Valcarcel, Edurne L. San Jose-Eneriz, Xabier Cendoya, Angel L. Rubio, Xabier Agirre, Felipe L. Prosper, Francisco Planes
Summary: The study introduces a novel method called BOSO for feature selection in linear regression models. BOSO demonstrates a better combination of accuracy and simplicity in dealing with high-dimensional datasets, making it important for predictive modeling in biomedical research.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Mathematical & Computational Biology
Francesco Balzerani, Daniel Hinojosa-Nogueira, Xabier Cendoya, Telmo Blasco, Sergio Perez-Burillo, Inigo Apaolaza, M. Pilar Francino, Jose Angel Rufian-Henares, Francisco J. Planes
Summary: The relevance of phenolic compounds in the human diet has increased due to their role as natural antioxidants and chemopreventive agents. This article presents a method to predict the metabolism of phenolic compounds in the human gut microbiota using an enzyme promiscuity approach and a reinforcement learning strategy. The predicted reactions were integrated with an existing metabolic network, resulting in a more complete understanding of the metabolic processing of various foods. Experimental validation of the microbial metabolites produced during the fermentation of lentils further supports the importance of these improvements.
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Francisco Guil, Guillermo Sanchez-Cid, Jose M. Garcia
Summary: Systems biology, combining computational biology with wet laboratory methods, is crucial for understanding biological systems, particularly in treating infectious microorganisms. Flux balance analysis is a widely used method for studying metabolic networks and optimizing metabolic flux.
Article
Mathematical & Computational Biology
Naroa Barrena, Luis V. V. Valcarcel, Danel Olaverri-Mendizabal, Inigo Apaolaza, Francisco J. J. Planes
Summary: Synthetic lethality is a promising concept in cancer research, and computational tools have been developed to predict and exploit it for identifying tumor-specific vulnerabilities. This article introduces the concept of genetic Minimal Cut Sets (gMCSs), which is a theoretical approach to synthetic lethality for genome-scale metabolic networks. By incorporating linear regulatory pathways, the authors extended the gMCS approach to complex protein-protein interactions and applied it to integrated models of human cells, discovering new essential genes and their associated synthetic lethal in cancer. The performance of different integrated models was assessed using large-scale in-vitro gene silencing data, and predictions were discussed based on published literature in cancer research.
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
(2023)
Article
Biochemical Research Methods
Francisco Guil, Jose F. Hidalgo, Jose M. Garcia
Summary: This article presents a methodology to improve the representativeness of the subset of elementary flux modes (EFMs) computed in metabolic networks. The authors introduce the concept of stability for a network parameter and define several metrics to study and compare EFM biases. They compare previously proposed methods and present a new method (PiEFM) that is more stable, has better representativeness measures, and exhibits better variability in the extracted EFMs.
Article
Operations Research & Management Science
J. E. Beasley
Summary: This paper addresses the problem of frequency assignment, specifically how to decide new frequency allocations while limiting interference and minimizing changes to the existing allocation. The authors propose an optimization model that minimizes changes and simplifies the representation of interference. They also explore adaptations for other scenarios and present computational results comparing their approach to the standard representation.
EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION
(2022)
Proceedings Paper
Computer Science, Information Systems
Luis Miralles-Pechuan, Fernando Jimenez, Josa Manuel Garcia
Summary: Real-Time Bidding (RTB) is a popular internet advertising system where advertisers bid to display their ads. The most popular method is the Generalized Second-Price auction. This paper proposes an alternative betting system that considers not only economic factors but also other relevant aspects of the advertising system. It also introduces a methodology using genetic algorithms to optimize advertiser selection. Experiments show that this approach offers greater benefits for RTB networks in the medium and long term.
PROCEEDINGS OF SIXTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICICT 2021), VOL 2
(2022)
