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bioinformatics 2021 - 12th international conference on bioinformatics models, methods and algorithms;Part of the 14th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021

BIOINFORMATICS 2021 - 12th International Conference on Bioin...

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12th international conference on bioinformatics models, methods and algorithms, bioinformatics 2021 - Part of the 14th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021

ISBN: (纸本)9789897584909

The proceedings contain 26 papers. The topics discussed include: coordinate systems for pangenome graphs based on the level function and minimum path covers;the extension of the standard genetic code via optimal codon blocks division;backward pattern matching on elastic degenerate strings;TotemBioNet enrichment methodology: application to the qualitative regulatory network of the cell metabolism;a deep learning method to impute missing values and compress genome-ide polymorphism data in rice;flower pollination algorithm for detection of epistasis associated with a phenotype;possibilities of using neural networks to blood flow modelling;comprehensive statistical analysis on estimated errors of Averagine model for intact proteins;machine learning algorithms for predicting chronic obstructive pulmonary disease from gene expression data with class imbalance;and machine learning studies of non-coding RNAs based on artificially constructed training data.

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bioinformatics 2020 - 11th international conference on bioinformatics models, methods and algorithms, Proceedings;Part of 13th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

BIOINFORMATICS 2020 - 11th International Conference on Bioin...

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11th international conference on bioinformatics models, methods and algorithms, bioinformatics 2020 - Part of 13th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

ISBN: (纸本)9789897583988

The proceedings contain 41 papers. The topics discussed include: properties of the standard genetic code and its alternatives measured by codon usage from corresponding genomes;gene co-expression analysis for lung cancer biomarkers detection;minimal complexity requirements for proteins and other combinatorial recognition systems;expanding polygenic risk scores to include automatic genotype encodings and gene-gene interactions;influence of data similarity on the scoring power of machine-learning scoring functions for docking;a grid-based simulation model for the evolution of influenza a viruses;measuring the similarity of proteomes using grammar-based compression via domain combinations;evaluation of phenotyping errors on polygenic risk score predictions;and a machine learning approach to select the type of intermittent fasting in order to improve health by effects on type 2 diabetes.

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BIOINFORMATICS 2020 - 11th International Conference on Bioin...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

ISBN: (纸本)9789897583988

The proceedings contain 41 papers. The topics discussed include: prediction of dynamical properties of biochemical pathways with graph neural networks;a novel method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming with guaranteed admissibility;transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract;an optimized method for 3D body scanning applications based on kinect fusion;a convolutional neural network for spot detection in microscopy images;inferring the synaptical weights of leaky integrate and fire asynchronous neural networks: modeled as timed automata;discovering trends in environmental time-series with supervised classification of metatranscriptomic reads and empirical mode decomposition;and how to realize device interoperability and information security in mhealth applications.

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bioinformatics 2019 - 10th international conference on bioinformatics models, methods and algorithms, Proceedings;Part of 12th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

BIOINFORMATICS 2019 - 10th International Conference on Bioin...

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10th international conference on bioinformatics models, methods and algorithms, bioinformatics 2019 - Part of 12th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

ISBN: (纸本)9789897583537

The proceedings contain 38 papers. The topics discussed include: indexing k-mers in linear-space for quality value compression;detection of gene-gene interactions: methodological comparison on real-world data and insights on synergy between methods;detection of gene-gene interactions: methodological comparison on real-world data and insights on synergy between methods;the impact of the transversion/transition ratio on the optimal genetic code graph partition;identifying and resolving genome misassembly issues important for biomarker discovery in the protozoan parasite, cryptosporidium;pattern matching in discrete models for ecosystem ecology;construct semantic type of 'gene-mutation-disease' relation by computer-aided curation from biomedical literature;vectorized character counting for faster pattern matching;and gene set overlap: an impediment to achieving high specificity in over-representation analysis.

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Cluster and cloud computing for life sciences

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2024年 152卷 254-256页

作者： Carretero, Jesus Krefting, Dagmar Univ Carlos III Madrid Comp Architecture & Technol Madrid Spain Inst Med Informat Gottingen Germany

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources to manage Terabytes or Petabytes of data and TeraFlops or PetaFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analysing *** special issue contains papers presenting novel aspects of security and performance in infrastructures used for applications in the Life Sciences, optimizations for popular applications, such as genomics and pandemic modelling, and contributions related to new algorithms joining machine learning and data processing platforms to increase the efficiency and accuracy of bioinformatics.

关键词： bioinformatics Biomedicine And health Genomics Life sciences Cloud computing Workflows Biomedical informatics

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bioinformatics 2018 - 9th international conference on bioinformatics models, methods and algorithms, Proceedings;Part of 11th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

BIOINFORMATICS 2018 - 9th International Conference on Bioinf...

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9th international conference on bioinformatics models, methods and algorithms, bioinformatics 2018 - Part of 11th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

ISBN: (纸本)9789897582806

The proceedings contain 26 papers. The topics discussed include: parameter learning for spiking neural networks modelled as timed automata;loop-loop interaction metrics on RNA secondary structures with pseudoknots;protein disorder prediction using jumping motifs from torsion angles dynamics in Ramachandran plots;computer-aided formal proofs about dendritic integration within a neuron;bicluster detection by hyperplane projection and evolutionary optimization;supervised classification of metatranscriptomic reads reveals the existence of light-dark oscillations during infection of phytoplankton by viruses;selective covariance-based human localization, classification and tracking in video streams from multiple cameras;a model-checking approach to reduce spiking neural networks;a novel computer vision methodology for intelligent molecular modeling and simulation;grammar-based compression for directed and undirected generalized series-parallel graphs using integer linear programming;species categorization via microRNAs - based on 3'UTR target sites using sequence features;apoptotic regulatory module as switched control system - analysis of asymptotic properties;classification of Helitron's types in the *** genome based on features extracted from wavelet transform and SVM methods;study on the fidelity of biodevice T7 DNA polymerase;and a new dimension of breast cancer epigenetics - applications of variational autoencoders with DNA methylation.

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BIOINFORMATICS 2018 - 9th International Conference on Bioinf...

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ISBN: (纸本)9789897582806

The proceedings contain 24 papers. The topics discussed include: parameter learning for spiking neural networks modelled as timed automata;loop-loop interaction metrics on RNA secondary structures with pseudoknots;protein disorder prediction using jumping motifs from torsion angles dynamics in Ramachandran plots;bicluster detection by hyperplane projection and evolutionary optimization;supervised classification of metatranscriptomic reads reveals the existence of light-dark oscillations during infection of phytoplankton by viruses;selective covariance-based human localization, classification and tracking in video streams from multiple cameras;a model-checking approach to reduce spiking neural networks;a novel computer vision methodology for intelligent molecular modeling and simulation;grammar-based compression for directed and undirected generalized series-parallel graphs using integer linear programming;species categorization via MicroRNAs - based on 3'UTR target sites using sequence features;apoptotic regulatory module as switched control system - analysis of asymptotic properties;classification of Helitron's types in the *** genome based on features extracted from wavelet transform and SVM methods;a new dimension of breast cancer epigenetics - applications of variational autoencoders with DNA methylation;and systems biology analysis and literature data mining for unmasking pathogenic neurogenomic variations in clinical molecular diagnosis.

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Exploring Machine Learning algorithms and Protein Language models Strategies to Develop Enzyme Classification Systems 10th

Exploring Machine Learning Algorithms and Protein Language M...

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10th international Work-conference on bioinformatics and Biomedical Engineering (IWBBIO)

作者： Fernandez, Diego Olivera-Nappa, Alvaro Uribe-Paredes, Roberto Medina-Ortiz, David Univ Magallanes Dept Ingn Computac Ave Pdte Manuel Bulnes Punta Arenas 01855 Chile Univ Chile Dept Ingn Quim Biotecnol & Mat Beauche 851 Santiago Chile Univ Chile Ctr Biotechnol & Bioengn Beauchef 851 Santiago Chile

ISBN: (纸本)9783031349522;9783031349539

Discovering functionalities for unknown enzymes has been one of the most common bioinformatics tasks. Functional annotation methods based on phylogenetic properties have been the gold standard in every genome annotation process. However, these methods only succeed if the minimum requirements for expressing similarity or homology are met. Alternatively, machine learning and deep learning methods have proven helpful in this problem, developing functional classification systems in various bioinformatics tasks. Nevertheless, there needs to be a clear strategy for elaborating predictive models and how amino acid sequences should be represented. In this work, we address the problem of functional classification of enzyme sequences (EC number) via machine learning methods, exploring various alternatives for training predictive models and numerical representation methods. The results show that the best performances are achieved by applying representations based on pre-trained models. However, there needs to be a clear strategy to train models. Therefore, when exploring several alternatives, it is observed that the methods based on CNN architectures proposed in this work present a more outstanding facility for learning and pattern extraction in complex systems, achieving performances above 97% and with error rates lower than 0.05 of binary cross entropy. Finally, we discuss the strategies explored and analyze future work to develop integrated methods for functional classification and the discovery of new enzymes to support current bioinformatics tools.

关键词： Machine learning algorithms protein language models EC number classifications convolutional neural networks enzyme discovery

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Microbiome Classification in Colon Cancer Patients Using Chaos Game Representations and CNN models 11th

Microbiome Classification in Colon Cancer Patients Using Cha...

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11th international Work-conference on bioinformatics and Biomedical Engineering (IWBBIO)

作者： Montoya, Javier Fernandes, Francisco Freitas, Ana Teresa Univ Lisbon Inst Super Tecn Lisbon Portugal Inst Engn Sistemas Comp Invest & Desenvolvimento Lisbon Portugal

ISBN: (纸本)9783031646355;9783031646362

Emerging evidence links Colorectal Cancer (CRC) risk to antibiotic use, yet it remains uncertain if CRC correlates with the gut microbiota's resistome. The human gut resistome encompasses all genes in the microbiome that confer antibiotic resistance, termed Antibiotic Resistance Genes (ARG). The diversity and genome location of ARGs complicate their detection in the complex gut microbiome. Variability in sequences, dispersion in larger genetic structures, and background noise in microbiome data challenge traditional string matching methods. Advanced bioinformatics tools, like machine learning (ML) models, are needed to detect and analyze ARG dynamics in the gut microbiome. This paper proposes a ML approach for the identification of ARGs in gut microbiome data with the aim of serving as a tool for analyzing the association between ARG abundance and CRC development. Chaos Game Representation (CGR) was used as a data preprocessing and feature extraction technique for representing microbiome sequences and ARGs as images. Convolutional Neural Networks (CNNs) were used to define the classifier to identify ARGs in the gut microbiomes of CRC patients and healthy individuals. The results show that by exploring different features of these algorithms, including the resolution of the CGR images and different CNN structures, it is possible to build classifiers with accuracy and precision of the class of interest of 98.7%-99.2% and 95.2%-98.3%, respectively. However, it is necessary to analyze a larger volume of data from patients and healthy individuals in order to conclude whether CRC development is indeed linked to the gut microbiota's resistome.

关键词： Colorectal Cancer Gut Resistome Chaos Game Representation Convolutional Neural Networks

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Differential diagnosis of radiation encephalopathy and post-radiation brain tumor recurrence by machine learning models based on contrast-enhanced MRI 4

Differential diagnosis of radiation encephalopathy and post-...

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4th international conference on bioinformatics and Intelligent Computing (BIC)

作者： Li, Te Wang, Lingfei Wang, Xi Sun, Hong Zhejiang Police Vocat Acad Dept Secur & Precaut Hangzhou Peoples R China Hangzhou Med Coll Dept Oncol 903 Hosp PLA Joint Logist Support Force Xi Hu Affiliated Hosp Hangzhou Peoples R China

ISBN: (纸本)9798400716645

Purpose: Discriminating radiation encephalopathy (REP) from brain tumor recurrence is often difficult. This study aims to develop and validate an approach to distinguish REP from post-radiation brain tumor recurrence using machine learning. methods: This study involved 102 patients diagnosed and treated in our institution between 2020 and 2023. A total of 2153 radiomics features were extracted from contrast-enhanced MRI by using 3D-Slicer software and Pycharm platform. Nine diagnostic models were built and compared based on three selection methods and three classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Results: The least absolute shrinkage and selection operator with correlation (LASSO+corr) was chosen as the optimal selection method, which selected 17 important radiomics features. The most promising model was a combination of LASSO+corr as the selection method and logistic regression (LR) as the classification algorithm. The combination models of LASSO+corr with LR, random forest (RF) and support vector machine (SVM) showed sensitivities of 0.88, 0.86 and 0.97, and specificities of 0.98, 0.93 and 0.90 with AUC of 0.9805, 0.9452 and 0.9743, respectively. Conclusion: Radiomics-based machine learning has potential to be utilized in differentiating REP from recurrent brain tumor after radiotherapy accurately.

关键词： Radiation encephalopathy (REP) brain tumor radiomics machine learning differential diagnosis

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