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13th Brazilian symposium on bioinformatics, BSB 2020

ISBN: (纸本)9783030657741

The proceedings contain 25 papers. The special focus in this conference is on bioinformatics. The topics include: Assessing the Sex-Related Genomic Composition Difference Using a k-mer-Based Approach: A Case of Study in Arapaima gigas (Pirarucu);in silico Pathogenomic Analysis of Corynebacterium Pseudotuberculosis Biovar Ovis;Natural Products as Potential Inhibitors for SARS-CoV-2 Papain-Like Protease: An in Silico Study;oncogenic Signaling Pathways in Mucopolysaccharidoses;a Brief History of bioinformatics Told by Data Visualization;analyzing Switch Regions of Human Rab10 by Molecular Dynamics Simulations;Redundancy Treatment of NGS Contigs in Microbial Genome Finishing with Hashing-Based Approach;A Systems biology Driven Approach to Map the EP300 Interactors Using Comprehensive Protein Interaction Network;A Simplified Complex Network-Based Approach to mRNA and ncRNA Transcript Classification;Anti-CD3 Stimulated T Cell Transcriptome Reveals Novel ncRNAs and Correlates with a Suppressive Profile;polysome-seq as a Measure of Translational Profile from Deoxyhypusine Synthase Mutant in Saccharomyces cerevisiae;a Scientometric Overview of bioinformatics Tools in the Pseudomonas Putida Genome Study;imTeNet: Image-Text Classification Network for Abnormality Detection and Automatic Reporting on Musculoskeletal Radiographs;center Genome with Respect to the Rank Distance;heuristics for Breakpoint Graph Decomposition with Applications in Genome Rearrangement Problems;sorting by Reversals and Transpositions with Proportion Restriction;experiencing DfAnalyzer for Runtime Analysis of Phylogenomic Dataflows;efficient Out-of-Core Contig Generation;importance of Meta-analysis in Studies Involving Plant Responses to Climate Change in Brazil;computational Simulations for Cyclizations Catalyzed by Plant Monoterpene Synthases;preface;a Classification of de Bruijn Graph Approaches for De Novo Fragment Assembly.

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Deep Learning Benchmarks on L1000 Gene Expression Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第6期17卷 1846-1857页

作者： McDermott, Matthew B. A. Wang, Jennifer Zhao, Wen-Ning Sheridan, Steven D. Szolovits, Peter Kohane, Isaac Haggarty, Stephen J. Perlis, Roy H. MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Massachusetts Gen Hosp Ctr Quantitat Hlth Boston MA 02114 USA Massachusetts Gen Hosp Chem Neurobiol Lab Boston MA 02114 USA Harvard Univ Dept Biomed Informat Cambridge MA 02138 USA

Gene expression data can offer deep, physiological insights beyond the static coding of the genome alone. We believe that realizing this potential requires specialized, high-capacity machine learning methods capable of using underlying biological structure, but the development of such models is hampered by the lack of published benchmark tasks and well characterized baselines. In this work, we establish such benchmarks and baselines by profiling many classifiers against biologically motivated tasks on two curated views of a large, public gene expression dataset (the LINCS corpus) and one privately produced dataset. We provide these two curated views of the public LINCS dataset and our benchmark tasks to enable direct comparisons to future methodological work and help spur deep learning method development on this modality. In addition to profiling a battery of traditional classifiers, including linear models, random forests, decision trees, K nearest neighbor (KNN) classifiers, and feed-forward artificial neural networks (FF-ANNs), we also test a method novel to this data modality: graph convolugtional neural networks (GCNNs), which allow us to incorporate prior biological domain knowledge. We find that GCNNs can be highly performant, with large datasets, whereas FF-ANNs consistently perform well. Non-neural classifiers are dominated by linear models and KNN classifiers.

关键词： Gene expression Deep learning Benchmark testing Data models Biological system modeling Task analysis Deep learning gene expression data benchmarks machine learning model development

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A computational Model to Analyse E/I (Excitation/Inhibition) Dynamics for Neural Network Integrated with Astrocyte

A Computational Model to Analyse E/I (Excitation/Inhibition)...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Anirban Bandyopadhyay Gaurav Sharma Shubhajit Roy Chowdhury School of Basic science Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Computing and Electrical Engineering Indian Institute of Technology Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group Mandi India

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Excitatory glutamate and inhibitory GABA dynamics homeostasis, which is often designated as E/I ratio, is regulated by different types of neural inhibitory circuit system. Gliotransmitter released from astrocyte coupled with two main types of the neural network system, feedback and feedforward inhibitory network can render alteration in E/I dynamics. The recent study provides a new model to study the E/I dynamics by integrating the neural circuit with astrocyte, aiming to understand the effect of input synaptic current ranging 20 -40μA/cm 2 , the effect of two different types of neural inhibition networks on the E/I dynamics and the effect of information transmission from astrocyte to the neural network with different coupling parameters on the E/I dynamics. The simulated result reveals that the frequency of E/I peak and the maximum peak height is increased with increasing the input current. The maximum E/I peak is diminished in the case of feedback inhibition system than the feedforward system with the constant input current. Another important outcome of the model is that the E/I ratio is dependent on the astrocytic glutamate with a limiting coupling parameter value.

关键词： Mathematical model Neurons Feedforward systems Biological neural networks Membrane potentials Information processing IP networks

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Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients

Using Genetic Programming to Investigate a Novel Model of Re...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： James Alexander Hughes Ryan E. R. Reid Sheridan Houghten Ross E. Andersen St. Francis Xavier University Computer Science Antigonish NS Canada St. Francis Xavier University Human Kinetics Antigonish NS Canada Brock University Computer Science St. Catharines ON Canada McGill University Kinesiology & Physical Education Montreal PQ Canada

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals - not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological *** and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance.A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.

关键词： Data models Mathematical model Predictive models computational modeling Surgery Analytical models Testing

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Control of Type 1 Diabetes Mellitus using Particle Swarm Optimization driven Receding Horizon Controller 11th

Control of Type 1 Diabetes Mellitus using Particle Swarm Opt...

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11th IFAC symposium on Biological and Medical Systems (BMS)

作者： Siket, Mate Novak, Kamilla Redjimi, Hemza Tar, Jozsef Kovacs, Levente Eigner, Gyorgy Obuda Univ Physiol Controls Res Ctr Becsi St 96-B H-1034 Budapest Hungary ELKH SZTAKI POB 63 H-1518 Budapest Hungary Obuda Univ Bejczy Antal Ctr Intelligent Robot Becsi St 96-B H-1034 Budapest Hungary Obuda Univ Biomat & Appl Artificial Intelligence Inst Becsi St 96-B H-1034 Budapest Hungary Obuda Univ Appl Informat & Appl Math Doctoral Sch Becsi St 96-B H-1034 Budapest Hungary

Receding Horizon Control (RHC), also known as Model Predictive Control (MPC) is one of the most intensively researched areas of control algorithms applied in the artificial pancreas concept. Nevertheless, MPC algorithms have not yet been implemented in commercially available insulin pumps, mainly due to their high computational demand, their less robust nature, and their instability on account of model's uncertainty. In this paper, we present a robust adjustable RHC. The proposed RHC controller was tested under known food inputs by applying a high degree of parameter uncertainty to the virtual patient implemented in the controller to test the robustness of the architecture. A particle swarm optimization method was applied to tune the controller. The so-called identifiable virtual patient (IVP) model was used in the tests, supplemented with food absorption and continuous glucose monitoring sensor model. The implementation was performed in Julia. The results showed that the proposed RHC is sufficiently robust under high food intake and parameter uncertainty. Copyright (C) 2021 The Authors.

关键词： Model predictive control of hybrid systems Optimal control of hybrid systems Control in system biology Nonlinear predictive control Control of physiological and clinical variables Type 1 Diabetes Mellitus Receding horizon control

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Natural Language Processing for EHR-Based computational Phenotyping

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第1期16卷 139-153页

作者： Zeng, Zexian Deng, Yu Li, Xiaoyu Naumann, Tristan Luo, Yuan Northwestern Univ Feinberg Sch Med Dept Prevent Med Chicago IL 60611 USA Harvard Univ Harvard TH Chan Sch Publ Hlth Dept Social & Behav Sci Boston MA 02115 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.

关键词： Electronic health records natural language processing computational phenotyping machine learning

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Parameter Estimation of Essential Amino Acids in Arabidopsis thaliana Using Hybrid of Bees Algorithm and Harmony Search 12th

Parameter Estimation of Essential Amino Acids in Arabidopsis...

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12th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Aw, Mei Yee Mohamad, Mohd Saberi Chong, Chuii Khim Deris, Safaai Remli, Muhammad Akmal Ismail, Mohd Arfian Manuel Corchado, Juan Omatu, Sigeru Univ Teknol Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Inst Artificial Intelligence & Big Data City Campus Kota Baharu 16100 Kelantan Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli CampusLock Bag 100 Jeli 17600 Kelantan Malaysia Univ Malaysia Pahang Soft Comp & Intelligent Syst Res Grp Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia Univ Salamanca Biomed Res Inst Salamanca BISITE Res Grp Salamanca Spain Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan

ISBN: (纸本)9783319987026;9783319987019

Mathematical models of metabolic processes are the cornerstone of computational systems biology. In model building, the task of parameter estimation is difficult due to the huge numbers of kinetics parameters involved. The common way of estimating the parameters is to formulate it as an optimization problem. Global optimization methods can be applied by minimizing the distance between experimental data and predicted models. This paper proposes the Hybrid of Bees Algorithm and Harmony Search (BAHS) to estimate the kinetics parameters of essential amino acid production in the aspartate metabolism for Arabidopsis thaliana. The performance of the BAHS is evaluated and compared with other algorithms. The results show that BAHS performed better as it improved the performance of the original BA by 60%. Meanwhile, it takes less computational time to estimate the kinetics parameters of essential amino acid production for Arabidopsis thaliana.

关键词： Systems biology Parameter estimation Bees Algorithm Harmony Search computational intelligence Arabidopsis thaliana

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Towards a Science Gateway for bioinformatics: Experiences in the Brazilian System of High Performance Computing 19

Towards a Science Gateway for Bioinformatics: Experiences in...

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19th Annual ieee/ACM International symposium on Cluster, Cloud, and Grid Computing (CCGRID)

作者： Ocana, Kary Galheigo, Marcelo Osthoff, Carla Gadelha, Luiz Gomes, Antonio Tadeu A. de Oliveira, Daniel Porto, Fabio Vasconcelos, Ana Tereza Natl Lab Sci Comp Petropolis Brazil

ISBN: (纸本)9781728109121

Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.

关键词： science gateway bioinformatics high performance computing

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Prospecting epilepsy surgery outcome using virtual resection paradigm. computational-model validation

Prospecting epilepsy surgery outcome using virtual resection...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Stiliyan Kalitzin George Petkov Matteo Demuru Guido Widman Stichting Epilepsie Instellingen Nederland (SEIN) The Netherlands

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Epilepsy surgery still lacks an operational method for finding the epileptogenic zone (EZ) or the minimal amount of tissue that has to be resected in order to leave the patient seizure-free. Here we propose a method for predicting the result of a resection from data collected before the eventual resection, therefore allowing an optimal surgical planning. Our major hypothesis is that focal generalized epilepsies are caused by sub-systems with excessive afferent connectivity to and from the rest of the neuronal tissue. To address the issue of delineating the EZ before the actual resection, we propose a paradigm performing "virtual surgery" on the matrix of connectivity between local EEG measurements. The virtual resection removes not only the nodes covered by the suspected EZ and their connections but also subtracts the influence of these nodes on the rest of connectivity. The residual connectivity is then compared to the original one and a significant decrease indicate that the resection contains the EZ or at least large part of it. We tested this approach on a computational model of spatially distributed bi-stable units that provides a generic model of focal epileptic neuronal system. In the modelled cases of epileptic system with spreading seizures, we found that the removal of the EZ can be predicted by significant decrease of the residual connectivity after performing virtual resection. This decrease commensurate with the increase of the epileptic threshold (the ground truth) . The method also predicts the actual change of connectivity after removing the nodes from the model dynamics. In addition we tested our techniques against the "naïve" virtual resection which is based on simply removing the corresponding nodes from the connectivity measure. The findings in this work can be exploited to increase the efficiency and accuracy of pre-surgical epileptogenic zone localization in cases of focal epileptic seizure onsets.

关键词： Mathematical model Epilepsy Brain modeling Surgery computational modeling Analytical models Predictive models

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Phenotype Prediction of DNA Sequence Data: A Machine- and Statistical Learning Approach

Phenotype Prediction of DNA Sequence Data: A Machine- and St...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Darlington Mapiye Mpho Mokoatle Dineo Makoro Stephanie J. Müller Gciniwe S. Dlamini James Mashiyane Lavina Joseph Nazir Ismail Shaheed Vally Omar IBM Research Johannesburg South Africa The national institute for communicable diseases (NICD) Johannesburg South Africa

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Advancements made in high-throughput sequencing technologies have continued to generate large amounts of sequencing data enabling the holistic investigation of complex biological phenomena. Genomic sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid expansion in available sequence data poses a tremendous computational challenge, calling for the development of novel data processing and analytic methods, as well as computing resources to match the volume of these datasets. In this work, a machine- and statistical learning approach for classification based on k-mer representations of DNA sequence data is proposed. While targeted sequencing focuses on a specific region of interest, whole genome sequencing enables a view of a species' entire genome. Thus, the approach is tested using whole genome sequences of Mycobacterium tuberculosis isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, and (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate. Furthermore, the computing challenges associated with whole genome sequence data analyses in producing interpretable and explainable insights are described. Classification models were trained using 104 Mycobacterium tuberculosis isolates. Cluster analyses showed that k-mers can be used to discriminate phenotypes and the discrimination becomes more concise as the k-mer size increases. The best performing classification model had a k-mer size of 10 (longest k-mer considered in this study) an accuracy, recall, precision, specificity, and Matthews Correlation coefficient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4, respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype pre

关键词： Genomics bioinformatics Sequential analysis DNA Statistical learning Microorganisms Data mining

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