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29th Annual International conference of the ieee Engineering in Medicine and biology Society (EMBS 2007), vol.12

作者： Kostas P. Exarchos Themis P. Exarchos Costas Papaloukas Anastassios N. Troganis Dimitrios I. Fotiadis Medical Technology and Intelligent Information Systems Medical Technology and Intelligent Information Systems Department of University of Ioannina (UoI) Ioannina Greece Department of Biological Applications and Technology University of Ioannina (UoI) Ioannina Greece

Distinguishing cis peptide bonds from trans isomers in protein sequences facilitates the exploration of protein structures and functions. In this study, we evaluated the effect of a large and informative feature vector, towards the reliable prediction of peptide bond conformation between any two amino acids. We used multiple sequence alignment, secondary structure information, real valued solvent accessibility predictions for each amino acid and physicochemical properties of the surrounding residues. A three stage schema was developed, comprising of feature extraction, feature selection and peptide bond classification between any two amino acids. We also explored the performance achieved when using the full feature vector without performing feature selection. The best discriminating ability was achieved using a Naive Bayes classifier, combined with wrapper feature selection. The proposed approach yielded prediction accuracy 86%, sensitivity 82% and specificity 90% in discriminating cis and trans peptide bond conformations.

关键词： Peptides Bonding computational intelligence Society Amino acids Proteins Sequences Support vector machines Biochemistry Alzheimer's disease Feature extraction

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ieee Access Special Section Editorial: Scalable Deep Learning for Big Data

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ieee ACCESS 2020年 8卷 216617-216622页

作者： Han, Liangxiu Zhang, Daoqiang Rana, Omer Pan, Yi Jabbar, Sohail Yousif, Mazin Aloqaily, Moayad Manchester Metropolitan Univ Dept Comp & Math Manchester M1 5GD Lancs England Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA Wales Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Univ Faisalabad Dept Computat Sci Faisalabad 38000 Pakistan DXC Technol Tysons VA 22102 USA Al Ain Univ Fac Engn Al Ain U Arab Emirates

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Prediction of directly regulated genes of transcription factors important for ascidian early development

Prediction of directly regulated genes of transcription fact...

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The International conference on computational and System biology 2009(ieee计算生物与系统生物国际会议)(iCCSB2009)

作者： Xuyang Yuan Atsushi Kubo Yutaka Satou Kenta Nakai Institute for Bioinformatics Research and Development(BIRD) Japan Science and Technology Agency(JST)Kawaguchi 332-0012Japan Department of Computer Science Graduate School of Information Science and TechnologyUniversity of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8656Japan Human Genome Center Institute of Medical ScienceUniversity of Tokyo4-6-1 ShirokanedaiMinato-kuTokyo108-8639Japan Human Genome Center Institute of Medical ScienceUniversity of Tokyo4-6-1 ShirokanedaiMinato-kuTokyo108-8639Japan Department of ZoologyGraduate School of ScienceKyoto UniversityKitashirakawa-Oiwake-choSakyo-kuKyoto 606-8502Japan Department of Computer ScienceGraduate School of Information Science and TechnologyUniversity of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8656Japan

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An efficient method to determine normal values of signal averaged ECG in healthy children

An efficient method to determine normal values of signal ave...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： A. Duhamel P. Devos G. Vaksmann R. Beuscart Faculté de Médecine CERIM Lille France Service deCardiologie infantile Hopital Cardiologique-CHRU de Lille Lille France

The development of exploration methods provides physicians with many tests which can be used to settle a reliable diagnosis. To evaluate the individual diagnostic value of a new test, it is necessary to assess the normal reference values to ascertain the sensitivity and the specificity of the method. For this purpose confidence intervals (CI) of the parameter are computed from a large healthy population. However, to compute the CIs, the distribution function of the parameter must be determined. Usually, CIs are computed assuming the normality of the distribution. When this assumption is not valid, the results can be strongly biased. To compute reliable CIs, the authors propose a methodology based on the modeling of the density function. This methodology uses the following steps: 1) estimating the density function; 2) deriving a model of the density function; 3) checking the validity of the model; 4) computing CIs using the theoretical density function. Signal averaged electrocardiography (SA ECG) is a new technique which shows promise for detection of children at risk for ventricular tachycardia (VT). However, normal values have not been established in a large paediatric population for the moment. This methodology has been used to compute CIs of SA ECG parameters in 530 healthy children. These computed CIs show that SA ECG can be used as a screening test of children at risk for VT.< >

关键词： computational intelligence Society Density functional theory Electrocardiography Pediatrics Testing Distributed computing Distribution functions

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Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework

Novel Architecture for supporting medical decision making of...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Elpiniki Papageorgiou Chrysostomos Stylios Peter Groumpos Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology TEI Epirus Greece

Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy cognitive maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.

关键词： Decision making Fuzzy cognitive maps Medical diagnostic imaging Data mining Decision support systems Learning systems computational intelligence Computer networks Diseases Spatial databases

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Assessments of variability in cortical and subcortical measurements and within-network connectivity of the brain using test-retest data

Assessments of variability in cortical and subcortical measu...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Hosna Tavakoli Reza Rostami Mohammad-Reza Nazem-Zadeh Computational and Artificial Intelligence Department Institute of Cognitive Science Studies Tehran Iran Department of Psychology University of Tehran Iran Research Center for Molecular and Cellular Imaging Advanced Medical Technologies and Equipment Institute (AMTEI) Tehran University of Medical Sciences Tehran Iran Department of Neuroscience Monash University Melbourne VIC Australia

The evaluation and diagnosis of structural changes in brain caused by disease or treatment over time has become one of the important applications of medical imaging methods, in particular MRI, and it is growing. It is critical to evaluate the reliability of the changes in measurements observed in an individual patient for any clinical decision-making. In this paper, we calculated the repeatability coefficient (RC) as a measure of uncertainty for MRI measurements of subcortical volumes and cortical thickness, and within-network connectivity using test-retest data of 20 healthy subjects. We also evaluated changes in 13 patients who received 20 sessions of transcranial magnetic stimulation as a treatment. The most reliable measure seems to be in the thickness of the left occipital with RC% of 3.5 and the least reliable measure is the brain connectivity within visual network using Yeo’s atlas with RC% of 29.4. The most sensitive measure to the percentage of true changes in treated patients is the connectivity within subcortical network of AAL with 76.9%.Clinical Relevance— The results of this study can be useful for evaluating changes in the gray matter structures or functional connectivity of the brain due to a neurological disease such as Alzheimer's or Parkinson's. Also, the obtained results can be used to evaluate the changes caused by any intervention or treatment that may have any positive or negative effect on the brain.

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PCA criterion for SVM (MLP) classifier for flavivirus biomarker from salivary SERS spectra at febrile stage

PCA criterion for SVM (MLP) classifier for flavivirus biomar...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： A. R. M. Radzol Khuan Y. Lee W. Mansor I. S. Omar Faculty of Electrical Engineering Universiti Teknologi MARA Computational Intelligence Detection RIG Universiti Teknologi MARA Selangor DE Malaysia

Non-structural protein (NS1) has been conceded as one of the biomarkers for flavivirus that causes diseases with life threatening consequences. NS1 is an antigen that allows detection of the illness at febrile stage, mostly from blood samples currently. Our work here intends to define an optimum model for PCA-SVM with MLP kernel for classification of flavivirus biomarker, NS1 molecule, from SERS spectra of saliva, which to the best of our knowledge has never been explored. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Input vector to classifier determined by each PCA criterion is subjected to brute force tuning of MLP parameters for entirety. Its performance is also compared to our previous works where a Linear and RBF kernel are used. It is found that the best PCA-SVM (MLP) model can be defined by 5 PCs from Cattel's Scree test for PCA, together with P1 and P2 values of 0.1 and -0.2 respectively, with a classification performance of [96.9%, 93.8%, 100.0%].

关键词： Kernel Principal component analysis Support vector machines Diseases Tuning Biological system modeling Mathematical model

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An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions

An Evaluation of Hybrid Deep Learning Models for Classifying...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Zilu Wang Ian Daly Junhua Li School of Computer Science and Electronic Engineering University of Essex Colchester UK Laboratory for Brain-Bionic Intelligence and Computational Neuroscience Wuyi University Jiangmen China

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.

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A Clinical Interpretable Approach Applied to Cardiovascular Risk Assessment

A Clinical Interpretable Approach Applied to Cardiovascular ...

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： S. Paredes J. Henriques T. Rocha D. Mendes P. Carvalho J. Morais A. Bianchi V. Salcedo Coimbra Polytechnic - ISEC Coimbra Portugal CISUC Center for Informatics and Systems of University of Coimbra University of Coimbra Cardiology Department Leiria Hospital Centre Portugal Politecnico di Milano Milano Lombardia IT Universitat Politecnica de Valencia Valencia Valenciana ES

The effectiveness of predictive models in supporting the Clinical Decision is closely related with their clinical interpretability, i.e. the model should provide clear information on how to reach a specific classification/decision. In fact, the development of interpretable and accurate predictive models assumes a key importance as these tools can be very useful in Clinical Decision Support Systems (CDSS). The development of those models may comprise two main perspectives;existent clinical knowledge (clinical expert knowledge, clinical guidelines, current models, etc.) as well as data driven approaches able to extract (new) knowledge from recent clinical datasets. This work focuses in knowledge extraction from recent datasets (data driven) based on computational intelligence techniques. The main hypothesis that supports this work is that individuals with similar characteristics present a similar risk profile. Thus, this work addresses the development of stratification models able to learn distinct groups (or classes) of subjects assessing the similarity between characterizing variables. In particular, in the current study a data-driven supervised cluster approach is proposed aiming the derivation of meaningful rules directly from the dataset. The validation was performed based on the largest Portuguese coronary artery disease patient's dataset, provided by the Portuguese Society of Cardiology and comprising 13902 acute coronary syndrome patients. The goal was to assess the risk of death 30 days after admission. The models' performance was assessed through the sensitivity, specificity and geometric mean values. The obtained results show the potential of this approach, as they represent an acceptable performance (GM = 72%) while the clinical interpretability of the model is assured through the derived rules. Despite the achieved results, there are several research directions to be followed in order to enhance this work.

关键词： Predictive models Clustering algorithms Mathematical model Partitioning algorithms computational modeling Data mining Cardiology

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Mining and Predicting CpG islands

Mining and Predicting CpG islands

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ieee International conference on Fuzzy Systems (FUZZ-ieee)

作者： Christopher Previti Oscar Harari Coral del Val Department of Molecular Biophysics(B020) German Cancer Research Center Heidelberg Germany Departamento de Ciencias de la Computación e Inteligencia Artificial Escuela Tècnica Superior de Ingeniería Informática C/. Daniel Saucedo Aranda s/n Granada Spain

A DNA sequence can be described as a string composed of four symbols: A, T, C and G. Each symbol represents a chemically distinct nucleotide molecule. Combinations of two nucleotides are called dinucleotides and CpG islands represent regions of a DNA sequence, certain substrings, which are enriched in CpG dinucleotides (C followed by G). CpG islands represent a prominent and enigmatic feature of vertebrate genomes. They are associated with the promoters of more than 60% of all human genes and represent a critical target for transcriptional control. Methylation of these CpG islands leads to structural changes in the DNA that stops the expression of any associated gene (gene-silencing). The factors that provoke or impede methylation are currently all but unknown. In general, the maintenance of a particular pattern of methylated CpG dinucleotides represents a critical regulatory system during a host of normal developmental processes, but the erroneous methylation of CpG islands and the resulting gene-silencing can lead to the development of cancer. In this work, we present a novel unsupervised machine learning method that is capable of distinguishing biologically significant classes of CpG islands, including the separation of methylated and unmethylated CpG islands. This method represents an important novel approach that will aid in the computational prediction of methylation, which is commonly used in the pre-selection of worthwhile sequences for methylation experiments.

关键词： DNA Sequences Chemicals Genomics bioinformatics Humans Impedance Cancer Learning systems biology computing

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