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检索条件"任意字段=6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015"

共 237 条记录，以下是11-20 订阅

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bioinformatics 2015 - 6th international conference on bioinformatics models, methods and algorithms, Proceedings;Part of 8th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015

BIOINFORMATICS 2015 - 6th International Conference on Bioinf...

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6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

ISBN: (纸本)9789897580703

the proceedings contain 34 papers. the topics discussed include: how to cross the border from R to D? - the example of conception of new medical devices;the bidirectional route of research and innovation - insights from the stressed brain;modeling genetical data with forests of latent trees for applications in association genetics at a large scale - which clustering method should be chosen?;essential proteins and functional modules in the host-pathogen interactions from innate to adaptive immunity - c. albicans-zebrafish infection model;identifying aging genes in the aging mouse hypothalamus using gateway node analysis of correlation networks;machine reading of biological texts - bacteria-biotope extraction;wave equation model of soft tissue for a virtual reality laparoscopy training system - a validation study;a nonlinear mixture model based unsupervised variable selection in genomics and proteomics;and approximate analysis of homeostasis of gene networks by linear temporal logic using network motifs.

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Backward Pattern Matching on Elastic Degenerate Strings 12

Backward Pattern Matching on Elastic Degenerate Strings

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14th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 12th international conference on bioinformatics models, methods and algorithms (bioinformatics)

作者： Prochazka, Petr Cvacho, Ondrej Krcal, Lubos Holub, Jan Czech Tech Univ Fac Informat Technol Dep Theoret Comp Sci Thakurova 2700-9 Prague 6 Czech Republic

ISBN: (纸本)9789897584909

Recently, the concept of Elastic Degenerate Strings (EDS) was introduced as a way of representing a sequenced population of the same species. Several on-line Elastic Degenerate String Matching (EDSM) algorithms were presented so far. Some of them provide practical implementation. We propose a new on-line EDSM algorithm BNDM-EDS. Our algorithm combines two traditional algorithms BNDM and the Shift-And that were adapted to the specifics needed by Elastic Degenerate Strings. BNDM-EDS is running in O(Nmd[m/w]) worst-case time. this implies O (Nm) time for small patterns, where m is the length of the searched pattern, N is the size of EDS, and w is the size of the computer word. the algorithm uses O (N + n) space, where n is the length of EDS. BNDM-EDS requires a simple preprocessing step with time and space O (m). Experimental results on real genomic data show superiority of BNDM-EDS over state-of-the-art algorithms.

关键词： Elastic Degenerate Strings Genomic Sequences Degenerate Pattern Matching Pan-Genomics

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Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance

Aggregating Statistically Correlated Metabolic Pathways Into...

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13th international conference on bioinformatics models, methods and algorithms (bioinformatics) held as part of 15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Basher, Abdur Rahman M. A. Hallam, Steven J. Univ British Columbia Grad Program Bioinformat Vancouver BC V5Z 4S6 Canada Univ British Columbia Dept Microbiol & Immunol Vancouver BC V6T 1Z3 Canada

ISBN: (纸本)9789897585524

Metabolic pathway prediction from genomic sequence information is an essential step in determining the capacity of living things to transform matter and energy at different levels of biological organization. A detailed and accurate pathway map enables researchers to interpret and engineer the flow of biological information from genotype to phenotype in both organismal and multi-organismal contexts. In this paper, we propose two novel hierarchical mixture models, SOAP (sparse correlated pathway group) and SPREAT (distributed sparse correlated pathway group), to improve pathway prediction outcomes. Both models leverage pathway abundance to represent an organismal genome as a mixed distribution of groups, and each group, in turn, is a mixture of pathways. Moreover, both models deal with missing potential pathways in the training set by provisioning supplementary pathways into the learning framework as part of noise reduction efforts. Because the introduction of supplementary pathways may lead to overestimation of some pathways, dual sparseness is applied. the resulting pathway group dataset is then used to train multi-label learning algorithms. Model effectiveness was evaluated on metabolic pathway prediction where correlated models, in particular, SOAP was able to equal or exceed the performance of previous pathway prediction algorithms on organismal genomes.

关键词： Pathway Group Relabeling Data Augmentation Correlated models Metabolic Pathway Prediction MetaCyc

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Dynamic Fitness Landscape Analysis 18th

Dynamic Fitness Landscape Analysis

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18th international conference on Computer Aided Systems theory, EUROCAST 2022

作者： Pitzer, Erik Werth, Bernhard Karder, Johannes Department Software Engineering University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Institute for Formal Models and Verification Johannes Kepler University Altenbergerstr 68 Linz4040 Austria

ISBN: (纸本)9783031253119

Dynamic optimization problems pose a big challenge for classic optimization algorithms. they could simply be viewed as a series of related optimization problems. In particular the aspect of time-linkage has not been well studied yet. In this work we are analyzing an artificial problem based on real-world data to elucidate the potential of fitness landscape analysis methods to discover problem difficulty and follow along the changes of dynamic problems and how these changes can be measures and might be exploited by enabling algorithm introspection. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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Efficient k-mer Indexing with Application to Mapping-free SNP Genotyping

Efficient k-mer Indexing with Application to Mapping-free SN...

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作者： Marcolin, Mattia Andreace, Francesco Comin, Matteo Univ Padua Dept Informat Engn Via Gradenigo 6-A I-35100 Padua Italy

ISBN: (纸本)9789897585524

Advances in sequencing technologies and computational methods have enabled rapid and accurate identification of genetic variants. Accurate genotype calls and allele frequency estimations are crucial for population genomics analyses. One of the most demanding step in the genotyping pipeline is mapping reads to the human reference genome. Recently mapping-free methods, like Lava and VarGeno, have been proposed for the genotyping problem. they are reported to perform 30 times faster than a standard alignment-based genotyping pipeline while achieving comparable accuracy. Moreover, these methods are able to include known genomic variants in the reference making read mapping, and genotyping variant-aware. However, in order to run they require a large k-mers database, of about 60GB, to be loaded in memory. In this paper we study the problem of genotyping using new efficient data structures based on k-mers set compression, and we present a fast mapping-free genotyping tool, named GenoLight. GenoLight reports accuracy results similar to the standard pipeline, but it is up to 8 times faster. Also, GenoLight uses between 5 to 10 times less memory than the other mapping-free tools, and it can be run on a laptop. Availability: https://***/CominLab/GenoLight.

关键词： Genotyping Mapping-free k-mers Set Representation

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Exploring Recurrent Neural Network models for Depression Detection through Facial Expressions: A Systematic Literature Review

Exploring Recurrent Neural Network Models for Depression Det...

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international conference of Computer and Informatics Engineering (IC2IE)

作者： Brilyan Nathanael Rumahorbo Bens Pardamean Gregorius Natanael Elwirehardja Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. therefore, researchers have turned to deep learning models to explore the detection of depression. the objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. this Systematic Literature Review found that facial expressions do have relevant patterns related to MDD.

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A Comparison of Covariate Shift Detection methods on Medical Datasets 18th

A Comparison of Covariate Shift Detection Methods on Medical...

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18th international conference on Computer Aided Systems theory, EUROCAST 2022

作者： Dreiseitl, Stephan Department of Software Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031253119

the performance of machine learning models is known to deteriorate on datasets drawn from a different distribution from the one used in model building. In a supervised setting, this deterioration can be assessed by a decrease in the evaluation metrics of the models. When no gold standard information is available, and thus evaluation metrics cannot be determined, one may directly address the problem of detecting whether two datasets differ in distribution. methods for assessing the difference of distribution from their samples are known as covariate shift detection algorithms. We investigate the ability of the maximum mean discrepancy method, of univariate tests, and of a domain classifier trained to distinguish two datasets, to detect covariate shift in two datasets: one collected for predicting stroke, and one collected for predicting acute myocardial infarction. For this, we artificially perturb parts of the datasets, and check how well these modified datasets can be distinguished from the remaining portions of the original datasets. We observe that univariate tests compare favorably with the other two methods, that changes can be detected more easily in large datasets, that smaller changes are more difficult to detect than larger changes, and that dimensionality reduction is detrimental to detecting covariate shift. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Gene Expression Pattern Recognition Algorithm Based on Deep Learning

Gene Expression Pattern Recognition Algorithm Based on Deep ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Xinzhuo Tu Yuanyuan Li Department of Pathology Air Force Medical Center PLA Beijing China China Medical University Shenyang China Nanchong Central Hospital Nanchong China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

With the rapid development of bioinformatics, the analysis of gene expression data has become crucial for understanding life processes, disease mechanisms, and drug development. For binary classification tasks, the performance of most traditional machine learning models is highly dependent on the quality of features, which often requires researchers to have a deep professional background, and artificially constructed feature vectors may lose the original information in protein sequences. this study aims to use deep learning models to extract high-order features from gene expression data, in order to solve the problem of gene expression pattern recognition. Firstly, by analyzing the limitations of traditional biological methods in gene recognition, a new approach combining recurrent neural networks (RNNs) and Transformer models is proposed. RNN can effectively capture temporal dependencies in sequence data, while Transformer, through its multi head self-attention mechanism, can more effectively process long sequence data and capture complex dependencies in gene expression sequences. through experiments on multiple publicly available gene expression datasets, it has been demonstrated that the Transformer model outperforms the RNN model, demonstrating its superiority in feature extraction. then, in response to the problem of low efficiency in processing long sequences with the Transformer model, this study further proposes the CNN Transformer model. the model first uses one-dimensional convolutional layers to extract local features from gene expression data, and then uses Transformer layers to capture long-range dependencies between these features. the experimental results show that the CNN Transformer model exhibits higher balanced accuracy on multiple independent test sets, demonstrating the effectiveness of the model structure. Finally, this article discusses the potential application value of this method in fields such as personalized medicine and early disease dia

关键词： Deep learning Proteins Recurrent neural networks Biological system modeling Transformers Feature extraction Data models Gene expression Convolutional neural networks bioinformatics

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Classification of Heart Disease from MRI Images Using Convolutional Neural Network 6

Classification of Heart Disease from MRI Images Using Convol...

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6th IEEE international conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Sharma, Ajay Kumar, Raj Jaiswal, Varun Dept Of Biotechnology And Bioinformatics Solan India Gachon University Bokjeong-dong Sujeong-gu Dept Of Food And Neutrition Seongnam-si Gyeonggi-do Korea Republic of

ISBN: (纸本)9781665425520

In this paper, we have demonstrated a CAD architecture for heart disease based on the Convolutional Neural Network. Heart disease is one of the major problems in worldwide and killing millions of people. the computational method with the help of Deep learning has the potential to early detection the disease to save lives. Data we have collected in the form of MRI images from the online resources. After preprocessing and normalization we have trained our model with the help of the convolution neural network (CNN). the image data set is divided into the training set and validation sets obtain an accuracy of 95% which is good as compare to the other methods. the model is compared with the state-of-the-art available model like Linear, 3D Google net, Vanilla 3D CNN. the model performed better are compare to the existing models. © 2021 IEEE.

关键词： Convolutional neural networks

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Advanced Stochastic Approaches for Applied Computing in Environmental Modeling 14th

Advanced Stochastic Approaches for Applied Computing in En...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Todorov, Venelin Dimov, Ivan Ganzha, Maria Paprzycki, Marcin Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Street Block 8 Sofia1113 Bulgaria Department of Parallel Algorithms Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Street Block 25 A Sofia1113 Bulgaria Polish Academy of Sciences Warszawa Poland

ISBN: (纸本)9783031304415

Mathematical models are used to study and predict the behavior of a variety of complex systems - engineering, physical, economic, social, environmental. Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of applications. Sensitivity analysis is a modern promising technique for studying large-scale systems such as ecological systems. the uncertainty in the model input in our case, as in many others, can be due to various reasons: inaccurate measurements or calculation, approximation, data compression, etc. Two kinds of sensitivity analysis have been discussed in the literature: local and global. In the current paper the subject of our study is the global sensitivity analysis performed via the Sobol’ variance-based approach, applied to a specific large-scale air pollution model. the mathematical treatment of the problem of providing global sensitivity analysis consists in evaluating total sensitivity indices which leads to computing multidimensional integrals. We propose a new specific stochastic approach which significantly improves the results by the standard stochastic approaches. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Monte Carlo methods

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