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computational grid-based 3-tier ART1 data mining for bioinformatics applications

3rd International conference on Fuzzy Systems and Knowledge Discovery

作者： Cho, Kyu Cheol Park, Da Hye Lee, Jong Sik Inha Univ Sch Comp Sci & Engn Nam Ku Inchon 402751 South Korea

ISBN: (纸本)3540459162

computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets.

关键词： Data mining

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Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic Esophagitis Dietary Treatment Assignment 20

Harnessing Digital Pathology And Causal Learning To Improve ...

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20th ieee conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Technion Fac Med & Ind Engn Dept Physiol Biophys & Syst Biol Haifa Israel Technion Fac Med & Comp Sci Dept Physiol Biophys & Syst Biol Haifa Israel Univ Cincinnati Coll Med Dept Pediat Cincinnati Childrens Hosp Med CtrDiv Allergy & I Cincinnati OH USA Univ Cincinnati Coll Med Div Pathol & Lab Med Cincinnati Childrens Hosp Med CtrDept Pediat Cincinnati OH USA Technion Dept Physiol Biophys & Syst Biol Fac Med Haifa Israel

ISBN: (纸本)9798350310177

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. Diagnosis of EoE heavily relies on counting eosinophils in histological slides, a manual, laborious, time-consuming task that limits the ability to extract complex patient-dependent features. The treatment for EoE typically involves a combination of medication and dietary changes, particularly food elimination. A personalized, tailor-made food elimination plan is crucial for the patient's engagement and treatment efficiency. Previous attempts to predict the best food-elimination strategy did not yield significant results. In this work, on the one hand, we utilize AI for inferring many histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the "Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study" (SOFEED), where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This work demonstrates the importance of AI in examining the distribution of molecular features within hist

关键词： Machine Learning Causal Learning Eosinophilic Esophagitis Personalized Medicine Treatment Assignment Clinical Decision Support Artificial intelligence

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computational identification of two potential ncRNAs from 48 esophageal carcinoma-related sequences

Computational identification of two potential ncRNAs from 48...

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2nd International conference on bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Wu, Bingli Xu, Liyan Ying, Xiaomin Niu, Yongdong Li, Wuju Li, Enmin Biochemistry and Molecular Biology Department Medical College of Shantou University Shantou China Department of Tumor Pathology Medical College of Shantou University Shantou China Center of Computational Biology Beijing Institute of Basic Medical Sciences Beijilng China

ISBN: (纸本)9781424417483

Forty-eight esophageal carcinoma-related sequences were analyzed by various bioinformatics tools. The results indicated that 46 sequences were holomogous to the known genes with detail functional annotations. However, two sequences did not have holomogy to all known genes. Further analysis implied that these two sequences might be noncoding RNAs (ncRNA), and one of them located in the human mitochondria. Finally, the mechanisms of their functions were discussed. © 2008 ieee.

关键词： Genes

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A deep bidirectional long short-term memory approach applied to the protein secondary structure prediction problem

A deep bidirectional long short-term memory approach applied...

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2017 ieee Latin American conference on computational intelligence, LA-CCI 2017

作者： Hattori, Leandro Takeshi Benítez, César Manuel Vargas Lopes, Heitor Silvério Bioinformatics and Computational Intelligence Laboratory Federal University of Technology-Paraná Curitiba Brazil

ISBN: (纸本)9781538637340

One of the most important open problems in science is the protein secondary structures prediction from the protein sequence of amino acids. This work presents an application of Deep Recurrent Neural Network with Bidirectional Long Short-Term Memory (DBLSTM) cells to this problem. We compare the performance of the proposed approach with the state-of-the-art approaches. Despite the lower complexity of the proposed approach (i.e. Neural Network architecture with fewer neurons), results showed that the DBLSTM could achieve a satisfactory level of accuracy when compared with the state-of-the-art approaches. We also studied the behavior of Gradient Optimizers applied to the DBLSTM. Furthermore, this paper concentrates on well-known quantitative analytical methods applied to evaluate the proposed approach. © 2017 ieee.

关键词： Long short-term memory

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MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第1期21卷 84-94页

作者： Tang, Wenliang Deng, Zhaohong Zhou, Hanwen Zhang, Wei Hu, Fuping Choi, Kup-Sze Wang, Shitong Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there are still deficiencies for effectively mining the discriminant information for enzyme function recognition in existing methods. In this study, we present MVDINET, a novel method for multi-level enzyme function prediction. First, the initial multi-view feature data is extracted by the enzyme sequence. Then, the above initial views are fed into various deep specific network modules to learn the depth-specificity information. Further, a deep view interaction network is designed to extract the interaction information. Finally, the specificity information and interaction information are fed into a multi-view adaptively weighted classification. We compressively evaluate MVDINET on benchmark datasets and demonstrate that MVDINET is superior to existing methods.

关键词： Multi-level enzyme function prediction multi-view deep interactive learning multi-view adaptively weighted classification

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7th International conference on Practical Applications of computational biology & bioinformatics 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Mohd Saberi Mohamad Loris Nanni Miguel P. Rocha Florentino Fdez-Riverola

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An Efficient computational intelligence Technique for Classification of Protein Sequences

An Efficient Computational Intelligence Technique for Classi...

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International conference on Computer and Information Sciences (ICCOINS)

作者： Iqbal, Muhammad Javed Faye, Ibrahima Said, Abas Md Samir, Brahim Belhaouari Univ Teknol PETRONAS Dept Comp & Informat Sci Tronoh Malaysia Univ Teknol PETRONAS Dept Fundamental & Appl Sci Tronoh Malaysia Alfaisal Univ Coll Sci Riyadh Saudi Arabia

ISBN: (纸本)9781479943913

Many artificial intelligence techniques have been developed to process the constantly increasing volume of data to extract meaningful information from it. The accurate annotation of the unknown protein using the classification of the protein sequence into an existing superfamily is considered a critical and challenging task in bioinformatics and computational biology. This classification would be helpful in the analysis and modeling of unknown protein to determine their structure and function. In this paper, a frequency-based feature encoding technique has been used in the proposed framework to represent amino acids of a protein's primary sequence. The technique has considered the occurrence frequency of each amino acid in a sequence. Popular classification algorithms such as decision tree, naive Bayes, neural network, random forest and support vector machine have been employed to evaluate the effectiveness of the encoding method utilized in the proposed framework. Results have indicated that the decision tree classifier significantly shows better results in terms of classification accuracy, specificity, sensitivity, F-measure, etc. The classification accuracy of 88.7% was achieved over the Yeast protein sequence data taken from the well-known UniProtKB database.

关键词： bioinformatics Feature encoding Data mining Superfamily Protein classification

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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7th International conference on computational Methods in Systems biology, CMSB 2009

作者： Degano, Pierpaolo Gorrieri, Roberto

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An Automated Colourimetric Test by computational Chromaticity Analysis: A Case Study of Tuberculosis Test 11th

An Automated Colourimetric Test by Computational Chromaticit...

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11th International conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Tania, Marzia Hoque Lwin, K. T. AbuHassan, Kamal Bakhori, Noremylia Mohd Azmi, Umi Zulaikha Mohd Yusof, Nor Azah Hossain, M. A. Anglia Ruskin Univ Anglia Ruskin IT Res Inst ARITI Chelmsford England Univ Putra Malaysia Inst Adv Technol Serdang Malaysia Univ Putra Malaysia Dept Chem Fac Sci Serdang Malaysia

ISBN: (纸本)9783319608167;9783319608150

This paper presents an investigation into a novel approach for an automated universal colourimetric test by chromaticity analysis. This work particularly focuses on how a well-adjusted harmony between computational complexity and biochemical analysis can reduce the associated cost and unlock the limit on conventional chemical practice. The proposed research goal encompasses the potential to the criteria- anytime anywhere access, low cost, rapid detection, better sensitivity, specificity and accuracy. Our method includes obtaining the amount of colour change for each instance by delta E calculation. The system can provide the result in any ambient condition from the trajectory of colour change using Euclidean distance in LAB colour space. The strategy is verified on plasmonic ELISA based diagnosis of tuberculosis (TB). TB detection by plasmonic ELISA is a challenging, demanding and a time-consuming diagnosis. Completing the computation in real time, we circumvent the obstacle liberating the TB diagnosis in less than 15 min.

关键词： Colourimetric test Plasmonic ELISA TB test Chromatic analysis Delta E

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A Classification System for Predicting RNA Hairpin Loops

A Classification System for Predicting RNA Hairpin Loops

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International Joint conference on bioinformatics, Systems biology and Intelligent Computing

作者： Aldwairi, Monther Duwairi, Rehab Algarqaz, Wafa'a Jordan Univ Sci & Technol Dept Comp Engn Irbid Jordan

ISBN: (纸本)9780769537399

RiboNucleic Acid (RNA) molecules fold back over themselves to form secondary structures which determine the RNA's functionality in living cells. RNA secondary structure can be determined in laboratory by X-ray diffraction and nuclear magnetic resonance (NMR) techniques. However, these techniques are slow and expensive. Therefore, computational approaches are used to predict the secondary structure of RNA molecules. A new approach. RNA-SSP, for predicting RNA secondary structure elements is proposed. It combines computational approaches and machine learning classifiers to predict individual structure elements using a new search heuristic. The approach is implemented and tested for hairpin loops and a methodology for extending the approach to predict the remaining secondary structure elements is proposed. The experiments showed a significant improvement in prediction accuracy to 95% for stem regions and 80% for loops. The overall weighted-average accuracy for predicting hairpin loop sub-structure is 89% with a sensitivity of 85.29%.

关键词： bioinformatics RNA Secondary Structure Prediction computational RNA

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