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 bioDAT...
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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.
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 i...
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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
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,...
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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 Bidir...
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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 bi...
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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.
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 class...
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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 computationalbiology. 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.
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 co...
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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.
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 dif...
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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%.
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