Deep learning applications in translational bioinformatics.
Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep le...
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| Format: | Electronic eBook |
|---|---|
| Language: | English |
| Published: |
New York, NY :
Academic Press,
2024.
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| Subjects: | |
| Online Access: | ScienceDirect |
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| 245 | 0 | 0 | |a Deep learning applications in translational bioinformatics. |
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| 505 | 0 | |a Front Cover -- Deep Learning Applications in Translational Bioinformatics -- Copyright Page -- Contents -- List of contributors -- About the editors -- 1 Deep learning ensembles in translational bioinformatics -- 1.1 Introduction -- 1.2 Basic ensembling methods -- 1.3 Application -- 1.3.1 Application of ensemble techniques to proteomics using microarray and mass spectrometry -- 1.3.2 Gene-gene interaction detection using random forest -- 1.3.3 Prediction of regulatory elements using ensemble methods -- 1.3.3.1 Further growing uses for ensemble methods in bioinformatics -- 1.4 Extensions of the ensemble method in bioinformatics -- 1.5 Ensemble theory's application to feature selection -- 1.6 Ensemble feature selection methods and stability of feature selection algorithms in bioinformatics -- 1.6.1 Stability and feature selection algorithms -- 1.7 Algorithmic ensembles for feature selection -- 1.8 Conclusion -- References -- 2 Recursive feature elimination and multisupport vector machine in healthcare analytics -- 2.1 Introduction -- 2.1.1 Motivation of the work -- 2.1.2 Contribution of the research work -- 2.1.3 Chapter organization -- 2.2 Related works -- 2.2.1 Limitation of the existing works -- 2.3 Proposed system -- 2.3.1 Description of dataset -- 2.3.2 Dimensionality reduction and feature selection methods in medical disease diagnosis -- 2.3.2.1 Filtering techniques -- 2.3.2.2 Wrapper techniques -- 2.3.2.3 Recursive feature elimination -- 2.3.3 Classification using multisupport vector machine -- 2.4 Results and discussion -- 2.4.1 Performance used in medical disease classification -- 2.4.2 Results evaluation for recursive feature elimination calculation -- 2.5 Conclusion -- References -- 3 Sensor-enabled biomedical decision support system using deep learning and fuzzy logic -- 3.1 Introduction -- 3.1.1 Background and motivation. | |
| 505 | 8 | |a 3.1.2 Research problem statement -- 3.1.3 Objectives and contributions -- 3.1.4 Chapter organization -- 3.2 Literature review -- 3.2.1 Fuzzy logic -- 3.2.1.1 Fuzzy sets and fuzzy numbers -- 3.2.1.2 Concepts of fuzzy logic approach for research study -- 3.2.1.3 Definition of fuzzy sets along with membership function -- 3.2.2 The deep learning concept -- 3.2.2.1 Convolutional neural network -- 3.2.2.2 The concept of recurrent neural networks -- 3.2.3 Biomedical decision support system -- 3.2.3.1 Electronic health records -- 3.2.3.2 The key challenges in developing biomedical decision support system -- 3.2.4 Related work on the integration of fuzzy logic and deep learning for sensor-enabled biomedical decision support systems -- 3.3 Methodology -- 3.3.1 Data collection and preprocessing -- 3.3.2 Fuzzy logic-based feature selection -- 3.3.3 Deep learning model design and implementation -- 3.3.4 Performance evaluation metrics -- 3.4 Results from experiment -- 3.4.1 Dataset description -- 3.4.2 Performance comparison with traditional machine learning methods -- 3.4.3 Case study: prediction of cardiovascular diseases -- 3.4.3.1 Fuzzy logic-based feature selection -- 3.4.3.2 Deep learning -- 3.4.4 Case study: prediction of cardiovascular diseases using fuzzy logic and deep learning -- 3.4.4.1 Dataset description -- 3.4.4.2 Fuzzy logic-based feature selection -- 3.4.4.3 Deep learning model design and implementation -- 3.4.4.4 Performance evaluation metrics -- 3.4.4.5 Results -- 3.5 Discussion -- 3.5.1 Interpretation of results -- 3.5.2 Limitations and challenges -- 3.5.2.1 Limitations -- 3.5.2.2 Challenges -- 3.5.3 Future research directions -- 3.6 Conclusion -- References -- Further reading -- 4 Prediction of Alzheimer's disease using densely convolutional neural network -- 4.1 Introduction -- 4.2 Literature review -- 4.3 Methodology. | |
| 505 | 8 | |a 4.4 Materials and tools -- 4.4.1 Dataset -- 4.4.2 Data preprocessing -- 4.4.3 Dense convolutional neural network architecture -- 4.4.4 Hyperparameters -- 4.4.5 Evaluation -- 4.5 Results and discussion -- 4.6 Conclusion -- References -- 5 Brain tumor detection from magnetic resonance imaging images using shallow convolutional neural network -- 5.1 Introduction -- 5.2 Related works -- 5.3 Methodology -- 5.3.1 Convolutional neural network models -- 5.3.2 Proposed shallow convolutional neural network -- 5.3.2.1 The architecture -- 5.3.3 Transfer learning -- 5.3.4 Support vector machine -- 5.4 Result and discussion -- 5.5 Conclusion -- References -- 6 Multiview learning with shallow 1D-CNN for anticancer activity classification of therapeutic peptides -- 6.1 Introduction -- 6.2 Related work -- 6.3 Methodology -- 6.3.1 Convolutional neural network -- 6.3.2 The proposed 1D-convolutional neural network -- 6.3.3 Feature descriptor -- 6.3.4 Sequence-based -- 6.3.5 Standard physicochemical properties -- 6.3.6 Physicochemical properties and sequence composition -- 6.4 Experimental setup -- 6.4.1 Dataset -- 6.4.2 Result discussion -- 6.4.3 Analysis of the optimal multiview learning -- 6.5 Conclusion -- References -- 7 Deep learning methods for protein classification -- 7.1 Introduction -- 7.2 Literature survey -- 7.3 Protein representation methods -- 7.3.1 Amino-acid sequence -- 7.3.2 Substitution matrix representation -- 7.3.3 Physicochemical property response matrix -- 7.4 Proposed methodology -- 7.4.1 Dataset -- 7.4.2 Proposed classification model -- 7.4.2.1 Convolutional neural network -- 7.4.2.1.1 Input layers for 2-D CNN model -- 7.4.2.1.2 Hidden layers -- 7.4.2.1.3 Output layers -- 7.5 Result and discussion -- 7.5.1 Performace evaluation metrics -- 7.5.1.1 Accuracy -- 7.5.1.2 Precision -- 7.5.1.3 Recall. | |
| 505 | 8 | |a 7.5.2 Prediction of hemolytic and nonhemolytic activity -- 7.6 Conclusion -- References -- 8 Biosensors-based identification of antibiotic resistance in bacteria -- 8.1 Introduction -- 8.2 Gonnococal antibiotic resistance -- 8.2.1 Penicillin -- 8.2.2 Tetracycline -- 8.2.3 Ciprofloxacin -- 8.2.4 Cefixime -- 8.2.5 Ceftriaxone -- 8.2.6 Azithromycin -- 8.3 Proposed work -- 8.4 Experimentation -- 8.5 Results and discussion -- 8.5.1 Learning curve -- 8.6 Conclusion -- References -- 9 Deep learning for vehement gene expression exploration -- 9.1 Introduction -- 9.2 Gene expression -- 9.3 Gene expression analysis -- 9.4 Gene expression functioning -- 9.5 Gene expression technologies -- 9.6 Microarray technique -- 9.7 Strengths and limitations of microarray -- 9.8 Strengths of microarrays -- 9.9 Limitations of microarrays -- 9.10 Microarray functioning -- 9.11 RNA-Seq technique -- 9.12 Strengths and limitations of RNA-Seq -- 9.13 Strengths of RNA-seq -- 9.14 Limitations of RNA-seq -- 9.15 RNA-Seq functioning -- 9.16 Study on RNA-Seq over microarray -- 9.17 Deep learning -- 9.18 Deep learning sets unique from neural network -- 9.19 Deep learning sets unique from machine learning -- 9.20 Deep learning architectures: convolutional neural network and recurrent neural network -- 9.21 Convolutional neural network functioning -- 9.22 Recurrent neural networks functioning -- 9.23 Deep learning for gene expression -- 9.24 Convolutional neural networks for gene expression -- 9.25 Recurrent neural network for gene expression -- 9.26 Potential of recurrent neural network over convolutional neural network for gene expression -- 9.27 Conclusion -- References -- 10 Machine learning-enforced bioinformatics approaches for drug discovery and development -- 10.1 Introduction -- 10.2 Drug discovery: an introduction and historical perspective -- 10.3 Machine learning approaches. | |
| 505 | 8 | |a 10.4 Machine learning-enforced bioinformatic tools for drug discovery -- 10.4.1 Random forest -- 10.4.2 Artificial neural network-based approaches -- 10.4.2.1 Convolutional neural networks -- 10.4.2.2 Support vector machines -- 10.4.2.3 Multilayer perception neural network -- 10.4.2.4 Recurrent neural networks -- 10.4.3 Bayesian models -- 10.5 Deep machine learning -- 10.6 Artificial intelligence -- 10.7 Machine learning-, deep learning-, and artificial intelligence-enforced bioinformatics and cheminformatics tools -- 10.7.1 Drug design, drug prediction, and optimization -- 10.7.2 Determination of the target and validation -- 10.7.3 Prophecy of drug-drug interaction -- 10.7.4 Compound screening or hit discovery -- 10.7.5 Drug repurposing -- 10.7.6 Molecular docking -- 10.7.7 Drug safety assessment -- 10.8 Challenges and opportunities -- 10.9 Conclusion -- References -- 11 Role of deep learning in predicting drug formulations and delivery systems -- 11.1 Introduction -- 11.2 Databases, deep learning tools and techniques -- 11.2.1 Establishing the dataset -- 11.2.2 Deep learning tools and techniques -- 11.3 Data processing methods -- 11.3.1 Steps involved in data preprocessing -- 11.4 Artificial intelligence algorithms and models in dosage form development -- 11.4.1 Application of artificial intelligence in pharmaceutical drug formulations -- 11.5 Conclusions and future prospects -- References -- 12 Deep learning in computer-aided drug design: a case study -- 12.1 Introduction -- 12.1.1 Drug designing approaches -- 12.1.1.1 Structure-based drug design -- 12.1.1.2 Ligand-based drug design -- 12.1.2 Why deep learning in computer-aided drug design? -- 12.2 Role of deep learning in computer-aided drug design -- 12.3 Software tools, web servers, and package -- 12.3.1 Parameters -- 12.3.2 Tools -- 12.3.3 Packages. | |
| 520 | |a Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein. | ||
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 6 | |a Bio-informatique. |0 (CaQQLa)201-0313075 | |
| 650 | 6 | |a Apprentissage profond. |0 (CaQQLa)000311746 | |
| 650 | 7 | |a Bioinformatics |2 fast |0 (OCoLC)fst00832181 | |
| 650 | 7 | |a Deep learning (Machine learning) |2 fast |0 (OCoLC)fst02032663 | |
| 776 | 0 | 8 | |i Print version: |z 0443222991 |z 9780443222993 |w (OCoLC)1394114025 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443222993 |