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1. |
Record Nr. |
UNINA9910886083403321 |
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Autore |
Raza Khalid |
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Titolo |
Machine Learning in Single-Cell RNA-seq Data Analysis / / by Khalid Raza |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (xviii, 88 pages) : illustrations |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3712 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Machine learning |
Quantitative research |
Artificial Intelligence |
Machine Learning |
Data Analysis and Big Data |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1. Introduction to Single-Cell RNA-seq Data Analysis -- Chapter 2. Preprocessing and Quality Control -- Chapter 3. Dimensionality Reduction and Clustering -- Chapter 4. Differential Expression Analysis -- Chapter 5. Trajectory Inference and Cell Fate Prediction -- Chapter 6. Emerging Topics and Future Directions. |
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Sommario/riassunto |
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This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging |
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machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets. . |
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2. |
Record Nr. |
UNINA9910483689303321 |
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Autore |
Cuevas Erik |
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Titolo |
Recent Metaheuristic Computation Schemes in Engineering / / by Erik Cuevas, Alma RodrÃguez, Avelina Alejo-Reyes, Carolina Del-Valle-Soto |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (xi, 277 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-9503 ; ; 948 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Cooperating objects (Computer systems) |
Computational Intelligence |
Artificial Intelligence |
Cyber-Physical Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introductory Concepts of Metaheuristic Computation -- A Metaheuristic Scheme Based on the Hunting Model of Yellow Saddle Goatfish -- Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization and Estimation Distribution Methods -- Corner Detection Algorithm Based on Cellular Neural Networks (CNN) and Differential Evolution (DE) -- Blood Vessel Segmentation Using Differential Evolution Algorithm -- Clustering Model Based on the Human Visual System -- Metaheuristic Algorithms for Wireless Sensor Networks -- Metaheuristic Algorithms Applied to the Inventory Problem. |
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Sommario/riassunto |
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This book includes two objectives. The first goal is to present advances and developments which have proved to be effective in their application to several complex problems. The second objective is to present the performance comparison of various metaheuristic techniques when |
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they face complex optimization problems. The material has been compiled from a teaching perspective. Most of the problems in science, engineering, economics, and other areas can be translated as an optimization or a search problem. According to their characteristics, some problems can be simple that can be solved by traditional optimization methods based on mathematical analysis. However, most of the problems of practical importance in engineering represent complex scenarios so that they are very hard to be solved by using traditional approaches. Under such circumstances, metaheuristic has emerged as the best alternative to solve this kind of complex formulations. This book is primarily intended for undergraduate and postgraduate students. Engineers and application developers can also benefit from the book contents since it has been structured so that each chapter can be read independently from the others, and therefore, only potential interesting information can be quickly available for solving an industrial problem at hand. . |
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