Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.

Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.

"Cells are the basic units of life, and yet they are regulated by many delicate and to some extent, fragile, subcellular processes that are crucial to their survival. A simple genetic mutation could possibly clog up some important regulatory processes, or perturb the function of the product it encodes, which might ultimately bring the demise of the entire system. Therefore, it is important to gain more insights into the many control processes of cell and the regulatory factors associated with them, one prominent example of which would be the mechanism related to the RNA subcellular localization that we would focus on almost exclusively in this study from a computational perspective.RNA subcellular localization mechanism is one of the most important, yet under-appreciated, facets of the broader gene regulatory process, which helps with the cellular organization and regulation on gene expression, via transporting the RNA transcripts to their designated locations where their function, structure or translated proteins are needed. It is generally accepted as a fact that RNA trafficking mechanism is mediated between the trans-regulatory factors such as the RNA binding proteins, and the cis-acting elements — short snippets of the transcript that contain the RBP binding sites — which we call zipcode as they are considered to contain information on its address of delivery.The release of new RNA subcellular localization dataset has enabled us to build the first computational tool using state-of-the-art deep learning techniques, to predict the localization outcome for the protein-coding RNA from mere transcript sequence, and subsequently to identify the zipcode elements thereof. Our proposed method has achieved good accuracy compared to the baseline methods based on the k-mers features, despite the intrinsic difficulty that arise from the complex and stochastic interactions during trafficking events, as well as the limitations imposed by the available dataset"--

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.