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Behavioral Predictive Modeling in Economics
This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions. This is an important issue, since traditional economic models assumed that people make wise economic decisions based on a detailed rational analysis of all the relevant aspects. However, in reality – as Nobel Prize-winning research has shown – people have a limited ability to process information and, as a result, their decisions are not always optimal. Discussing the need for prediction-oriented statistical techniques, since many statistical methods currently used in economics focus more on model fitting and do not always lead to good predictions, the book is a valuable resource for researchers and students interested in the latest results and challenges and for practitioners wanting to learn how to use state-of-the-art techniques.
Regulatory microRNA
This book includes updated information about microRNA regulation, for example, in the fields of circular RNAs, multiomics analysis, biomarkers and oncogenes. The variety of topics included in this book reaffirms the extent to which microRNA regulation affects biological processes. Although microRNAs are not translated to proteins, their importance for biological processes is not less than proteins. An understanding of their roles in various biological processes is critical to understanding gene function in these biological processes. Although non-coding RNAs other than microRNAs have recently come under investigation, microRNA still remains the front runner as the subject of genetic and biological studies. In reading the collection of papers, readers can grasp the most updated information regarding microRNA regulation, which will continue to be an important topic in genetics and biology.
Case Reports in Multiple Sclerosis and Neuroimaging, volume III - 2023
This Research Topic aims to collect all the Case Reports submitted to the Multiple Sclerosis and Neuroimmunology. All the Case Reports submitted to this collection will be personally assessed by a senior Associate Editor before the beginning of the peer-review process. Please make sure your article adheres to the following guidelines before submitting it. Case Reports highlight unique cases of patients that present with an unexpected diagnosis, treatment outcome, or clinical course. Only Case Reports that are original and significantly advance the field will be considered: 1) RARE case with TYPICAL features. 2) FREQUENT case with ATYPICAL features. 3) Cases with a convincing response to new treatments, i.e. single case of off-label use.
Multiple Sclerosis and Related Disorders: Challenges and Approaches to Mechanisms, Biomarkers, and Therapeutic Targets
Multiple sclerosis (MS) is an autoimmune condition that causes inflammatory damage to the central nervous system. The pathologic features are diffuse and focal inflammation, demyelination, gliosis, and neuronal damage in the optic nerves, brain, and spinal cord. Multiple sclerosis remains a challenging and disabling disease. There is a greater understanding of the underlying genetic and environmental factors driving the disorder, including low vitamin D levels, smoking, and obesity. Early and accurate diagnosis is essential and needs to be supported by diagnostic criteria, including imaging and spinal fluid abnormalities in patients with clinically isolated syndromes. Other demyelinating diseases are neuromyelitis optica spectrum disorders (NMOSD), anti-myelin oligodendrocyte glycoprotein antibody disease (MOGAD), and acute disseminated encephalomyelitis (ADEM).
Unsupervised Feature Extraction Applied to Bioinformatics
This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.
