Download or read book Cancer Subtyping Detection Using Biomarker Discovery in Multi Omics Tensor Datasets written by Farnoosh Koleini and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis begins with a thorough review of research trends from 2015 to 2022, examining the challenges and issues related to biomarker discovery in multi-omics datasets. The review covers areas of application, proposed methodologies, evaluation criteria used to assess performance, as well as limitations and drawbacks that require further investigation and improvement. This comprehensive overview serves to provide a deeper understanding of the current state of research in this field and the opportunities for future research. It will be particularly useful for those who are interested in this area of study and seeking to expand their knowledge. In the second part of this thesis, a novel methodology is proposed for the identification of significant biomarkers in a multi-omics colon cancer dataset. The integration of clinical features with biomarker discovery has the potential to facilitate the early identification of mortality risk and the development of personalized therapies for a range of diseases, including cancer and stroke. Recent advancements in "omics" technologies have opened up new avenues for researchers to identify disease biomarkers through system-level analysis. Machine learning methods, particularly those based on tensor decomposition techniques, have gained popularity due to the challenges associated with integrative analysis of multi-omics data owing to the complexity of biological systems. Despite extensive efforts towards discovering disease-associated biomolecules by analyzing data from various "omics" experiments, such as genomics, transcriptomics, and metabolomics, the poor integration of diverse forms of 'omics' data has made the integrative analysis of multi-omics data a daunting task. Our research includes ANOVA simultaneous component analysis (ASCA) and Tucker3 modeling to analyze a multivariate dataset with an underlying experimental design. By comparing the spaces spanned by different model components we showed how the two methods can be used for confirmatory analysis and provide complementary information. we demonstrated the novel use of ASCA to analyze the residuals of Tucker3 models to find the optimum one. Increasing the model complexity to more factors removed the last remaining ASCA detectable structure in the residuals. Bootstrap analysis of the core matrix values of the Tucker3 models used to check that additional triads of eigenvectors were needed to describe the remaining structure in the residuals. Also, we developed a new simple, novel strategy for aligning Tucker3 bootstrap models with the Tucker3 model of the original data so that eigenvectors of the three modes, the order of the values in the core matrix, and their algebraic signs match the original Tucker3 model without the need for complicated bookkeeping strategies or performing rotational transformations. Additionally, to avoid getting an overparameterized Tucker3 model, we used the bootstrap method to determine 95% confidence intervals of the loadings and core values. Also, important variables for classification were identified by inspection of loading confidence intervals. The experimental results obtained using the colon cancer dataset demonstrate that our proposed methodology is effective in improving the performance of biomarker discovery in a multi-omics cancer dataset. Overall, our study highlights the potential of integrating multi-omics data with machine learning methods to gain deeper insights into the complex biological mechanisms underlying cancer and other diseases. The experimental results using NIH colon cancer dataset demonstrate that the successful application of our proposed methodology in cancer subtype classification provides a foundation for further investigation into its utility in other disease areas.
Download or read book Machine Learning Methods for Multi Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
Download or read book Visualization and Integrative Analysis of Cancer Multi omics Data written by Hao Ding and published by . This book was released on 2016 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and characterizing cancer heterogeneity not only generates new mechanistic insights but can also lead to personalized treatments for patients. With advances in data generation technologies, ever-increasing amounts and types of multi-omics open great opportunities for researchers to gain extremely valuable information for cancer research and clinical biomarker discovery. However, the vast and complex nature of multi-omics data pose significant challenges regarding the extraction of useful information and the effective integration of multiple types of data. This dissertation tackles the problem of multi-omics data analysis through both visual analytics and computational angles. First, we present GRAPh based Histology Image Explorer (GRAPHIE), a visual analytics tool designed to explore, annotate, and discover potential relationships in phenomics datasets (histology images). By taking a data-driven approach, we developed an unbiased way to visualize the entire dataset with node-link graphs. The intuitive visualization and rich set of interactive functions allow users to effectively explore the dataset. While (GRAPHIE) focusing on analysising the histological information, we present the second visual analytics tool, integrative Genomic Patient Stratification explorer (iGPSe) which leverages multiple types of molecular features to further characterize patients and tumors. iGPSe is designed to assist researchers in effectively performing integrative multi-omics analysis through interactive visualization components. The tool integrates unsupervised clustering with graph and parallel sets visualization and allows a direct comparison of clinical outcomes via survival analysis. For both tools, we comprehensively analyzed the design requirements and carried out users' case studies to demonstrated the usefulness. Lastly, we developed a computational method that can jointly cluster cancer patient samples based on multi-omics data. The proposed method creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We applied our approach to a breast cancer dataset and showed that by integrating gene expression, microRNA, and DNA methylation data, the proposed method would produce potentially clinically useful subtypes of breast cancer. The proposed visual analytics tools and computational method can be extended to more generalized applications in which exploration and integration of multi-omics data are needed. This dissertation also provides high-level design considerations for visual analytics tools to conceptual methodologies in integrative analysis to future researchers and practitioners for devising effective multi-omics data analysis.
Download or read book Omics Data Integration towards Mining of Phenotype Specific Biomarkers in Cancers and Diseases written by Liang Cheng and published by Frontiers Media SA. This book was released on 2022-02-16 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Biomarker Detection Algorithms and Tools for Medical Imaging or Omic Data written by Fengfeng Zhou and published by Frontiers Media SA. This book was released on 2022-07-13 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Leveraging Machine Learning for Omics driven Biomarker Discovery written by Sheng Li and published by Frontiers Media SA. This book was released on 2023-02-14 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Identification of Multi Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi omics Data written by Chunquan Li and published by Frontiers Media SA. This book was released on 2023-03-02 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Developing Bottom up Integrated Omics Methodologies for Big Data Biomarker Discovery written by Bobak David Kechavarzi and published by . This book was released on 2020 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of highly-distributed computing compliments the proliferation of next generation sequencing (NGS) and genome-wide association studies (GWAS) datasets. These data sets are often complex, poorly annotated or require complex domain knowledge to sensibly manage. These novel datasets provide a rare, multi-dimensional omics (proteomics, transcriptomics, and genomics) view of a single sample or patient. Previously, biologists assumed a strict adherence to the central dogma: replication, transcription and translation. Recent studies in genomics and proteomics emphasize that this is not the case. We must employ big-data methodologies to not only understand the biogenesis of these molecules, but also their disruption in disease states. The Cancer Genome Atlas (TCGA) provides high-dimensional patient data and illustrates the trends that occur in expression profiles and their alteration in many complex disease states. I will ultimately create a bottom-up multi-omics approach to observe biological systems using big data techniques. I hypothesize that big data and systems biology approaches can be applied to public datasets to identify important subsets of genes in cancer phenotypes. By exploring these signatures, we can better understand the role of amplification and transcript alterations in cancer.
Download or read book Identification of Features Related to Cancer written by Juan Emmanuel Martínez Ledesma and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cancer is a complex disease characterized by the disrupted activity of several cancer related genes such as oncogenes and tumor-suppressor genes (TSG). By definition, it is expected that the expression of cancer-related genes change during tumor progression. Despite the enormous efforts made for biomarker and gene pattern discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us to understand the dynamics and complexity of tumor progression. In this thesis, I present four methodologies related to some aspects of tumor progression. The first methodology described in Chapter 3 is the improvement of particle swarm optimization (PSO) algorithm for feature selection setting parameters to restrict the number of features. Our PSO algorithm was able to provide the same or better classification accuracy that other PSO implementations but using less variables and faster. The second methodology, shown in Chapter 4, is based on the proportion of samples whose gene expression level were activated or inactivated within a tumor stage to compose expression patterns associated to tumor progression. The objective of this method is to identify the expected profile corresponding to oncogenes and TSG in both cancer and non-cancer related genes. In Chapter 5, we tested our method on prostate cancer datasets and data from The Cancer Genome Atlas (TCGA) project. Our results showed that the genes obtained are associated to well-known cancer related terms and provide models that could be used as survival biomarkers. A common clinical indicator of cancer progression is survival. Therefore, Chapter 6 explains a method to obtain network-based biomarkers using Protein- Protein Interaction (PPI) data related to survival. Besides, network-based algorithms have gained popularity recently. We used this method to elucidate biomarkers from different types of genomics data as gene expression, copy number variation (CNV), and mutation data. We applied this method to different datasets from TCGA and cancer tissues as ovary, breast, lung, and lymphoma. From the algorithmic view, we think that our method provide a creative way to explore the network space in reasonable time. Distinct studies suggest that different cancers present molecular parallels. We developed an analysis to study gene expression similarities among the cancer subtypes from 11 cancer tissues defined by TCGA. This analysis, shown in Chapter 7 used gene expression to define clusters of tumors with similar expression patterns, then we related clusters to CNV, mutations, and pathway data. This lead to the discovery of what we called Superclusters, groups of cancer subtypes with similar genomic characteristics. This project is product of a research stay at Dr. Roel Verhaak laboratory from MD Anderson Cancer Center."--descripción del autor.
Download or read book Integrative Analysis of Multi modality Data in Cancer written by Chao Wang and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Gleaning insights of highly complex, heterogeneous cancer biology requires data collected from different levels - genetic, genomic and phenotypic. There is a high degree of diversity between individuals with a wide spectrum of clinical, pathologic, and molecular features. Traditionally in clinical settings, phenotypic data such as histopathological images are often used for diagnosis, subtyping, staging, prognosis and treatment. With the advent of new high-throughput biotechnologies, multi-modality of genomics and genetic data provide extremely valuable information for cancer research and clinical biomarker discovery. However, the challenge still remains towards the determination of causal relationship in these multi-modality data and effective integration to gain better understanding of cancer biology. In particular, molecular basis of cellular phenotypes manifest in histopathological images are unknown and remain inexplicable. In this dissertation, I present a new analytic framework and accompanying computational methods to facilitate integrative analyses of multi-modality biomedical data. The first part of this volume describes the extraction of image features thus enabling quantitative analysis of the cellular structures. Our feature collections include texture features, previously discovered salient features and features designed to mimic the observations of a trained pathologist. In the next part, studies that establish the genotype-phenotype links using morphological features from histopathology are presented. Molecules and molecular events associated with breast cancer morphology are discovered. In the third part, beyond pairwise correlations, I explore multivariate molecular basis of lung adenocarcinoma morphology. This study suggests that a cellular structure can be potential target in treatment of lung adenocarcinoma. Finally, the last part aims to develop computational methods that can jointly cluster cancer patient samples based on multi-modality data. These effective integrative cluster methods allow patient stratification based on both essential categorical attributes and multi-dimensional data from different sources. I demonstrate the application of these methods using datasets pertaining to breast cancer. The proposed image processing workflows, the collection of morphological features, the analytical framework that links molecular expression to morphological measurements, and the integrative clustering methods show potential in revealing biological basis and new therapeutic targets of various types of cancer. The results from the studies indicate biologically interesting subtypes with potential biomarkers. The frameworks and methodologies presented in this dissertation can mine the large and complex collections of data to identify new comprehensive biomarkers generate new hypothesis.
Download or read book Learning to Classify Text Using Support Vector Machines written by Thorsten Joachims and published by Springer Science & Business Media. This book was released on 2002-04-30 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Bidirectional Gene Promoters written by Fumiaki Uchiumi and published by Elsevier. This book was released on 2022-11-25 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent studies in human genetics and in silico analyses have revealed that a number of genes are head-head orientated with other genes or non-coding RNAs. The expression of regulatory element-containing 5'-upstream regions of gene pairs are referred to as bi-directional promoters and are thought to have a key role in biological regulatory mechanisms. For example, tumor suppressor protein-encoding TP53 and BRCA1 genes are head-head bound with WRAP53 and NBR2, respectively. DNA-repair factor-encoding ATM and PRKDC (DNA-PKcs) genes have bidirectional partner NPAT and MCM4, respectively. Surveillance of the human DNA database has revealed that the numbers of DNA repair/mitochondrial function/immune response-associated genes are bound with other genes that are transcribed to opposite direction. The observations may encourage us to investigate in the molecular mechanisms how DNA repair/mitochondrial function/immune response-associated genes are regulated by bidirectional promoters. Not only protein-coding genes, but also quite a few ncRNAs, which play important roles in various cellular events, are transcribed under the regulation of the bidirectional promoters. More importantly, we know that dysregulation in the promoter activity and transcription initiation of genes might cause human diseases. - Provides an overview of the process of transcription - Explains why there so many bidirectional promoters present in human genomes - Covers how the diverse biological functions of (non-coding RNAs) ncRNAs are controlled
Download or read book Deep Learning for Cancer Diagnosis written by Utku Kose and published by Springer Nature. This book was released on 2020-09-12 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Download or read book Applied Biclustering Methods for Big and High Dimensional Data Using R written by Adetayo Kasim and published by CRC Press. This book was released on 2016-10-03 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.
Download or read book Deep Learning for Biomedical Data Analysis written by Mourad Elloumi and published by Springer Nature. This book was released on 2021-07-13 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Download or read book Medical Imaging Informatics written by Alex A.T. Bui and published by Springer Science & Business Media. This book was released on 2009-12-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.
Download or read book Temporomandibular Disorders written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-07-01 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Temporomandibular disorders (TMDs), are a set of more than 30 health disorders associated with both the temporomandibular joints and the muscles and tissues of the jaw. TMDs have a range of causes and often co-occur with a number of overlapping medical conditions, including headaches, fibromyalgia, back pain and irritable bowel syndrome. TMDs can be transient or long-lasting and may be associated with problems that range from an occasional click of the jaw to severe chronic pain involving the entire orofacial region. Everyday activities, including eating and talking, are often difficult for people with TMDs, and many of them suffer with severe chronic pain due to this condition. Common social activities that most people take for granted, such as smiling, laughing, and kissing, can become unbearable. This dysfunction and pain, and its associated suffering, take a terrible toll on affected individuals, their families, and their friends. Individuals with TMDs often feel stigmatized and invalidated in their experiences by their family, friends, and, often, the health care community. Misjudgments and a failure to understand the nature and depths of TMDs can have severe consequences - more pain and more suffering - for individuals, their families and our society. Temporomandibular Disorders: Priorities for Research and Care calls on a number of stakeholders - across medicine, dentistry, and other fields - to improve the health and well-being of individuals with a TMD. This report addresses the current state of knowledge regarding TMD research, education and training, safety and efficacy of clinical treatments of TMDs, and burden and costs associated with TMDs. The recommendations of Temporomandibular Disorders focus on the actions that many organizations and agencies should take to improve TMD research and care and improve the overall health and well-being of individuals with a TMD.
