Download or read book Unsupervised Learning Models for Unlabeled Genomic Transcriptomic Proteomic Data written by Jianing Xi and published by Frontiers Media SA. This book was released on 2022-01-05 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book In Silico Dreams written by Brian S. Hilbush and published by John Wiley & Sons. This book was released on 2021-07-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolution An outline of the fundamental breakthroughs in AI and deep learning and their applications across medicine A compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeutics A summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic development A guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.

Download or read book Machine Learning Big Data and IoT for Medical Informatics written by Pardeep Kumar and published by Academic Press. This book was released on 2021-06-13 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

Download or read book Multi Pronged Omics Technologies to Understand COVID 19 written by Sanjeeva Srivastava and published by CRC Press. This book was released on 2022-07-07 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: "COVID-19 and Omics Technologies" is a comprehensive, integrative assessment of recent information and knowledge collected on SARS-CoV-2 and COVID-19 during the pandemic based on omics technologies. It demonstrates how omics technologies could better investigate the infectious disease and propose solutions to the current concerns. The value of multi-omics technologies in understanding disease etiology and host response, discovering infection biomarkers and illness prediction, identifying vaccine candidates, discovering therapeutic targets, and tracing pathogen evolution is discussed in this book. These factors combine to make it a valuable resource to enhance understanding of both "Omics technology" and "COVID-19" as a disease. The book covers the most recent understanding of COVID-19 and the applications of cutting-edge studies, making it accessible to a large multidisciplinary readership. The book explains how high-throughput technologies and systems biology might assist to solve the pandemic’s challenges and deconstruct and appreciate the substantial contributions that omics technologies have made in predicting the path of this unforeseeable pandemic. Features: In-depth summary of clinical presentation, epidemiological impact, and long-term sequelae of COVID-19 pandemic. A systematic overview of omics-based approaches to the study of COVID-19 biology. Recent research results and some pointers to future advancements in methodologies used. Detailed examples from recent studies on COVID-19 encompassing different omics methodologies. A detailed description of methodologies and notes on the applications of state-of-the-art technologies. This book is intended for scientists who need to understand the biology of COVID-19 from the perspective of omics investigations, as well as researchers who want to employ omics-based technologies in disease biology.

Download or read book Advances in AI Based Tools for Personalized Cancer Diagnosis Prognosis and Treatment written by Israel Tojal Da Silva and published by Frontiers Media SA. This book was released on 2022-09-21 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in mathematical and computational oncology volume III written by George Bebis and published by Frontiers Media SA. This book was released on 2023-10-25 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Science AI and Machine Learning in Drug Development written by Harry Yang and published by CRC Press. This book was released on 2022-10-04 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

Download or read book Bioinformatics and Computational Biology written by Tiratha Raj Singh and published by CRC Press. This book was released on 2023-12-13 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics and Computational Biology: Technological Advancements, Applications and Opportunities is an invaluable resource for general and applied researchers who analyze biological data that is generated, at an unprecedented rate, at the global level. After careful evaluation of the requirements for current trends in bioinformatics and computational biology, it is anticipated that the book will provide an insightful resource to the academic and scientific community. Through a myriad of computational resources, algorithms, and methods, it equips readers with the confidence to both analyze biological data and estimate predictions. The book offers comprehensive coverage of the most essential and emerging topics: Cloud-based monitoring of bioinformatics multivariate data with cloud platforms Machine learning and deep learning in bioinformatics Quantum machine learning for biological applications Integrating machine learning strategies with multiomics to augment prognosis in chronic diseases Biomedical engineering Next generation sequencing techniques and applications Computational systems biology and molecular evolution While other books may touch on some of the same issues and nuances of biological data analysis, they neglect to feature bioinformatics and computational biology exclusively, and as exhaustively. This book's abundance of several subtopics related to almost all of the regulatory activities of biomolecules from where real data is being generated brings an added dimension.

Download or read book Artificial Intelligence written by and published by BoD – Books on Demand. This book was released on 2019-07-31 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.

Download or read book Deep Learning for Transcriptomics and Proteomics written by Ayse Berceste Dincer and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improvements in sequencing technologies increased the availability of omics data, such as transcriptomics and proteomics, providing information about various molecular mechanisms from complementary angles. These measurements can be key to gaining a better understanding of phenotype-genotype associations. Machine learning has great potential to capture the relevant signals from these datasets; however, the inherently complex nature of the measurements, where the signals of biological interest are entangled with technical and other biological factors, makes it difficult to apply these methods directly.Our goal in this thesis is to address the fundamental challenges associated with transcriptomics and proteomics data hindering the application of machine learning models. Specifically, we tackle (1) high dimensionality, i.e., higher number of features than samples, (2) batch effects and confounders, i.e., signals introduced by technical or biological artefacts, and (3) experimental noise and bias, i.e., inaccuracies in measurements. To solve these problems, we develop three novel deep learning approaches: DeepProfile, AD-AE, and Pepper. DeepProfile is an ensemble of unsupervised neural network models trained to learn lower dimensional embeddings, effectively reducing the dimensionality and complexity of gene expression profiles. By integrating expression profiles from different sources and adopting an interpretable framework, we generate embeddings to investigate cancer mechanisms. AD-AE disentangles the confounding sources of biological or technical variance and the biological signals of interest. Our model consists of an unsupervised neural network to learn lower dimensional embeddings and an adversarial predictor to eliminate confounders. The resulting deconfounded representations improve accuracy of downstream prediction models and can be successfully transferred across domains. Pepper focuses on proteomics measurements and aims to reduce the effects of sequence-induced bias for the accurate quantification of proteins. We incorporate our biological hypothesis into the loss functions of our neural network approach to predict and correct for sequence-induced bias. This results in reduction in quantification bias as well as an increase in the correlation between gene and protein expression. We demonstrate that each of these deep learning models can generate more informative and interpretable versions of our datasets. The resulting representations or the denoised measurements facilitate the application of machine learning techniques for the investigation of phenotypic variation and cellular mechanisms, which we hope will lead to a better understanding of underlying biology.

Download or read book DNA Methylation written by J. Jost and published by Birkhäuser. This book was released on 2013-11-11 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: The occurrence of 5-methylcytosine in DNA was first described in 1948 by Hotchkiss (see first chapter). Recognition of its possible physiologi cal role in eucaryotes was first suggested in 1964 by Srinivasan and Borek (see first chapter). Since then work in a great many laboratories has established both the ubiquity of 5-methylcytosine and the catholicity of its possible regulatory function. The explosive increase in the number of publications dealing with DNA methylation attests to its importance and makes it impossible to write a comprehensive coverage of the literature within the scope of a general review. Since the publication of the 3 most recent books dealing with the subject (DNA methylation by Razin A. , Cedar H. and Riggs A. D. , 1984 Springer Verlag; Molecular Biology of DNA methylation by Adams R. L. P. and Burdon R. H. , 1985 Springer Verlag; Nucleic Acids Methylation, UCLA Symposium suppl. 128, 1989) considerable progress both in the techniques and results has been made in the field of DNA methylation. Thus we asked several authors to write chapters dealing with aspects of DNA methyla tion in which they are experts. This book should be most useful for students, teachers as well as researchers in the field of differentiation and gene regulation. We are most grateful to all our colleagues who were willing to spend much time and effort on the publication of this book. We also want to express our gratitude to Yan Chim Jost for her help in preparing this book.

Download or read book Invertebrate Learning and Memory written by Randolf Menzel and published by Academic Press. This book was released on 2013-06-18 with total page 603 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how memories are induced and maintained is one of the major outstanding questions in modern neuroscience. This is difficult to address in the mammalian brain due to its enormous complexity, and invertebrates offer major advantages for learning and memory studies because of their relative simplicity. Many important discoveries made in invertebrates have been found to be generally applicable to higher organisms, and the overarching theme of the proposed will be to integrate information from different levels of neural organization to help generate a complete account of learning and memory. Edited by two leaders in the field, Invertebrate Learning and Memory will offer a current and comprehensive review, with chapters authored by experts in each topic. The volume will take a multidisciplinary approach, exploring behavioral, cellular, genetic, molecular, and computational investigations of memory. Coverage will include comparative cognition at the behavioral and mechanistic level, developments in concepts and methodologies that will underlie future advancements, and mechanistic examples from the most important vertebrate systems (nematodes, molluscs, and insects). Neuroscience researchers and graduate students with an interest in the neural control of cognitive behavior will benefit, as will as will those in the field of invertebrate learning. Presents an overview of invertebrate studies at the molecular / cellular / neural levels and correlates findings to mammalian behavioral investigations Linking multidisciplinary approaches allows for full understanding of how molecular changes in neurons and circuits underpin behavioral plasticity Edited work with chapters authored by leaders in the field around the globe – the broadest, most expert coverage available Comprehensive coverage synthesizes widely dispersed research, serving as one-stop shopping for comparative learning and memory researchers

Download or read book Avian Immunology written by Bernd Kaspers and published by Academic Press. This book was released on 2021-12-05 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: Avian Immunology, Third Edition contains a detailed description of the avian innate immune system, encompassing the mucosal, enteric, respiratory and reproductive systems. The diseases and disorders it covers, include immunodepressive diseases and immune evasion, autoimmune diseases, and tumors of the immune system. Practical aspects of vaccination are examined as well. Extensive appendices summarize resources for scientists including cell lines, inbred chicken lines, cytokines, chemokines, and monoclonal antibodies. With contributions from the foremost international experts in the field, Avian Immunology 3rd, provides the most up-to-date crucial information not only for poultry health professionals and avian biologists, but also for comparative and veterinary immunologists, graduate students and veterinary students with an interest in avian immunology. Avian Immunology, Third Edition, is a fascinating and growing field and surely provides new and exciting insights for mainstream immunology in the future. Reflects significant advances in the field since the second edition, particularly the explosion of knowledge on genomics including work on the chicken, turkey and zebra finch genomes Provides a single source reference ranging from the basic science to cutting edge research Provides practical information for veterinarians particularly those specialised in poultry or companion bird medicine New chapters on the impact of the microbiome on the immune system, defence mechanisms in the egg and embryo and emerging transgene technologies

Download or read book Probabilistic Machine Learning with Omics Data and Biological Prior Knowledge written by David Merrell (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern biological research requires sophisticated analysis of omics data. For the purposes of this dissertation, ``omics data'' includes many commonly-used modalities collected in biological experiments. These include genomic, transcriptomic, proteomic, epigenomic, and other kinds of data. Omics datasets can be high-dimensional and complex. For instance, RNA-seq datasets with tens of thousands of dimensions are common. Datasets may be multimodal, with measurements collected by distinct assay technologies. Furthermore, samples are not usually independent and identically distributed (iid).Nuisance factors or experimental conditions may cause distributional differences between groups of samples. Samples may come from a time series, or different points in space. Missing values are common, but not necessarily random. These issues can lead to incorrect conclusions if they aren't modeled correctly. We do not analyze biological data in a vacuum, though. Biologists have spent decades accumulating insights about biological systems. In principle, this prior knowledge has the potential to strengthen data analyses by (i) biasing inferences toward more probable solutions and (ii) making the solutions more interpretable. Biological pathways are a particularly rich form of prior knowledge. Pathways encode well-studied molecular processes that govern cells. Public databases like Reactome and KEGG curate these pathways in forms that are computationally accessible---formal ontologies, networks, or gene sets. However, biological prior knowledge also poses challenges. It may be too incomplete or context-specific to be useful in analyzing new data. Probabilistic models provide a natural framework for extracting insights from data and prior knowledge.Data can be modeled as observed variables and prior knowledge can be encoded in a prior distribution. We can then estimate quantities of interest via posterior inference. A central question motivating this dissertation is "what value, if any, can biological prior knowledge provide in omics data analysis?" To that end, this dissertation presents two probabilistic models that combine omics data and pathway prior knowledge. Chapter 2 presents a method to infer the structure of a signaling pathway from time series phosphoproteomic data and prior knowledge about signaling pathways. Chapter 3 proposes a matrix factorization model for multiomic data.The method uses biological prior knowledge to generate an interpretation of its outputs. These models also entail the design of inference procedures that are principled and computationally efficient. The performance of each model is evaluated on simulated and real datasets. Their code is distributed on GitHub, and care is taken to make the analyses reproducible via workflow managers. Biological prior knowledge is found to be a mixed blessing. In particular: while pathways are a useful tool for biologists to organize their knowledge, their utility as a Bayesian prior is found to be questionable.

Download or read book Efficient Large Scale Machine Learning Algorithms for Genomic Sequences written by Daniel Quang and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing (HTS) has led to many breakthroughs in basic and translational biology research. With this technology, researchers can interrogate whole genomes at single-nucleotide resolution. The large volume of data generated by HTS experiments necessitates the development of novel algorithms that can efficiently process these data. At the advent of HTS, several rudimentary methods were proposed. Often, these methods applied compromising strategies such as discarding a majority of the data or reducing the complexity of the models. This thesis focuses on the development of machine learning methods for efficiently capturing complex patterns from high volumes of HTS data.First, we focus on on de novo motif discovery, a popular sequence analysis method that predates HTS. Given multiple input sequences, the goal of motif discovery is to identify one or more candidate motifs, which are biopolymer sequence patterns that are conjectured to have biological significance. In the context of transcription factor (TF) binding, motifs may represent the sequence binding preference of proteins. Traditional motif discovery algorithms do not scale well with the number of input sequences, which can make motif discovery intractable for the volume of data generated by HTS experiments. One common solution is to only perform motif discovery on a small fraction of the sequences. Scalable algorithms that simplify the motif models are popular alternatives. Our approach is a stochastic method that is scalable and retains the modeling power of past methods.Second, we leverage deep learning methods to annotate the pathogenicity of genetic variants. Deep learning is a class of machine learning algorithms concerned with deep neural networks (DNNs). DNNs use a cascade of layers of nonlinear processing units for feature extraction and transformation. Each layer uses the output from the previous layer as its input. Similar to our novel motif discovery algorithm, artificial neural networks can be efficiently trained in a stochastic manner. Using a large labeled dataset comprised of tens of millions of pathogenic and benign genetic variants, we trained a deep neural network to discriminate between the two categories. Previous methods either focused only on variants lying in protein coding regions, which cover less than 2% of the human genome, or applied simpler models such as linear support vector machines, which can not usually capture non-linear patterns like deep neural networks can.Finally, we discuss convolutional (CNN) and recurrent (RNN) neural networks, variations of DNNs that are especially well-suited for studying sequential data. Specifically, we stacked a bidirectional recurrent layer on top of a convolutional layer to form a hybrid model. The model accepts raw DNA sequences as inputs and predicts chromatin markers, including histone modifications, open chromatin, and transcription factor binding. In this specific application, the convolutional kernels are analogous to motifs, hence the model learning is essentially also performing motif discovery. Compared to a pure convolutional model, the hybrid model requires fewer free parameters to achieve superior performance. We conjecture that the recurrent layer allows our model spatial and orientation dependencies among motifs better than a pure convolutional model can. With some modifications to this framework, the model can accept cell type-specific features, such as gene expression and open chromatin DNase I cleavage, to accurately predict transcription factor binding across cell types. We submitted our model to the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, where it was among the top performing models. We implemented several novel heuristics, which significantly reduced the training time and the computational overhead. These heuristics were instrumental to meet the Challenge deadlines and to make the method more accessible for the research community.HTS has already transformed the landscape of basic and translational research, proving itself as a mainstay of modern biological research. As more data are generated and new assays are developed, there will be an increasing need for computational methods to integrate the data to yield new biological insights. We have only begun to scratch the surface of discovering what is possible from both an experimental and a computational perspective. Thus, further development of versatile and efficient statistical models is crucial to maintaining the momentum for new biological discoveries.

Download or read book Machine Learning Models for Functional Genomics and Therapeutic Design written by Haoyang Zeng (Ph.D.) and published by . This book was released on 2019 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the limited size of training data available, machine learning models for biology have remained rudimentary and inaccurate despite the significant advance in machine learning research. With the recent advent of high-throughput sequencing technology, an exponentially growing number of genomic and proteomic datasets have been generated. These large-scale datasets admit the training of high-capacity machine learning models to characterize sophisticated features and produce accurate predictions on unseen examples. In this thesis, we attempt to develop advanced machine learning models for functional genomics and therapeutics design, two areas with ample data deposited in public databases and tremendous clinical implications. The shared theme of these models is to learn how the composition of a biological sequence encodes a functional phenotype and then leverage such knowledge to provide insight for target discovery and therapeutic design. First, we design three machine learning models that predict transcription factor binding and DNA methylation, two fundamental epigenetic phenotypes closely tied to gene regulation, from DNA sequence alone. We show that these epigenetic phenotypes can be well predicted from the sequence context. Moreover, the predicted change in phenotype between the reference and alternate allele of a genetic variant accurately reflect its functional impact and improves the identification of regulatory variants causal for complex diseases. Second, we devise two machine learning models that improve the prediction of peptides displayed by the major histocompatibility complex (MHC) on the cell surface. Computational modeling of peptide-display by MHC is central in the design of peptide-based therapeutics. Our first machine learning model introduces the capacity to quantify uncertainty in the computational prediction and proposes a new metric for peptide prioritization that reduces false positives in high-affinity peptide design. The second model improves the state-of-the-art performance in MHC-ligand prediction by employing a deep language model to learn the sequence determinants for auxiliary processes in MHC-ligand selection, such as proteasome cleavage, that are omitted by existing methods due to the lack of labeled data. Third, we develop machine learning frameworks to model the enrichment of an antibody sequence in phage-panning experiments against a target antigen. We show that antibodies with low specificity can be reduced by a computational procedure using machine learning models trained for multiple targets. Moreover, machine learning can help to design novel antibody sequences with improved affinity.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.