Download or read book RNA Sequence Classification Using Secondary Structure Fingerprints Sequence Based Features and Deep Learning written by Kevin Sutanto and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: RNAs are involved in different facets of biological processes; including but not limited to controlling and inhibiting gene expressions, enabling transcription and translation from DNA to proteins, in processes involving diseases such as cancer, and virus-host interactions. As such, there are useful applications that may arise from studies and analyses involving RNAs, such as detecting cancer by measuring the abundance of specific RNAs, detecting and identifying infections involving RNA viruses, identifying the origins of and relationships between RNA viruses, and identifying potential targets when designing novel drugs. Extracting sequences from RNA samples is usually not a major limitation anymore thanks to sequencing technologies such as RNA-Seq. However, accurately identifying and analyzing the extracted sequences is often still the bottleneck when it comes to developing RNA-based applications. Like proteins, functional RNAs are able to fold into complex structures in order to perform specific functions throughout their lifecycle. This suggests that structural information can be used to identify or classify RNA sequences, in addition to the sequence information of the RNA itself. Furthermore, a strand of RNA may have more than one possible structural conformations it can fold into, and it is also possible for a strand to form different structures in vivo and in vitro. However, past studies that utilized secondary structure information for RNA identification purposes have relied on one predicted secondary structure for each RNA sequence, despite the possible one-to-many relationship between a strand of RNA and the possible secondary structures. Therefore, we hypothesized that using a representation that includes the multiple possible secondary structures of an RNA for classification purposes may improve the classification performance. We proposed and built a pipeline that produces secondary structure fingerprints given a sequence of RNA, that takes into account the aforementioned multiple possible secondary structures for a single RNA. Using this pipeline, we explored and developed different types of secondary structure fingerprints in our studies. A type of fingerprints serves as high-level topological representations of the RNA structure, while another type represents matches with common known RNA secondary structure motifs we have curated from databases and the literature. Next, to test our hypothesis, the different fingerprints are then used with deep learning and with different datasets, alone and together with various sequence-based features, to investigate how the secondary structure fingerprints affect the classification performance. Finally, by analyzing our findings, we also propose approaches that can be adopted by future studies to further improve our secondary structure fingerprints and classification performance.

Download or read book Deep Learning Models for RNA protein Binding written by Shreshth Gandhi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: RNA binding proteins(RBPs) are crucial bio-molecules that fine-tune gene expression in cells. Each RBP prefers to bind to a specific RNA sub-sequence, like a key fitting a lock. Understanding the specific binding preferences of RBPs is an important step to understanding the various steps of gene expression in cells and in solving several genetic disorders. There are thousands of RBPs in humans and only a small fraction of them are well understood. In this work, we develop deep neural network models that allow us to learn binding preferences for a large number of RBPs from high-throughput data, without requiring any specific domain knowledge or feature engineering. Deep learning has improved state of the art in several fields such as image classification, speech recognition, and even genomics. Deep learning approaches obviate the need for careful feature engineering by learning useful representations directly from the data. We propose two deep architectures and use them to predict RNA-protein binding. Based on recent findings that show the importance of RNA secondary structure in RBP binding, we incorporate computationally predicted secondary structure features as input to our models and show its effectiveness in boosting prediction performance. We demonstrate that our models achieve significantly higher correlations on held out in vitro testing data compared to previous approaches. We show that our model can generalize well to in-vivo CLIP-SEQ data and achieve higher median AUCs than other approaches. We demonstrate that our models discover known preferences for proteins such as CPO and VTS1 as well as report other proteins for which we find secondary structure playing an important role in binding. We demonstrate the strengths of our model compared to other approaches such as the ability to combine information from long distances along the sequence input.

Download or read book Protein Nucleic Acid Interactions written by Phoebe A. Rice and published by Royal Society of Chemistry. This book was released on 2008-05-22 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides both in-depth background and up-to-date information in this area. The chapters are organized by general themes and principles, written by experts who illustrate topics with current findings. Topics covered include: - the role of ions and hydration in protein-nucleic acid interactions - transcription factors and combinatorial specificity - indirect readout of DNA sequence - single-stranded nucleic acid binding proteins - nucleic acid junctions and proteins, - RNA protein recognition - recognition of DNA damage. It will be a key reference for both advanced students and established scientists wishing to broaden their horizons.

Download or read book Modified BPN Based RNA written by Shailendra Singh and published by LAP Lambert Academic Publishing. This book was released on 2014-11-18 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea of this work is to classify the RNA secondary structure elements using the modified backpropagation neural network. RNA secondary structure contains elements like helix, hairpin, internal loop, external loop, multi branch loop, bulge etc.The hairpin, non hairpin, helix and non helix portions are extracted using the representative and the secondary structure. Since sequences are strings of alphabets A, U, C, G and vary in length so they can't be used as such for inputs of neural network. The feature vector is extracted from sequences. Feature vector has eight parameters.

Download or read book Computational Analysis and Annotation of Structurally Functional RNAs written by Milad Miladi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This work is a dissertation about computational methodologies and analyses of ribonucleic acid (RNA) molecules based on their sequence and structure properties. RNA is an essential molecule of living cells that acts as the career of the proteins genetic information and also as a regulatory functional element that contributes to cellular mechanisms. While only less than 3% of the human genome is encoding for known proteins, more than 85% of the genome is getting transcribed into RNA. Alone for the human genome, tens of thousands of non-coding RNA genes exist bearing pervasive functions. Despite the important roles of RNAs, functional and the regulatory mechanisms of a large number of the non-coding and protein-coding RNAs is either unknown or poorly understood. To solve this challenge, computational methodologies are a vital asset for a scalable and systematic analysis and annotation of RNAs with unknown functions. RNAs are polymer molecules that fold into complex structures within the cells. For a functional RNA, its folded structure often plays an important role and is better conserved than the polymer sequence through evolution. Therefore, it is essential to consider both the sequence and structure information for the task of annotation and discovery of functional RNAs using the computational approaches. Comparative methodologies utilise the evolutionary conservation information of both sequence and structure. They are pivot assets for providing reliable structure prediction and annotation of functional RNAs. Over the past decade, millions of RNA sequences have been obtained using techniques such as genomic screens and high-throughput sequencing experiments. These techniques produce up to several thousands or even millions of sequences and can be applied over all the domains of life. Analysing these large collections of sequences, for the evaluation and annotation of functional RNAs, demands efficient optimisation algorithms with sufficiently accurate models. Additionally, since the cells rely on heterogeneous molecules and mechanisms to function, integrative analysis of biological data is commonly required nowadays. Therefore, computational approaches based on techniques such as machine learning are needed to provide comprehensive strategies with high efficiencies also at different levels of the data. This thesis addresses some substantial challenges for the evaluation and annotation of functional RNAs by presenting novel contributions using computational analysis, optimisation algorithms, comparative methodologies, clustering approaches. The personal contributions are presented in the form of six works that are encompassed as six publications from three domains for the tasks of annotation, discovery, and analysis of functional RNAs. SPARSE and Pankov are two novel contributed algorithms for the problem of simultaneous alignment and folding (SA&F) of RNAs. SPARSE achieves a quadratic complexity without sequence-based heuristics by utilising a strong sparsification over the ensemble of possible secondary structure formations. The second SA&F algorithm Pankov, enables a fast simultaneous alignment and folding of RNAs while cohering to the nearest-neighbour thermodynamics principle of the standard RNA folding model. Pankov provides the most accurate SA&F probabilistic energy model until today, by mapping the nearest-neighbour principle to a Markov scheme using conditional in-loop probabilities. RNAscClust and GraphClust2 are presented for scalable clustering of RNA sequences based on sequence and structure. The RNAscClust methodology enables a linear-time clustering of paralogous RNAs based on their sequence and structure. Both tools are machine learning approaches that utilise graph kernel and locality-sensitive hashing schemes to support the clustering of input entries in an asymptotically linear time. RNAscClust incorporates orthogonal structure conservation to enhance the clustering and annotation performance. GraphClust2 is an integrative approach for the accessible and scalable clustering of RNAs to identify structurally conserved non-coding RNAs and motifs. GraphClust2 outperforms its predecessor and importantly supports diverse sources of genomic and experimental data in an accessible fashion. GraphClust2 bridges the gap between high-throughput sequencing experiments and the structure-based methodologies for functional RNA discovery. The final topic covered by this thesis is the mutational analysis of RNA secondary structure and function. A large-scale compilation and statistical analysis of somatic cancer synonymous mutations is presented. The analysis and experiments reveal that the synonymous mutations, despite not changing encoded protein sequence, can have substantial impacts on the gene expression levels and considerably disrupt the local secondary structure of mRNAs. Finally, MutaRNA is presented as an accessible web-based solution for evaluating the impact of mutation on the RNA secondary structure and visualising the complex impacts of the mutation on the intra-molecular interactions potentials in an intuitive manner

Download or read book Deep Learning for Biomedical Applications written by Utku Kose and published by CRC Press. This book was released on 2021-07-19 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Download or read book Data Mining in Bioinformatics written by Jason T. L. Wang and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Download or read book Feature Representation and Learning Methods With Applications in Protein Secondary Structure written by Zhibin Lv and published by Frontiers Media SA. This book was released on 2021-10-25 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning in Biology and Medicine written by Davide Bacciu and published by World Scientific Publishing Europe Limited. This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

Download or read book Machine learning for biological sequence analysis written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-03-09 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proteomics Data Analysis written by Daniela Cecconi and published by . This book was released on 2021 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bioinformatics for Beginners written by Supratim Choudhuri and published by Elsevier. This book was released on 2014-05-09 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools provides a coherent and friendly treatment of bioinformatics for any student or scientist within biology who has not routinely performed bioinformatic analysis. The book discusses the relevant principles needed to understand the theoretical underpinnings of bioinformatic analysis and demonstrates, with examples, targeted analysis using freely available web-based software and publicly available databases. Eschewing non-essential information, the work focuses on principles and hands-on analysis, also pointing to further study options. Avoids non-essential coverage, yet fully describes the field for beginners Explains the molecular basis of evolution to place bioinformatic analysis in biological context Provides useful links to the vast resource of publicly available bioinformatic databases and analysis tools Contains over 100 figures that aid in concept discovery and illustration

Download or read book Bioinformatics Computing written by Bryan P. Bergeron and published by Prentice Hall Professional. This book was released on 2003 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.

Download or read book Mapping and Sequencing the Human Genome written by National Research Council and published by National Academies Press. This book was released on 1988-01-01 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is growing enthusiasm in the scientific community about the prospect of mapping and sequencing the human genome, a monumental project that will have far-reaching consequences for medicine, biology, technology, and other fields. But how will such an effort be organized and funded? How will we develop the new technologies that are needed? What new legal, social, and ethical questions will be raised? Mapping and Sequencing the Human Genome is a blueprint for this proposed project. The authors offer a highly readable explanation of the technical aspects of genetic mapping and sequencing, and they recommend specific interim and long-range research goals, organizational strategies, and funding levels. They also outline some of the legal and social questions that might arise and urge their early consideration by policymakers.

Download or read book Identification of immune related biomarkers for cancer diagnosis based on multi omics data written by Liang Cheng and published by Frontiers Media SA. This book was released on 2023-02-02 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining for Genomics and Proteomics written by Darius M. Dziuda and published by John Wiley & Sons. This book was released on 2010-07-16 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

Download or read book An Introduction to Variational Autoencoders written by Diederik P. Kingma and published by . This book was released on 2019-11-12 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Variational Autoencoders provides a quick summary for the of a topic that has become an important tool in modern-day deep learning techniques.