Relevant bibliographies by topics / Biology, Genetics|Biology, Bioinformatics|Computer Science

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biology, Genetics|Biology, Bioinformatics|Computer Science'

Author: Grafiati
Published: 9 July 2021
Last updated: 17 February 2022

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Journal articles on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

1

Wefer, Stephen H., and Keith Sheppard. "Bioinformatics in High School Biology Curricula: A Study of State Science Standards." CBE—Life Sciences Education 7, no. 1 (March 2008): 155–62. http://dx.doi.org/10.1187/cbe.07-05-0026.

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The proliferation of bioinformatics in modern biology marks a modern revolution in science that promises to influence science education at all levels. This study analyzed secondary school science standards of 49 U.S. states (Iowa has no science framework) and the District of Columbia for content related to bioinformatics. The bioinformatics content of each state's biology standards was analyzed and categorized into nine areas: Human Genome Project/genomics, forensics, evolution, classification, nucleotide variations, medicine, computer use, agriculture/food technology, and science technology and society/socioscientific issues. Findings indicated a generally low representation of bioinformatics-related content, which varied substantially across the different areas, with Human Genome Project/genomics and computer use being the lowest (8%), and evolution being the highest (64%) among states' science frameworks. This essay concludes with recommendations for reworking/rewording existing standards to facilitate the goal of promoting science literacy among secondary school students.
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Rajpal, Deepak K. "Understanding Biology Through Bioinformatics." International Journal of Toxicology 24, no. 3 (May 2005): 147–52. http://dx.doi.org/10.1080/10915810590948325.

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During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.
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Chen, Yi-Ping Phoebe, and Geoff McLachlan. "Bioinformatics Research in Australia." Asia-Pacific Biotech News 07, no. 03 (February 3, 2003): 82–84. http://dx.doi.org/10.1142/s0219030303000211.

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Bioinformatics is the intersection of computer science, statistics, molecular biology and genetics. It is one of the most important emerging research areas of the 21st century and has already attracted worldwide interest. It is clear that major initiatives are being undertaken which will establish Australia both as a vital link in the international bioinformatics community for research and development and also as an Asia-Pacific service for bioinformatics. This article briefly notes some groups carrying out bioinformatics research in Australia.
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Hofestaedt, R. "Computer science and biology—the German Conference on Bioinformatics (GCB'96)." Biosystems 43, no. 1 (May 1997): 69–71. http://dx.doi.org/10.1016/s0303-2647(97)01689-4.

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Gauthier, Jeff, Antony T. Vincent, Steve J. Charette, and Nicolas Derome. "A brief history of bioinformatics." Briefings in Bioinformatics 20, no. 6 (August 3, 2018): 1981–96. http://dx.doi.org/10.1093/bib/bby063.

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AbstractIt is easy for today’s students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of ‘Big Data’ has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.
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Fogg, Christiana N. "ISMB 2016 offers outstanding science, networking, and celebration." F1000Research 5 (June 14, 2016): 1371. http://dx.doi.org/10.12688/f1000research.8640.1.

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The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community. ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.
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Barron, S., M. Witten, R. Harkness, and J. Driver. "A bibliography on computational algorithms in molecular biology and genetics." Bioinformatics 7, no. 2 (1991): 269. http://dx.doi.org/10.1093/bioinformatics/7.2.269.

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Orlov, Yuriy L., Ancha V. Baranova, and Tatiana V. Tatarinova. "Bioinformatics Methods in Medical Genetics and Genomics." International Journal of Molecular Sciences 21, no. 17 (August 28, 2020): 6224. http://dx.doi.org/10.3390/ijms21176224.

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Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.
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Heinemann, M., and S. Panke. "Synthetic biology--putting engineering into biology." Bioinformatics 22, no. 22 (September 5, 2006): 2790–99. http://dx.doi.org/10.1093/bioinformatics/btl469.

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Likić, Vladimir A., Malcolm J. McConville, Trevor Lithgow, and Antony Bacic. "Systems Biology: The Next Frontier for Bioinformatics." Advances in Bioinformatics 2010 (February 9, 2010): 1–10. http://dx.doi.org/10.1155/2010/268925.

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Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
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Dissertations / Theses on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

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Wang, Jeremy R. "Analysis and Visualization of Local Phylogenetic Structure within Species." Thesis, The University of North Carolina at Chapel Hill, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3562960.

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While it is interesting to examine the evolutionary history and phylogenetic relationship between species, for example, in a sort of "tree of life", there is also a great deal to be learned from examining population structure and relationships within species. A careful description of phylogenetic relationships within species provides insights into causes of phenotypic variation, including disease susceptibility. The better we are able to understand the patterns of genotypic variation within species, the better these populations may be used as models to identify causative variants and possible therapies, for example through targeted genome-wide association studies (GWAS). My thesis describes a model of local phylogenetic structure, how it can be effectively derived under various circumstances, and useful applications and visualizations of this model to aid genetic studies.

I introduce a method for discovering phylogenetic structure among individuals of a population by partitioning the genome into a minimal set of intervals within which there is no evidence of recombination. I describe two extensions of this basic method. The first allows it to be applied to heterozygous, in addition to homozygous, genotypes and the second makes it more robust to errors in the source genotypes.

I demonstrate the predictive power of my local phylogeny model using a novel method for genome-wide genotype imputation. This imputation method achieves very high accuracy—on the order of the accuracy rate in the sequencing technology—by imputing genotypes in regions of shared inheritance based on my local phylogenies.

Comparative genomic analysis within species can be greatly aided by appropriate visualization and analysis tools. I developed a framework for web-based visualization and analysis of multiple individuals within a species, with my model of local phylogeny providing the underlying structure. I will describe the utility of these tools and the applications for which they have found widespread use.

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Guturu, Harendra. "Deciphering human gene regulation using computational and statistical methods." Thesis, Stanford University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3581147.

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It is estimated that at least 10-20% of the mammalian genome is dedicated towards regulating the 1-2% of the genome that codes for proteins. This non-coding, regulatory layer is a necessity for the development of complex organisms, but is poorly understood compared to the genetic code used to translate coding DNA into proteins. In this dissertation, I will discuss methods developed to better understand the gene regulatory layer. I begin, in Chapter 1, with a broad overview of gene regulation, motivation for studying it, the state of the art with a historically context and where to look forward.

In Chapter 2, I discuss a computational method developed to detect transcription factor (TF) complexes. The method compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid transcription factor (TF) complexes. Structural data were integrated to explore overlapping motif arrangements while ensuring physical plausibility of the TF complex. Using this approach, I predicted 422 physically realistic TF complex motifs at 18% false discovery rate (FDR). I found that the set of complexes is enriched in known TF complexes. Additionally, novel complexes were supported by chromatin immunoprecipitation sequencing (ChIP-seq) datasets. Analysis of the structural modeling revealed three cooperativity mechanisms and a tendency of TF pairs to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. The TF complexes and associated binding site predictions are made available as a web resource at http://complex.stanford.edu.

Next, in Chapter 3, I discuss how gene enrichment analysis can be applied to genome-wide conserved binding sites to successfully infer regulatory functions for a given TF complex. A genomic screen predicted 732,568 combinatorial binding sites for 422 TF complex motifs. From these predictions, I inferred 2,440 functional roles, which are consistent with known functional roles of TF complexes. In these functional associations, I found interesting themes such as promiscuous partnering of TFs (such as ETS) in the same functional context (T cells). Additionally, functional enrichment identified two novel TF complex motifs associated with spinal cord patterning genes and mammary gland development genes, respectively. Based on these predictions, I discovered novel spinal cord patterning enhancers (5/9, 56% validation rate) and enhancers active in MCF7 cells (11/19, 53% validation rate). This set replete with thousands of additional predictions will serve as a powerful guide for future studies of regulatory patterns and their functional roles.

Then, in Chapter 4, I outline a method developed to predict disease susceptibility due to gene mis-regulation. The method interrogates ensembles of conserved binding sites of regulatory factors disrupted by an individual's variants and then looks for their most significant congregation next to a group of functionally related genes. Strikingly, when the method is applied to five different full human genomes, the top enriched function for each is reflective of their very different medical histories. These results suggest that erosion of gene regulation results in function specific mutation loads that manifest as disease predispositions in a familial lineage. Additionally, this aggregate analysis method addresses the problem that although many human diseases have a genetic component involving many loci, the majority of studies are statistically underpowered to isolate the many contributing loci.

Finally, I conclude in Chapter 5 with a summary of my findings throughout my research and future directions of research based on my findings.

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Brewer, Judy. "Metabolic Modeling of Inborn Errors of Metabolism: Carnitine Palmitoyltransferase II Deficiency and Respiratory Chain Complex I Deficiency." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:24078365.

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The research goal was to assess the current capabilities of a metabolic modeling environment to support exploration of inborn errors of metabolism (IEMs); and to assess whether, drawing on evidence from published studies of EMs, the current capabilities of this modeling environment correlate with clinical measures of energy production, fatty acid oxidation, accumulation of toxic by-products of defective metabolism, and mitigation via therapeutic agents. IEMs comprise several hundred disorders of energy production, often with significant impact on morbidity and mortality. Despite advances in genomic medicine, currently the majority of therapeutic options for IEMs are supportive only, and most only weakly evidenced. Metabolic modeling could potentially offer an in silico alternative for exploring therapeutic possibilities. This research established models of two inborn errors of metabolism (IEMs), carnitine palmitoyltransferase (CPT) II deficiency and respiratory chain complex I deficiency, allowing exploration of combinations of IEMs at different degrees of enzyme deficiency. It utilized a modified version of the human metabolic network reconstruction, Recon 2, which includes known metabolic reactions and metabolites in human cells, and which allows constraint-based modeling within a computational and mathematical representation of human metabolism. It utilized the Matlab-based COBRA (Constraint-based Reconstruction and Analysis) Toolbox 2.0, and a customized suite of functions, to model ATP production, long-chain fatty acid oxidation (LCFA), and acylcarnitine accumulation in response to varying defect levels, inputs and a simulated candidate therapy. Following significant curation of the metabolic network reconstruction and customization of COBRA/Matlab functions, this study demonstrated that ATP production and LCFA oxidation were within expected ranges, and correlated with clinical data for enzyme deficiencies, while acylcarnitine accumulation inversely correlated with the degree of enzyme deficiency; and that it was possible to simulate upregulation of enzyme activity with a therapeutic agent. Results of the curation effort contributed to development of an updated version of the metabolic reconstruction Recon 2. Customization of modeling approaches resulted in a suite of re-usable Matlab functions and scripts usable with COBRA Toolbox methods available for further exploration of IEMs. While this research points to potentially greater suitability of kinetic modeling for some aspects of metabolic modeling of IEMs, it helps to demonstrate potential viability of constraint-based steady state modeling as a means to explore some clinically relevant measures of metabolic function for single and combined inborn errors of metabolism.
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Zou, James Yang. "Algorithms and Models for Genome Biology." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11280.

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New advances in genomic technology make it possible to address some of the most fundamental questions in biology for the first time. They also highlight a need for new approaches to analyze and model massive amounts of complex data. In this thesis, I present six research projects that illustrate the exciting interaction between high-throughput genomic experiments, new machine learning algorithms, and mathematical modeling. This interdisci- plinary approach gives insights into questions ranging from how variations in the epigenome lead to diseases across human populations to how the slime mold finds the shortest path. The algorithms and models developed here are also of interest to the broader machine learning community, and have applications in other domains such as text modeling.
Mathematics
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Nicol, Megan E. "Unraveling the Nexus: Investigating the Regulatory Genetic Networks of Hereditary Ataxias." Ohio University Honors Tutorial College / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1400604580.

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Kiritchenko, Svetlana. "Hierarchical text categorization and its application to bioinformatics." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29298.

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In a hierarchical categorization problem, categories are partially ordered to form a hierarchy. In this dissertation, we explore two main aspects of hierarchical categorization: learning algorithms and performance evaluation. We introduce the notion of consistent hierarchical classification that makes classification results more comprehensible and easily interpretable for end-users. Among the previously introduced hierarchical learning algorithms, only a local top-down approach produces consistent classification. The present work extends this algorithm to the general case of DAG class hierarchies and possible internal class assignments. In addition, a new global hierarchical approach aimed at performing consistent classification is proposed. This is a general framework of converting a conventional "flat" learning algorithm into a hierarchical one. An extensive set of experiments on real and synthetic data indicate that the proposed approach significantly outperforms the corresponding "flat" as well as the local top-down method. For evaluation purposes, we use a novel hierarchical evaluation measure that is superior to the existing hierarchical and non-hierarchical evaluation techniques according to a number of formal criteria. Also, this dissertation presents the first endeavor of applying the hierarchical text categorization techniques to the tasks of bioinformatics. Three bioinformatics problems are addressed. The objective of the first task, indexing biomedical articles with Medical Subject Headings (MeSH), is to associate documents with biomedical concepts from the specialized vocabulary of MeSH. In the second application, we tackle a challenging problem of gene functional annotation from biomedical literature. Our experiments demonstrate a considerable advantage of hierarchical text categorization techniques over the "flat" method on these two tasks. In the third application, our goal is to enrich the analysis of plain experimental data with biological knowledge. In particular, we incorporate the functional information on genes directly into the clustering process of microarray data with the outcome of an improved biological relevance and value of clustering results.
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Parmidge, Amelia J. "NEPIC, a Semi-Automated Tool with a Robust and Extensible Framework that Identifies and Tracks Fluorescent Image Features." Thesis, Mills College, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1556025.

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As fluorescent imaging techniques for biological systems have advanced in recent years, scientists have used fluorescent imaging more and more to capture the state of biological systems at different moments in time. For many researchers, analysis of the fluorescent image data has become the limiting factor of this new technique. Although identification of fluorescing neurons in an image is (seemingly) easily done by the human visual system, manual delineation of the exact pixels comprising these fluorescing regions of interest (or fROIs) in digital images does not scale up well, being time-consuming, reiterative, and error-prone. This thesis introduces NEPIC, the Neuron-to- Environment Pixel Intensity Calculator, which seeks to help resolve this issue. NEPIC is a semi-automated tool for finding and tracking the cell body of a single neuron over an entire movie of grayscale calcium image data. NEPIC also provides a highly extensible, open source framework that could easily support finding and tracking other kinds of fROIs. When tested on calcium image movies of the AWC neuron in C. elegans under highly variant conditions, NEPIC correctly identified the neuronal cell body in 95.48% of the movie frames, and successfully tracked this cell body feature across 98.60% of the frame transitions in the movies. Although support for finding and tracking multiple fROIs has yet to be implemented, NEPIC displays promise as a tool for assisting researchers in the bulk analysis of fluorescent imaging data.

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Daniels, Noah Manus. "Remote Homology Detection in Proteins Using Graphical Models." Thesis, Tufts University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3563611.

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Given the amino acid sequence of a protein, researchers often infer its structure and function by finding homologous, or evolutionarily-related, proteins of known structure and function. Since structure is typically more conserved than sequence over long evolutionary distances, recognizing remote protein homologs from their sequence poses a challenge.

We first consider all proteins of known three-dimensional structure, and explore how they cluster according to different levels of homology. An automatic computational method reasonably approximates a human-curated hierarchical organization of proteins according to their degree of homology.

Next, we return to homology prediction, based only on the one-dimensional amino acid sequence of a protein. Menke, Berger, and Cowen proposed a Markov random field model to predict remote homology for beta-structural proteins, but their formulation was computationally intractable on many beta-strand topologies.

We show two different approaches to approximate this random field, both of which make it computationally tractable, for the first time, on all protein folds. One method simplifies the random field itself, while the other retains the full random field, but approximates the solution through stochastic search. Both methods achieve improvements over the state of the art in remote homology detection for beta-structural protein folds.

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Chen, Hui 1974. "Algorithms and statistics for the detection of binding sites in coding regions." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97926.

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This thesis deals with the problem of detecting binding sites in coding regions. A new comparative analysis method is developed by improving an existing method called COSMO.
The inter-species sequence conservation observed in coding regions may be the result of two types of selective pressure: the selective pressure on the protein encoded and, sometimes, the selective pressure on the binding sites. To predict some region in coding regions as a binding site, one needs to make sure that the conservation observed in this region is not due to the selective pressure on the protein encoded. To achieve this, COSMO built a null model with only the selective pressure on the protein encoded and computed p-values for the observed conservation scores, conditional on the fixed set of amino acids observed at the leaves.
It is believed, however, that the selective pressure on the protein assumed in COSMO is overly strong. Consequently, some interesting regions may be left undetected. In this thesis, a new method, COSMO-2, is developed to relax this assumption.
The amino acids are first classified into a fixed number of overlapping functional classes by applying an expectation maximization algorithm on a protein database. Two probabilities for each gene position are then calculated: (i) the probability of observing a certain degree of conservation in the orthologous sequences generated under each class in the null model (i.e. the p-value of the observed conservation under each class); and (ii) the probability that the codon column associated with that gene position belongs to each class. The p-value of the observed conservation for each gene position is the sum of the products of the two probabilities for all classes. Regions with low p-values are identified as potential binding sites.
Five sets of orthologous genes are analyzed using COSMO-2. The results show that COSMO-2 can detect the interesting regions identified by COSMO and can detect more interesting regions than COSMO in some cases.
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Chen, Xiaoyu 1974. "Computational detection of tissue-specific cis-regulatory modules." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97927.

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A cis-regulatory module (CRM) is a DNA region of a few hundred base pairs that consists of clustering of several transcription factor binding sites and regulates the expression of a nearby gene. This thesis presents a new computational approach to CRM detection.
It is believed that tissue-specific CRMs tend to regulate nearby genes in a certain tissue and that they consist of binding sites for transcription factors (TFs) that are also expressed in that tissue. These facts allow us to make use of tissue-specific gene expression data to detect tissue-specific CRMs and improve the specificity of module prediction.
We build a Bayesian network to integrate the sequence information about TF binding sites and the expression information about TFs and regulated genes. The network is then used to infer whether a given genomic region indeed has regulatory activity in a given tissue. A novel EM algorithm incorporating probability tree learning is proposed to train the Bayesian network in an unsupervised way. A new probability tree learning algorithm is developed to learn the conditional probability distribution for a variable in the network that has a large number of hidden variables as its parents.
Our approach is evaluated using biological data, and the results show that it is able to correctly discriminate among human liver-specific modules, erythroid-specific modules, and negative-control regions, even though no prior knowledge about the TFs and the target genes is employed in our algorithm. In a genome-wide scale, our network is trained to identify tissue-specific CRMs in ten tissues. Some known tissue-specific modules are rediscovered, and a set of novel modules are predicted to be related with tissue-specific expression.
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Books on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

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Ciobanu, Gabriel. Modelling in Molecular Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Priami, Corrado. Transactions on Computational Systems Biology XIII. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Bioinformatics and functional genomics. Hoboken, NJ: Wiley-Liss, 2004.

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Pevsner, Jonathan. Bioinformatics and Functional Genomics. New York: John Wiley & Sons, Ltd., 2005.

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Bleasby, Alan. EMBOSS administrator's guide: Bioinformatics software management. Cambridge: Cambridge University Press, 2011.

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Lingeman, Jesse M. Network Inference in Molecular Biology: A Hands-on Framework. New York, NY: Springer New York, 2012.

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Corrado, Priami, Waterman Michael S, Pevzner Pavel, and SpringerLink (Online service), eds. Transactions on Computational Systems Biology IX. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.

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library, Wiley online, ed. Knowledge based bioinformatics: From analysis to interpretation. Chichester, West Sussex: John Wiley & Sons, 2010.

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Systems biology and livestock science. Chichester, West Sussex: Wiley-Blackwell, 2011.

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Transactions on computational systems biology XI. Berlin: Springer-Verlag, 2009.

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Book chapters on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

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Thomas, Michael A., Mitch D. Day, and Luobin Yang. "Computational Options for Bioinformatics Research in Evolutionary Biology." In Lecture Notes in Computer Science, 68–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428848_9.

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Singh, Desh Deepak. "Bioinformatics—Structural Biology Interface." In Bioinformatics: Applications in Life and Environmental Sciences, 25–33. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_4.

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Priami, Corrado. "Algorithmic Systems Biology — Computer Science Propels Systems Biology." In Handbook of Natural Computing, 1835–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-92910-9_54.

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Marcus, Frederick B. "Science Management." In Bioinformatics and Systems Biology, 215–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78353-4_10.

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Prasad, T. V., and S. I. Ahson. "Data Mining for Bioinformatics— Systems Biology." In Bioinformatics: Applications in Life and Environmental Sciences, 145–72. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_9.

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Lander, Eric. "Biology as Information." In Lecture Notes in Computer Science, 373. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11415770_28.

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Priami, Corrado. "Computational Thinking in Biology." In Lecture Notes in Computer Science, 63–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76639-1_4.

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Mukherjee, Amar. "Computational Biology – The New Frontier of Computer Science." In Distributed Computing - IWDC 2004, 204–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30536-1_25.

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Cardelli, Luca. "Abstract Machines of Systems Biology." In Lecture Notes in Computer Science, 145–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11599128_10.

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Waterman, Michael S. "Stan Ulam and Computational Biology." In Lecture Notes in Computer Science, 159. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732990_14.

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Conference papers on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

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Karp, Richard M. "Computer Science as a Lens on the Sciences: The Example of Computational Molecular Biology." In 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.66.

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Tartaro, Andrea, and Renee J. Chosed. "Computer Scientists at the Biology Lab Bench." In SIGCSE '15: The 46th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676723.2677246.

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Dodds, Zachary, Malia Morgan, Lindsay Popowski, Henry Coxe, Caroline Coxe, Kewei Zhou, Eliot Bush, and Ran Libeskind-Hadas. "A Biology-based CS1." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3408877.3432469.

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Fisher, Jasmin. "Understanding biology through logic." In CSL-LICS '14: JOINT MEETING OF the Twenty-Third EACSL Annual Conference on COMPUTER SCIENCE LOGIC. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2603088.2603166.

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Soshinsky, Ivan. "Two-Interval Musical Scales and Binary Structures in Computer Science and Biology." In ISIS Summit Vienna 2015—The Information Society at the Crossroads. Basel, Switzerland: MDPI, 2015. http://dx.doi.org/10.3390/isis-summit-vienna-2015-t7005.

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Huang, Xiuzhen, and Jing Lai. "Parameterized Graph Problems in Computational Biology." In Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007). IEEE, 2007. http://dx.doi.org/10.1109/imsccs.2007.4392590.

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Huang, Xiuzhen, and Jing Lai. "Parameterized Graph Problems in Computational Biology." In Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007). IEEE, 2007. http://dx.doi.org/10.1109/imsccs.2007.50.

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Widodo, Ari. "Experienced biology teachers’ pedagogical content knowledge (PCK) on photosynthesis." In MATHEMATICS, SCIENCE, AND COMPUTER SCIENCE EDUCATION (MSCEIS 2016): Proceedings of the 3rd International Seminar on Mathematics, Science, and Computer Science Education. Author(s), 2017. http://dx.doi.org/10.1063/1.4983985.

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McFarlane, Ross A., and Irina V. Biktasheva. "Beatbox—A Computer Simulation Environment for Computational Biology of the Heart." In Visions of Computer Science - BCS International Academic Conference. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/vocs2008.10.

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Run-ze, Zhang, Xu Hao, Xu Xiang-rong, and Yu Ling-guo. "Research of path planning based on synthetic biology and DNA computer." In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2015. http://dx.doi.org/10.1109/icsess.2015.7339233.

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Reports on the topic "Biology, Genetics|Biology, Bioinformatics|Computer Science"

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Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, October 2020. http://dx.doi.org/10.47496/nl.blog.09.

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Abstract:
Modern systems biology is essentially interdisciplinary, tying molecular biology, the omics, bioinformatics and non-biological disciplines like computer science, engineering, physics, and mathematics together.
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Tucker Blackmon, Angelicque. Formative External Evaluation and Data Analysis Report Year Three: Building Opportunities for STEM Success. Innovative Learning Center, LLC, August 2020. http://dx.doi.org/10.52012/mlfk2041.

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