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Clinical and Translational Science Mar 2019
Topics: Computational Biology; Data Analysis; Information Dissemination; National Institutes of Health (U.S.); Translational Research, Biomedical; United States
PubMed: 30412342
DOI: 10.1111/cts.12595 -
Genome Biology Jan 2012A report of the Wellcome Trust Functional Genomics and Systems Biology Conference, Hinxton, UK, 29 November to 1 December 2011.
A report of the Wellcome Trust Functional Genomics and Systems Biology Conference, Hinxton, UK, 29 November to 1 December 2011.
Topics: Computational Biology; Gene Expression; Gene Regulatory Networks; Genomics; Proteomics; Systems Biology; United Kingdom
PubMed: 22289510
DOI: 10.1186/gb-2012-13-1-312 -
In Silico Biology 2015Are we close to a complete inventory of living processes so that we might expect in the near future to reproduce every essential aspect necessary for life? Or are there... (Review)
Review
Are we close to a complete inventory of living processes so that we might expect in the near future to reproduce every essential aspect necessary for life? Or are there mechanisms and processes in cells and organisms that are presently inaccessible to us? Here I argue that a close examination of a particularly well-understood system--that of Escherichia coli chemotaxis--shows we are still a long way from a complete description. There is a level of molecular uncertainty, particularly that responsible for fine-tuning and adaptation to myriad external conditions, which we presently cannot resolve or reproduce on a computer. Moreover, the same uncertainty exists for any process in any organism and is especially pronounced and important in higher animals such as humans. Embryonic development, tissue homeostasis, immune recognition, memory formation, and survival in the real world, all depend on vast numbers of subtle variations in cell chemistry most of which are presently unknown or only poorly characterized. Overcoming these limitations will require us to not only accumulate large quantities of highly detailed data but also develop new computational methods able to recapitulate the massively parallel processing of living cells.
Topics: Animals; Computational Biology; Humans; Models, Biological; Research
PubMed: 25318467
DOI: 10.3233/ISB-140461 -
Mutation Research Jun 2011Proteomics is the study of proteins on a large scale, encompassing the many interests scientists and physicians have in their expression and physical properties.... (Review)
Review
Proteomics is the study of proteins on a large scale, encompassing the many interests scientists and physicians have in their expression and physical properties. Proteomics continues to be a rapidly expanding field, with a wealth of reports regularly appearing on technology enhancements and scientific studies using these new tools. This review focuses primarily on the quantitative aspect of protein expression and the associated computational machinery for making large-scale identifications of proteins and their post-translational modifications. The primary emphasis is on the combination of liquid chromatography-mass spectrometry (LC-MS) methods and associated tandem mass spectrometry (LC-MS/MS). Tandem mass spectrometry, or MS/MS, involves a second analysis within the instrument after a molecular dissociative event in order to obtain structural information including but not limited to sequence information. This review further focuses primarily on the study of in vitro digested proteins known as bottom-up or shotgun proteomics. A brief discussion of recent instrumental improvements precedes a discussion on affinity enrichment and depletion of proteins, followed by a review of the major approaches (label-free and isotope-labeling) to making protein expression measurements quantitative, especially in the context of profiling large numbers of proteins. Then a discussion follows on the various computational techniques used to identify peptides and proteins from LC-MS/MS data. This review article then includes a short discussion of LC-MS approaches to three-dimensional structure determination and concludes with a section on statistics and data mining for proteomics, including comments on properly powering clinical studies and avoiding over-fitting with large data sets.
Topics: Computational Biology; Data Mining; Gas Chromatography-Mass Spectrometry; Gene Expression; Molecular Conformation; Protein Processing, Post-Translational; Proteomics; Statistics as Topic
PubMed: 20620221
DOI: 10.1016/j.mrgentox.2010.06.016 -
Journal of Biomedical Informatics Sep 2018
Topics: Behavior Control; Computational Biology; Health Promotion; Healthy Lifestyle; Humans; Information Technology; Medical Informatics; Persuasive Communication
PubMed: 30071315
DOI: 10.1016/j.jbi.2018.07.020 -
BMC Bioinformatics Jun 2005Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high...
Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing.
Topics: Abstracting and Indexing; Algorithms; Artificial Intelligence; Computational Biology; Computers; Databases, Bibliographic; Genomics; Information Storage and Retrieval; Internet; MEDLINE; Natural Language Processing; Pattern Recognition, Automated; Publishing; Software; Systems Integration
PubMed: 15941477
DOI: 10.1186/1471-2105-6-142 -
BMC Bioinformatics Mar 2016The fourteenth NETTAB workshop, NETTAB 2014, was devoted to a range of disciplines going from structural bioinformatics, to proteomics and to integrative systems...
The fourteenth NETTAB workshop, NETTAB 2014, was devoted to a range of disciplines going from structural bioinformatics, to proteomics and to integrative systems biology. The topics of the workshop were centred around bioinformatics methods, tools, applications, and perspectives for models, standards and management of high-throughput biological data, structural bioinformatics, functional proteomics, mass spectrometry, drug discovery, and systems biology.43 scientific contributions were presented at NETTAB 2014, including keynote, special guest and tutorial talks, oral communications, and posters. Full papers from some of the best contributions presented at the workshop were later submitted to a special Call for this Supplement.Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement.
Topics: Computational Biology; Data Mining; Drug Discovery; Genomics; Humans; Proteomics; Systems Biology
PubMed: 26960985
DOI: 10.1186/s12859-016-0907-y -
BioMed Research International 2017
Topics: Animals; Computational Biology; Humans; Translational Research, Biomedical
PubMed: 28337442
DOI: 10.1155/2017/1572730 -
Nature Communications Aug 2020Single cell transcriptomics technologies have vast potential in advancing our understanding of biology and disease. Here, Sarah Aldridge and Sarah Teichmann review the... (Review)
Review
Single cell transcriptomics technologies have vast potential in advancing our understanding of biology and disease. Here, Sarah Aldridge and Sarah Teichmann review the last decade of technological advancements in single-cell transcriptomics and highlight some of the recent discoveries enabled by this technology.
Topics: Animals; Computational Biology; Forecasting; Gene Expression Profiling; History, 21st Century; Humans; Single-Cell Analysis; Transcriptome
PubMed: 32855414
DOI: 10.1038/s41467-020-18158-5 -
Future Medicinal Chemistry Jan 2019
Review
Topics: Computational Biology; Machine Learning; Models, Chemical; Molecular Probes
PubMed: 30526065
DOI: 10.4155/fmc-2018-0282