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

Author: Grafiati

Published: 9 July 2021

Last updated: 17 February 2022

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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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5

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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7

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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8

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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9

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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10

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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Valencia, A. "BIOINFORMATICS: BIOLOGY BY OTHER MEANS." Bioinformatics 18, no. 12 (December 1, 2002): 1551–52. http://dx.doi.org/10.1093/bioinformatics/18.12.1551.

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12

Krilowicz, Beverly, Wendie Johnston, Sandra B. Sharp, Nancy Warter-Perez, and Jamil Momand. "A Summer Program Designed to Educate College Students for Careers in Bioinformatics." CBE—Life Sciences Education 6, no. 1 (March 2007): 74–83. http://dx.doi.org/10.1187/cbe.06-03-0150.

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A summer program was created for undergraduates and graduate students that teaches bioinformatics concepts, offers skills in professional development, and provides research opportunities in academic and industrial institutions. We estimate that 34 of 38 graduates (89%) are in a career trajectory that will use bioinformatics. Evidence from open-ended research mentor and student survey responses, student exit interview responses, and research mentor exit interview/survey responses identified skills and knowledge from the fields of computer science, biology, and mathematics that are critical for students considering bioinformatics research. Programming knowledge and general computer skills were essential to success on bioinformatics research projects. General mathematics skills obtained through current undergraduate natural sciences programs were adequate for the research projects, although knowledge of probability and statistics should be strengthened. Biology knowledge obtained through the didactic phase of the program and prior undergraduate education was adequate, but advanced or specific knowledge could help students progress on research projects. The curriculum and assessment instruments developed for this program are available for adoption by other bioinformatics programs at http://www.calstatela.edu/SoCalBSI .

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Sansom, Clare. "The way forward?: The growth of systems biology." Biochemist 29, no. 5 (October 1, 2007): 24–26. http://dx.doi.org/10.1042/bio02905024.

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Systems biology is certainly fashionable. In the UK, the Biotechnology and Biological Sciences Research Council has put forward the majority of an investment of well over £70 million to set up six university-based ‘centres of integrative systems biology’. Other countries are making similar investments. A few years ago, however, as with ‘bioinformatics’ a decade or so earlier, it seemed that there were almost as many definitions of systems biology as there were practitioners. It is not too much of an exaggeration to say that almost any computer analysis of a biological problem might have been badged in that way.

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Talbi, El-Ghazali, and Albert Zomaya. "Grids in bioinformatics and computational biology." Journal of Parallel and Distributed Computing 66, no. 12 (December 2006): 1481. http://dx.doi.org/10.1016/j.jpdc.2006.09.001.

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15

Holloway, Eric. "Tutorial: Bioinformatics Basics." Communications of the Blyth Institute 2, no. 2 (August 1, 2020): 35–38. http://dx.doi.org/10.33014/issn.2640-5652.2.2.holloway.1.

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Bioinformatics can appear to be a daunting field, since it combines the complex science of biology with the complex theory of computer science. However, the basics are surprisingly simple. This tutorial gives a glimpse of the tools and techniques needed to get started in the field.

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Najeb Mohammed, Bafreen. "A Review: Genetics Algorithms in Bioinformatics Tools." ICONTECH INTERNATIONAL JOURNAL 5, no. 1 (March 28, 2021): 16–25. http://dx.doi.org/10.46291/icontechvol5iss1pp16-25.

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Bioinformaticians study biological questions by analyzing molecular data with various programs and tools. Today, bioinformatics is used in large number of fields such as microbial genome applications, biotechnology, waste cleanup, Gene therapy, fingerprint and eye detection. The field of bioinformatics, is one of the most prominent areas that our need is increasing, and the demand for it is increasing day by day. Where dealing with this vital and biological information using advanced computer technologies to generate useful information and new discoveries. For this reason, vital bioinformatics is one of the domains that combines both interested and programming at the same time. It provides you with resources for self-learning, the most important information in the field of vital information, and asked questions of those wishing to learn this field. The term bioinformatics was first used in 1968 by Margret Dayhoff, which is a pioneer in this field, but its definition appeared for the first time in 1978. This science arose and developed in conjunction with the emergence and development of computers. It is also referred to as "computational biology."

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Lawlor, Brendan, and Roy D. Sleator. "The roles of code in biology." Science Progress 104, no. 2 (April 2021): 003685042110105. http://dx.doi.org/10.1177/00368504211010570.

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The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a “sweet spot” in the taxonomy—a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the “inverse Conway maneuver” to shape a research team so as to allow dedicated software engineers to interface with researchers working in this “sweet spot.” We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.

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18

Alaie, Adrienne, Virginia Teller, and Wei-gang Qiu. "A Bioinformatics Module for Use in an Introductory Biology Laboratory." American Biology Teacher 74, no. 5 (May 1, 2012): 318–22. http://dx.doi.org/10.1525/abt.2012.74.5.6.

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Since biomedical science has become increasingly data-intensive, acquisition of computational and quantitative skills by science students has become more important. For non-science students, an introduction to biomedical databases and their applications promotes the development of a scientifically literate population. Because typical college introductory biology laboratories do not include experiences of this type, we present a bioinformatics module that can easily be included in a 90-minute session of a biology course for both majors and non-majors. Students completing this computational, inquiry-based module observed the value of computer-assisted analysis. The module gave students an understanding of how to read files in a biological database (GenBank) and how to use a software tool (BLAST) to mine the database.

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19

Paraskevopoulou-Kollia, Efrosyni-Alkisti, and Pantelis G. Bagos. "Bioinformatics Education in Greece: A Survey." Biosaintifika: Journal of Biology & Biology Education 9, no. 1 (March 12, 2017): 1. http://dx.doi.org/10.15294/biosaintifika.v9i1.7257.

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<p>Bioinformatics is an interdisciplinary field, placed at the interface of Biology, Mathematics and Computer Science. In this work, we tried for the first time to investigate the current situation of Bioinformatics education in Greece. We searched the online resources of all relevant University Departments for Bioinformatics or relevant courses. We found that all the Departments of Biological Sciences include in their curricula courses dedicated to Bioinformatics, but this is not the case for Departments of Computer Science, Computer Engineering, or Medical Schools. Despite the fact that large Universities played a crucial role in establishing Bioinformatics research and education in Greece, we observe that Universities of the periphery invest in the field, by including more relevant courses in the curricula and appointing faculty members trained in the field. In order for us to “triangulate” we didn’t confine ourselves to online resources and descriptive statistics but we also included interviews so as to have a more spherical view of the subject under discussion. The interviews provided useful insights regarding the teaching methods used by bioinformatics tutors, their attitudes and the difficulties they encounter. The tutors mentioned also the material that they choose, the audience’s attraction techniques and the feedback they receive.</p>

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20

Zheng, H., K. C. Wiese, and F. Azuaje. "Special Section on Bioinformatics and Computational Biology." IEEE Transactions on NanoBioscience 4, no. 3 (September 2005): 205–6. http://dx.doi.org/10.1109/tnb.2005.853643.

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21

Luscombe, N. M., D. Greenbaum, and M. Gerstein. "What is Bioinformatics? A Proposed Definition and Overview of the Field." Methods of Information in Medicine 40, no. 04 (2001): 346–58. http://dx.doi.org/10.1055/s-0038-1634431.

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Summary Background: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Objectives: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Methods: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Results and Conclusions: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data). Additional information includes the text of scientific papers and “relationship data” from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

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ADEBO, PHILIP. "A PRIMER ON BIOINFORMATICS." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 4 (May 1, 2018): 9. http://dx.doi.org/10.23956/ijarcsse.v8i4.589.

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ABSTRACT Today biological research is experiencing explosive growth in academic, industry, and government sectors. Bioinformatics has emerged to make sense of such high volume and complex data. It is an interdisciplinary field that combines computer science, biology, engineering, and mathematics in order to develop methods, techniques, and tools for analyzing and interpreting biological data. It uses computational approaches to solve complex biological problems and analyze large-volume of biological data. This paper provides a primer on bioinformatics.

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23

Medvedev, Paul. "Modeling biological problems in computer science: a case study in genome assembly." Briefings in Bioinformatics 20, no. 4 (January 30, 2018): 1376–83. http://dx.doi.org/10.1093/bib/bby003.

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Abstract As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded.

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24

de Lorenzo, V., L. Serrano, and A. Valencia. "Synthetic Biology: challenges ahead." Bioinformatics 22, no. 2 (January 15, 2006): 127–28. http://dx.doi.org/10.1093/bioinformatics/btk018.

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Kovarik, Dina N., Davis G. Patterson, Carolyn Cohen, Elizabeth A. Sanders, Karen A. Peterson, Sandra G. Porter, and Jeanne Ting Chowning. "Bioinformatics Education in High School: Implications for Promoting Science, Technology, Engineering, and Mathematics Careers." CBE—Life Sciences Education 12, no. 3 (September 2013): 441–59. http://dx.doi.org/10.1187/cbe.12-11-0193.

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We investigated the effects of our Bio-ITEST teacher professional development model and bioinformatics curricula on cognitive traits (awareness, engagement, self-efficacy, and relevance) in high school teachers and students that are known to accompany a developing interest in science, technology, engineering, and mathematics (STEM) careers. The program included best practices in adult education and diverse resources to empower teachers to integrate STEM career information into their classrooms. The introductory unit, Using Bioinformatics: Genetic Testing, uses bioinformatics to teach basic concepts in genetics and molecular biology, and the advanced unit, Using Bioinformatics: Genetic Research, utilizes bioinformatics to study evolution and support student research with DNA barcoding. Pre–post surveys demonstrated significant growth (n = 24) among teachers in their preparation to teach the curricula and infuse career awareness into their classes, and these gains were sustained through the end of the academic year. Introductory unit students (n = 289) showed significant gains in awareness, relevance, and self-efficacy. While these students did not show significant gains in engagement, advanced unit students (n = 41) showed gains in all four cognitive areas. Lessons learned during Bio-ITEST are explored in the context of recommendations for other programs that wish to increase student interest in STEM careers.

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Schmidt, H., and M. Jirstrand. "Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology." Bioinformatics 22, no. 4 (November 29, 2005): 514–15. http://dx.doi.org/10.1093/bioinformatics/bti799.

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Bateman, A., and A. Valencia. "Structural genomics meets computational biology." Bioinformatics 22, no. 19 (October 1, 2006): 2319. http://dx.doi.org/10.1093/bioinformatics/btl426.

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Payne, Joshua L., Nicholas A. Sinnott-Armstrong, and Jason H. Moore. "Exploiting graphics processing units for computational biology and bioinformatics." Interdisciplinary Sciences: Computational Life Sciences 2, no. 3 (July 25, 2010): 213–20. http://dx.doi.org/10.1007/s12539-010-0002-4.

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Fogg, Christiana N., Diane E. Kovats, and Bonnie Berger. "2017 ISCB Accomplishment by a Senior Scientist Award: Pavel Pevzner." F1000Research 6 (June 26, 2017): 1001. http://dx.doi.org/10.12688/f1000research.11588.1.

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The International Society for Computational Biology (ISCB) recognizes an established scientist each year with the Accomplishment by a Senior Scientist Award for significant contributions he or she has made to the field. This award honors scientists who have contributed to the advancement of computational biology and bioinformatics through their research, service, and education work. Pavel Pevzner, PhD, Ronald R. Taylor Professor of Computer Science and Director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego, has been selected as the winner of the 2017 Accomplishment by a Senior Scientist Award. The ISCB awards committee, chaired by Dr. Bonnie Berger of the Massachusetts Institute of Technology, selected Pevzner as the 2017 winner. Pevzner will receive his award and deliver a keynote address at the 2017 Intelligent Systems for Molecular Biology-European Conference on Computational Biology joint meeting (ISMB/ECCB 2017) held in Prague, Czech Republic from July 21-July 25, 2017. ISMB/ECCB is a biennial joint meeting that brings together leading scientists in computational biology and bioinformatics from around the globe.

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Dumancas, Gerard G., Indra Adrianto, Ghalib Bello, and Mikhail Dozmorov. "Current Developments in Machine Learning Techniques in Biological Data Mining." Bioinformatics and Biology Insights 11 (January 1, 2017): 117793221668754. http://dx.doi.org/10.1177/1177932216687545.

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This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

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Vega-Rodríguez, Miguel A., and Álvaro Rubio-Largo. "Parallelism in computational biology." International Journal of High Performance Computing Applications 32, no. 3 (December 7, 2016): 317–20. http://dx.doi.org/10.1177/1094342016677599.

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Computational biology allows and encourages the application of many different parallelism-based technologies. This special issue brings together high-quality state-of-the-art contributions about parallelism-based technologies in computational biology, from different points of view or perspectives, that is, from diverse high-performance computing applications. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2015 (the Third International Workshop on Parallelism in Bioinformatics, and part of IEEE ISPA 2015 ). The domains and topics covered in these seven papers are timely and important, and the authors have done an excellent job of presenting the material.

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Liu, Lin, and Hao Wang. "The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine." Current Bioinformatics 14, no. 3 (March 7, 2019): 200–210. http://dx.doi.org/10.2174/1574893614666190102125403.

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Background:Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.Objective:This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.Conclusion:Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

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Hill, Anthony D., Jonathan R. Tomshine, Emma M. B. Weeding, Vassilios Sotiropoulos, and Yiannis N. Kaznessis. "SynBioSS: the synthetic biology modeling suite." Bioinformatics 24, no. 21 (August 30, 2008): 2551–53. http://dx.doi.org/10.1093/bioinformatics/btn468.

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Mirschel, Sebastian, Katrin Steinmetz, Michael Rempel, Martin Ginkel, and Ernst Dieter Gilles. "ProMoT: modular modeling for systems biology." Bioinformatics 25, no. 5 (January 15, 2009): 687–89. http://dx.doi.org/10.1093/bioinformatics/btp029.

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Dress, Andreas, Michal Linial, Olga Troyanskaya, and Martin Vingron. "ISCB/SPRINGER series in computational biology." Bioinformatics 29, no. 24 (November 8, 2013): 3246–47. http://dx.doi.org/10.1093/bioinformatics/btt630.

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Dress, A., M. Linial, O. Troyanskaya, and M. Vingron. "ISCB/SPRINGER series in computational biology." Bioinformatics 30, no. 1 (December 18, 2013): 146–47. http://dx.doi.org/10.1093/bioinformatics/btt670.

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37

Fielding, Alan. "Applications of fractal geometry to biology." Bioinformatics 8, no. 4 (1992): 359–66. http://dx.doi.org/10.1093/bioinformatics/8.4.359.

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Steinhauser, D., B. Usadel, A. Luedemann, O. Thimm, and J. Kopka. "CSB.DB: a comprehensive systems-biology database." Bioinformatics 20, no. 18 (July 9, 2004): 3647–51. http://dx.doi.org/10.1093/bioinformatics/bth398.

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Bodlaender, H. L., R. G. Downey, M. R. Fellows, M. T. Hallett, and H. T. Wareham. "Parameterized complexity analysis in computational biology." Bioinformatics 11, no. 1 (1995): 49–57. http://dx.doi.org/10.1093/bioinformatics/11.1.49.

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Tsonis, Panagiotis A., and Anastasios A. Tsonis. "Chaos: principles and implications in biology." Bioinformatics 5, no. 1 (1989): 27–32. http://dx.doi.org/10.1093/bioinformatics/5.1.27.

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Gilbert, D. G. "Two Hypercard calculators for molecular biology." Bioinformatics 6, no. 2 (1990): 113–16. http://dx.doi.org/10.1093/bioinformatics/6.2.113.

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Fogg, C. N., and D. E. Kovats. "International Society for Computational Biology Honors Goncalo Abecasis with Top Bioinformatics/Computational Biology Award for 2013." Bioinformatics 29, no. 12 (May 9, 2013): 1586–87. http://dx.doi.org/10.1093/bioinformatics/btt251.

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ZHOU, SHUIGENG, RUIQI LIAO, and JIHONG GUAN. "WHEN CLOUD COMPUTING MEETS BIOINFORMATICS: A REVIEW." Journal of Bioinformatics and Computational Biology 11, no. 05 (October 2013): 1330002. http://dx.doi.org/10.1142/s0219720013300025.

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In the past decades, with the rapid development of high-throughput technologies, biology research has generated an unprecedented amount of data. In order to store and process such a great amount of data, cloud computing and MapReduce were applied to many fields of bioinformatics. In this paper, we first introduce the basic concepts of cloud computing and MapReduce, and their applications in bioinformatics. We then highlight some problems challenging the applications of cloud computing and MapReduce to bioinformatics. Finally, we give a brief guideline for using cloud computing in biology research.

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Golding, G. Brian. "DNA and the revolutions of molecular evolution, computational biology, and bioinformatics." Genome 46, no. 6 (December 1, 2003): 930–35. http://dx.doi.org/10.1139/g03-108.

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The discovery of the structure of DNA was a necessary prerequisite for determining the sequence of DNA molecules. Technological advances have now made it possible to sequence DNA rapidly and has resulted in public databases with over 30 billion nucleotides of known sequence. The analysis of these data has lead to new fields of science and to amazing advances in our understanding of evolution.Key words: bioinformatics, computational biology, molecular evolution, DNA, structure, DNA sequence.

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Kovats, Diane, Ron Shamir, and Christiana Fogg. "Bonnie Berger named ISCB 2019 ISCB Accomplishments by a Senior Scientist Award recipient." F1000Research 8 (May 23, 2019): 721. http://dx.doi.org/10.12688/f1000research.19219.1.

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The International Society for Computational Biology (ISCB) honors a leader in the fields of computational biology and bioinformatics each year with the Accomplishments by a Senior Scientist Award. This award is the highest honor conferred by ISCB to a scientist who is recognized for significant research, education, and service contributions. Bonnie Berger, Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) is the 2019 recipient of the Accomplishments by a Senior Scientist Award. She is receiving her award and presenting a keynote address at the 2019 Joint International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Basel, Switzerland on July 21-25, 2019.

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Brame, Cynthia J., Wendy M. Pruitt, and Lucy C. Robinson. "A Molecular Genetics Laboratory Course Applying Bioinformatics and Cell Biology in the Context of Original Research." CBE—Life Sciences Education 7, no. 4 (December 2008): 410–21. http://dx.doi.org/10.1187/cbe.08-07-0036.

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Research based laboratory courses have been shown to stimulate student interest in science and to improve scientific skills. We describe here a project developed for a semester-long research-based laboratory course that accompanies a genetics lecture course. The project was designed to allow students to become familiar with the use of bioinformatics tools and molecular biology and genetic approaches while carrying out original research. Students were required to present their hypotheses, experiments, and results in a comprehensive lab report. The lab project concerned the yeast casein kinase 1 (CK1) protein kinase Yck2. CK1 protein kinases are present in all organisms and are well conserved in primary structure. These enzymes display sequence features that differ from other protein kinase subfamilies. Students identified such sequences within the CK1 subfamily, chose a sequence to analyze, used available structural data to determine possible functions for their sequences, and designed mutations within the sequences. After generating the mutant alleles, these were expressed in yeast and tested for function by using two growth assays. The student response to the project was positive, both in terms of knowledge and skills increases and interest in research, and several students are continuing the analysis of mutant alleles as summer projects.

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Dill-McFarland, Kimberly A., Stephan G. König, Florent Mazel, David C. Oliver, Lisa M. McEwen, Kris Y. Hong, and Steven J. Hallam. "An integrated, modular approach to data science education in microbiology." PLOS Computational Biology 17, no. 2 (February 25, 2021): e1008661. http://dx.doi.org/10.1371/journal.pcbi.1008661.

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We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

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Norris, Robert F. "Weed Science Society of America weed biology survey." Weed Science 45, no. 3 (June 1997): 343–48. http://dx.doi.org/10.1017/s0043174500092961.

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WSSA members were surveyed in 1993–1994 to determine their perceptions of the contribution of weed biology to weed management. A questionnaire was included in the society newsletter, from which 152 responses were returned by mail or collected at the 1994 annual meeting. Over half the respondents felt that the overall contribution of weed biology to weed management had been moderate to high. Aspects of population dynamics and competition emerged as the areas that respondents felt should have the greatest impact on weed management in the future. The areas of computer modeling, interactions between weeds and other pests, and seedbank dynamics were predicted to show the greatest increases in importance in the future. The relative importance of taxonomy and weed identification was expected to decrease. Allelopathy, morphology and anatomy, and genetics and evolution were considered least likely to be important to weed management.

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Ferreira, Gloria C., Jenna Oberstaller, Renée Fonseca, Thomas E. Keller, Swamy Rakesh Adapa, Justin Gibbons, Chengqi Wang, et al. "Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich’s ataxia, and other rare iron-related diseases." F1000Research 8 (July 19, 2019): 1135. http://dx.doi.org/10.12688/f1000research.19140.1.

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Background: Basic and clinical scientific research at the University of South Florida (USF) have intersected to support a multi-faceted approach around a common focus on rare iron-related diseases. We proposed a modified version of the National Center for Biotechnology Information’s (NCBI) Hackathon-model to take full advantage of local expertise in building “Iron Hack”, a rare disease-focused hackathon. As the collaborative, problem-solving nature of hackathons tends to attract participants of highly-diverse backgrounds, organizers facilitated a symposium on rare iron-related diseases, specifically porphyrias and Friedreich’s ataxia, pitched at general audiences. Methods: The hackathon was structured to begin each day with presentations by expert clinicians, genetic counselors, researchers focused on molecular and cellular biology, public health/global health, genetics/genomics, computational biology, bioinformatics, biomolecular science, bioengineering, and computer science, as well as guest speakers from the American Porphyria Foundation (APF) and Friedreich’s Ataxia Research Alliance (FARA) to inform participants as to the human impact of these diseases. Results: As a result of this hackathon, we developed resources that are relevant not only to these specific disease-models, but also to other rare diseases and general bioinformatics problems. Within two and a half days, “Iron Hack” participants successfully built collaborative projects to visualize data, build databases, improve rare disease diagnosis, and study rare-disease inheritance. Conclusions: The purpose of this manuscript is to demonstrate the utility of a hackathon model to generate prototypes of generalizable tools for a given disease and train clinicians and data scientists to interact more effectively.

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Delarue, Marc, and Patrice Koehl. "Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?" F1000Research 7 (July 24, 2018): 1125. http://dx.doi.org/10.12688/f1000research.14870.1.

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Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.

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