Special Issue "Biomolecular Data Science—in Honor of Professor Philip E. Bourne"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 30 August 2022 | Viewed by 259

Special Issue Editors

Dr. Cameron Mura
E-Mail Website
Guest Editor
School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
Interests: computational biology; structural biology; molecular simulations; molecular evolution; machine learning
Prof. Dr. Lei Xie
E-Mail Website
Guest Editor
1. Department of Computer Science, Hunter College & The Graduate Center, The City University of New York, New York, NY 10065, USA
2. Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
Interests: bioinformatics; machine learning; biomedical data; computational drug design; systems biology

Special Issue Information

Dear Colleagues,

Prof Philip E. Bourne (‘Phil’), the founding Dean of the School of Data Science at the University of Virginia, has spent his career exploring and helping to define the intersection of biomolecules and computation—as a practicing scientist and as an academic leader, as well as in conjunction with industry and government. In those 40 years, our knowledge of biomolecular structure, function and evolution (in both health and disease) has rapidly advanced, often with exponential growth. What enabled that? The advances were enabled, in no small part, by Phil’s highly collaborative and foundational work, where two pervasive themes have been (i) the key role of three-dimensional structure, as a bridge between a biomolecule’s sequence and its function, and (ii) computational methodologies and resources, including development of state-of-the-art databases (notably the RCSB Protein Data Bank) and associated data-formats (e.g., the macromolecular crystallographic information file), creating standards and interoperable tools, and developing algorithms such as the widely used combinatorial extension (CE) method for structure alignment. Alongside these foundational, ‘basic research’ advances, Phil’s work and its applications have significantly impacted a vast array of biological domains, including early stage drug discovery, molecular evolution, immunology and more—resulting in over 300 papers and two related books. All the while, Phil has been unwavering in his intense support of public service in government and academia, in open scholarship and research best-practices, and in the professional development of all who have crossed his path, at all levels (students, peers, colleagues).

Anyone who’s worked with Phil has seen that a notable trait in his approach to biosciences (and now data science) is that it is expansive and forward-looking—in a word, ‘visionary’. Phil’s focus in recent years, as it relates to this readership, has turned to “biomedical data sciences”, which can be viewed as the natural evolution (and synthesis) of bioinformatics, computational biology, systems biology, and other allied fields. This Special Issue honors Phil by trying to capture his vision as it relates to biomolecules: how this vision arose, what it can encompass, and with an invitation for reviews and original research papers that share the spirit of that vision.

That vision can be expressed as four elements of data science: systems, design, analysis and value. Biomolecular systems, in a computational sense, relate to underlying infrastructure such as data structures, ontologies, software libraries/tools, etc., that enable discovery. Biomolecular analysis, of late, is dominated by machine learning approaches such as deep learning (for which systems to access training data are critical). In our data science context, design can refer to human–computer interaction, for example, where biomolecular visualization plays a vital role. Lastly, value relates to maximizing the benefit that research has on those it is intended to serve. This issue’s papers exemplify what a field of “biomolecular data sciences” can represent, as a fitting tribute to someone who has endeavored to move the field forward—both via his own work and by his steadfast support of the work of others in the field more broadly.

Dr. Cameron Mura
Prof. Dr. Lei Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science;
  • deep learning;
  • machine learning;
  • structural bioinformatics;
  • biophysics;
  • systems biology

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: To be determined
Authors: Russ B. Altman
Affiliation: Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
Abstract: To be determined

Title: To be determined
Authors: Christine Orengo
Affiliation: Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, UK
Abstract: To be determined

Title: To be determined
Authors: Brian Y. Chen; et al.
Affiliation: Department of Computer Science and Engineering, Lehigh University, Building "C", 113 Research Drive, Room 330, Bethlehem, PA 18015-3006, USA
Abstract: To be determined

Title: To be determined
Authors: Qiangfeng Zhang; et al.
Affiliation: Tsinghua University
Abstract: To be determined

Title: To be determined
Authors: Gert Vriend; et al.
Affiliation: CMBI, Radboud University Nijmegen Medical Centre, 6525 GA 26-28 Nijmegen, The Netherlands
Abstract: To be determined

Title: To be determined
Authors: Stefan Bekiranov; et al.
Affiliation: School of Medicine, University of Virginia
Abstract: To be determined

Title: To be determined
Authors: Judy Blake
Affiliation: Jackson Laboratory, Farmington, USA
Abstract: To be determined

Title: To be determined
Authors: Ying Zhang; et al.
Affiliation: University of Rhode Island, Kingston, United States
Abstract: To be determined

Title: To be determined
Authors: Teresa Przytycka; et al
Affiliation: Computational Biology Brunch, National Center of Biotechnology, NLM, NIH, 8000 Rockville Pike, Bethesda, MD 20894, USA
Abstract: To be determined

Title: Protein Data Bank: A Statistical Analysis of 3D Structure Holdings and Their Usage Across the Medical, Natural, Physical, and Engineering Sciences
Authors: Stephen K. Burley; et al.
Affiliation: Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
Abstract: To be determined

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