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: closed (31 December 2022) | Viewed by 36382
Special Issue Editors
Interests: computational biology; structural biology; molecular simulations; molecular evolution; machine learning
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.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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
