scholarly journals A field guide to cultivating computational biology

PLoS Biology ◽  
2021 ◽  
Vol 19 (10) ◽  
pp. e3001419
Author(s):  
Gregory P. Way ◽  
Casey S. Greene ◽  
Piero Carninci ◽  
Benilton S. Carvalho ◽  
Michiel de Hoon ◽  
...  
Keyword(s):  
Computational Biology ◽  
Scientific Progress ◽  
Scientific Field ◽  
Data Driven ◽  
Team Science ◽  
Research Model ◽  
The Past ◽  
Multidisciplinary Data ◽  
Funding Agencies ◽  
Single Discipline

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Machine learning for rediscovering revolutionary ideas of the past

2021 ◽  
pp. 105971232098304
Author(s):  
R Alexander Bentley ◽  
Joshua Borycz ◽  
Simon Carrignon ◽  
Damian J Ruck ◽  
Michael J O’Brien
Keyword(s):  
Language Processing ◽  
Scientific Progress ◽  
Phylogenetic Inference ◽  
The Past ◽  
New Methods ◽  
Inherent Nature ◽  
Social Learning Processes ◽  
High Level ◽  
Scientific Ideas ◽  
Revolutionary Science

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

Cybersecurity Teamwork: A Review of Current Practices and Suggested Improvements

2020 ◽  
Vol 64 (1) ◽  
pp. 451-455
Author(s):  
Richard J. Simonson ◽  
Joseph R. Keebler ◽  
Mathew Lessmiller ◽  
Tyson Richards ◽  
John C. Lee
Keyword(s):  
Cyber Security ◽  
Cyber Attacks ◽  
Team Science ◽  
The Past ◽  
Insight Into ◽  
Current Practices

As cyber-attacks and their subsequent responses have become more frequent and complex over the past decade, research into the performance and effectiveness of cybersecurity teams has gained an immense amount of traction. However, investigation of teamwork in this domain is lacking due to the exclusion of known team competencies and a lack of reliance on team science. This paper serves to provide insight into the benefit that can be gained from utilizing the extant teamwork literature to improve teams’ research and applications in the domain of cyber-security.

Designing For Diversity: Implications for Research and Practice

2020 ◽  
Vol 64 (1) ◽  
pp. 563-567
Author(s):  
Abigail R. Wooldridge ◽  
Rod D. Roscoe ◽  
Rod D. Roscoe ◽  
Shannon C. Roberts ◽  
Rupa Valdez ◽  
...  
Keyword(s):  
Interdisciplinary Teams ◽  
Team Science ◽  
Participatory Methods ◽  
Sampling Strategies ◽  
Design Problems ◽  
Sociotechnical System ◽  
The Past ◽  
Diversity Committee ◽  
The Impact ◽  
Equity And Inclusion

The Diversity Committee of HFES has led sessions at the Annual Meeting for the past three years focused on improving diversity, equity and inclusion in the society as well as providing support to human factors and ergonomics (HF/E) researchers and practitioners who aim to apply HF/E knowledge and principles to improve diversity, equity and inclusion through their work. In this panel, we bring together researchers actively engaged in designing technology and systems by considering issues of diversity, equity and inclusion to share insights and methods. Topics include the thoughtful design of sampling strategies and research approaches, alternative and participatory methods to understand the impact of automation and technology on equity, scoping design problems to be inclusive and equitable through interdisciplinary partnerships, and the application of sociotechnical system design and team science to develop interdisciplinary teams. By sharing our experiences, we hope to prepare others to successfully approach these topics.

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing

2021 ◽  
Author(s):  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Alexander Feigin
Keyword(s):  
Co2 Concentration ◽  
Data Driven ◽  
Time Interval ◽  
Solar Forcing ◽  
Russian Science ◽  
Dynamical Mechanism ◽  
Enso Variability ◽  
The Past ◽  
Sst Variability ◽  
Data Driven Modeling

<p>We investigate the decadal-to-centennial ENSO variability based on nonlinear data-driven stochastic modeling. We construct data-driven model of yearly Niño-3.4 indices reconstructed from paleoclimate proxies based on three different sea-surface temperature (SST) databases at the time interval from 1150 to 1995 [1]. The data-driven model is forced by the solar activity and CO2 concentration signals. We find the persistent antiphasing relationship between the solar forcing and Niño-3.4 SST on the bicentennial time scale. The dynamical mechanism of such a response is discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056)</p><p>1. Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications, Journal of Climate, 26(7), 2329-2352.</p>

The vigorous development of data driven astronomy education and public outreach (DAEPO)

2019 ◽  
Vol 15 (S367) ◽  
pp. 199-209
Author(s):  
Shanshan Li ◽  
Chenzhou Cui ◽  
Cuilan Qiao ◽  
Dongwei Fan ◽  
Changhua Li ◽  
...  
Keyword(s):  
Best Practices ◽  
Rapid Evolution ◽  
Internet Technology ◽  
Data Driven ◽  
Public Outreach ◽  
Continuous Change ◽  
Astronomy Education ◽  
The Past ◽  
Education And Public Outreach ◽  
Science Educators

AbstractAstronomy education and public outreach (EPO) is one of the important part of the future development of astronomy. During the past few years, as the rapid evolution of Internet and the continuous change of policy, the breeding environment of science EPO keep improving and the number of related projects show a booming trend. EPO is no longer just a matter of to teachers and science educators but also attracted the attention of professional astronomers. Among all activates of astronomy EPO, the data driven astronomy education and public outreach (abbreviated as DAEPO) is special and important. It benefits from the development of Big Data and Internet technology and is full of flexibility and diversity. We will present the history, definition, best practices and prospective development of DAEPO for better understanding this active field.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Comparative analysis of molecular representations in prediction of cancer drug combination synergy and sensitivity

2021 ◽  
Author(s):  
Bulat Zagidullin ◽  
Ziyan Wang ◽  
Yuanfang Guan ◽  
Esa Pitkänen ◽  
Jing Tang
Keyword(s):  
Drug Combination ◽  
High Throughput Screening ◽  
Data Driven ◽  
Cancer Drug ◽  
Molecular Fingerprints ◽  
Rule Based ◽  
Drug Synergy ◽  
The Past ◽  
Optimal Representation ◽  
Preclinical Drug Development

Application of machine and deep learning (ML/DL) methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel DL solutions in relation to established techniques. To this end we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high throughput screening studies, comprising 64,200 unique combinations of 4,153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular fingerprints and quantify their similarity by adapting Centred Kernel Alignment metric. Our work demonstrates that in order to identify an optimal representation type it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

scholarly journals Technoversal Leader: Triumphant Leader of the Technological Era

2020 ◽  
Vol 23 (1) ◽  
pp. 440
Author(s):  
Hakan Kapucu
Keyword(s):  
Significant Role ◽  
Scientific Knowledge ◽  
New World ◽  
World Order ◽  
Digital Transformation ◽  
Business Leaders ◽  
Data Driven ◽  
Critical Component ◽  
New World Order ◽  
The Past

The new world order reminds disruptions and turmoil. Exponentially-developing technology plays a significant role in causing these radical changes. These rapidly-changing conditions affect leaders with all humans. As scientific knowledge, digital transformation, technology is a backbone at the point that humanity has reached. Thus, it has become a critical component, which affects leader behaviors and the skillset expected from them. In this context, this article introduces a new leader who distinguishes from other styles. This distinction arises from the skills that leaders must adopt in the future are different than the past, from the reality of the earth’s being on the edge of collapse, business leaders’ being obliged to act upon it. And along with these specific behaviors, the leaders’ having data-driven mindsets, being technology adept.

Motor Development: A Perspective on the Past, the Present, and the Future

2021 ◽  
pp. 1-10
Author(s):  
Jane E. Clark ◽  
Jill Whitall
Keyword(s):  
Physical Education ◽  
Motor Development ◽  
Academic Discipline ◽  
Scientific Field ◽  
Grand Challenges ◽  
Scientific Area ◽  
The Past ◽  
The Future ◽  
Area Of Study

In 1981, George Brooks provided a review of the academic discipline of physical education and its emerging subdisciplines. Forty years later, the authors review how the field has changed from the perspective of one subdiscipline, motor development. Brooks’s text sets the scene with four chapters on motor development from leaders in the field, including G. Lawrence Rarick, to whom the book is dedicated. From this beginning, the paper describes the evolving scientific perspectives that have emerged since 1981. Clearly, from its past to the present, motor development as a scientific field has itself developed into a robust and important scientific area of study. The paper ends with a discussion of the grand challenges for kinesiology and motor development in the next 40 years.

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