Étiquette : biomedical research

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Deep Learning in Mining Biological Data

Authors : Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature.

Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems.

To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data.

This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided.

Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

URL : Deep Learning in Mining Biological Data

DOI : https://doi.org/10.1007/s12559-020-09773-x

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The move to open: medical library leadership in scholarly communication

Author : Chris Shaffer

Over the years, health sciences librarians have been change agents, leading the charge on issues of importance to the profession and the communities we serve. From its founding in 1898 with the Exchange, the Medical Library Association (MLA) has been dedicated to improving access to health information.

In 2003, the Board of Directors published a statement supporting open access to information generated from federally funded scientific and medical research and maintained that having access to timely, relevant, and accurate information is vital to the health of the nation and its education and research programs.

At some financial risk, the association made the Journal of the Medical Library Association (JMLA) open access and published the entire archive of JMLA and its predecessor, the Bulletin of the Medical Library Association, in PubMed Central. Nearly two decades later, the promise of open access and open science finally seems to be coming to fruition.

In the 2020 Janet Doe Lecture, Chris Shaffer, AHIP, described the ways that MLA has led the profession, standing behind a shared vision and “walking the walk.” In challenging listeners to embrace open science, he affirmed that, as leaders in improving access to health sciences information since 1898, medical librarians must work in the open science arena to realize our vision “that quality information is essential for improved health.”

URL : The move to open: medical library leadership in scholarly communication

DOI : https://doi.org/10.5195/jmla.2021.1127

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Biomedical Data Sharing Among Researchers: A Study from Jordan

Authors : Lina Al-Ebbini, Omar F Khabour, Karem H Alzoubi, Almuthanna K Alkaraki

Background

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.

Methods

This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent’s attitudes toward sharing of biomedical data.

Results

Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included “lack of regulations” (33.5%), “access to research data should be limited to the research team” (29.5%), “no place to deposit the data” (6.5%), and “lack of funding for data deposition” (6.0%).

Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).

Conclusion

About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.

URL : Biomedical Data Sharing Among Researchers: A Study from Jordan

DOI : https://doi.org/10.2147/JMDH.S284294

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EN

Open Bioeconomy—A Bibliometric Study on the Accessibility of Articles in the Field of Bioeconomy

Authors : Marianne Duquenne, Hélène Pros, Joachim Schöpfel, Franck Dumeignil

Open access (OA) to scientific information is one of the major challenges and objectives of actual public research policy. The purpose of this paper is to assess the degree of openness of scientific articles on bioeconomy, as one of the emergent research fields at the crossroads of several disciplines and with high societal and industrial impact.

Based on a Web of Science (WoS) corpus of 2489 articles published between 2015 and 2019, we calculated bibliometric indicators, explored the openness of each article and assessed the share of journals, countries and research areas of these articles.

The results show a sharp increase and diversification of articles in the field of bioeconomy, with a beginning long tail distribution. 45.6% of the articles are freely available and the share of OA articles is steadily increasing, from 31% in 2015 to 52% in 2019.

Gold is the most important variant of OA. Open access is low in the applied research areas of chemical, agricultural and environmental engineering but higher in the domains of energy and fuels, forestry and green and sustainable science and technology.

The UK and the Netherlands have the highest rates of OA articles, followed by Spain and Germany. The funding rate of OA articles is higher than of non-OA articles. This is the first bibliometric study on open access to articles on bioeconomy.

The results can be useful for the further development of OA editorial and funding criteria in the field of bioeconomy.

URL : Open Bioeconomy—A Bibliometric Study on the Accessibility of Articles in the Field of Bioeconomy

DOI : https://doi.org/10.3390/publications8040055

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fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

Authors : René Bernard, Tracey L. Weissgerber, Evgeny Bobrov, Stacey J. Winham, Ulrich Dirnag, Nico Riedel

Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific evidence.

Continuously expanding ´file drawers’ of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature.

Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don’t know how to use them.

Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms.

This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.

URL : fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

DOI : https://doi.org/10.1042/CS20201125

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Scholarly publishing and journal targeting in the time of the Coronavirus Disease 2019 (COVID-19) pandemic: a cross-sectional survey of rheumatologists and other specialists

Authors : Latika Gupta, Armen Yuri Gasparyan, Olena Zimba, Durga Prasanna Misra

The evolving research landscape in the time of the Coronavirus disease 2019 (COVID-19) pandemic calls for greater understanding of the perceptions of scholars regarding the current state and future of publishing.

An anonymised and validated e-survey featuring 30 questions was circulated among rheumatologists and other specialists over social media to understand preferences while choosing target journals, publishing standards, commercial editing services, preprint archiving, social media and alternative publication activities.

Of 108 respondents, a significant proportion were clinicians (68%), researchers (60%) and educators (47%), with median 23 publications and 15 peer-review accomplishments. The respondents were mainly rheumatologists from India, Ukraine and Turkey.

While choosing target journals, relevance to their field (69%), PubMed Central archiving (61%) and free publishing (59%) were the major factors. Thirty-nine surveyees (36%) claimed that they often targeted local journals for publishing their research. However, only 18 (17%) perceived their local society journals as trustworthy.

Occasional publication in the so-called predatory journals (5, 5%) was reported and obtaining support from commercial editing agencies to improve English and data presentation was not uncommon (23, 21%).

The opinion on preprint archiving was disputed; only one-third believed preprints were useful. High-quality peer review (56%), full and immediate open access (46%) and post-publication social media promotion (32%) were identified as key anticipated features of scholarly publishing in the foreseeable future.

These perceptions of surveyed scholars call for greater access to free publishing, attention to proper usage of English and editing skills, and a larger role for engagement over social media.

URL : Scholarly publishing and journal targeting in the time of the Coronavirus Disease 2019 (COVID-19) pandemic: a cross-sectional survey of rheumatologists and other specialists

DOI : https://doi.org/10.1007/s00296-020-04718-x

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EN

ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

Authors: Nico Riedel, Miriam Kip, Evgeny Bobro

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion.

We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (n = 8689) and randomly selected from PubMed (n = 1500) we iteratively developed a set of derived keyword categories.

ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code).

To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.73 and specificity of 0.97.

Open Data was detected for 11.5% (n = 91) of publications. Open Code was detected for 1.4% (n = 11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science.

Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub.

URL : ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

DOI : http://doi.org/10.5334/dsj-2020-042