The implementation of e-learning tools to enhance undergraduate bioinformatics teaching and learning: a case study in the National University of Singapore

BMC Bioinformatics10SUPPL. 15BBMI

Metacuration Standards and Minimum Information about a Bioinformatics Investigation

Many bioinformatics databases published in journals are here this year and gone the next. There is generally (i) no requirement, mandatory or otherwise, by reviewers, editors or publishers for full disclosure of how databases are built and how they are maintained; (ii) no standardized requirement for data in public access databases to be kept as backup for release and access when a project ends, when funds expire and website terminates; (iii) the case of proprietary resources, there is no requirement for data to be kept in escrow for release under stated conditions such as when a published database disappears due to company closure. Consequently, much of the biological databases published in the past twenty years are easily lost, even though the publications describing or referencing these databases and webservices remain. Given the volume of publications today, even though it is practically possible for reviewers to re-create databases as described in a manuscript, there is usually insufficient disclosure and raw data for this to be done, even if there is sufficient time and resources available to perform this. Consequently, verification and validation is assumed, and claims of the paper accepted as true and correct at face value. A solution to this growing problem is to experiment with some kind of minimum standards of reporting such as the Minimum Information About a Bioinformatics Investigation (MIABi) and standardized requirements of data deposition and escrow for enabling persistence and reproducibility. With easy availability of cloud computing, such a level of reproducibility can become a reality in the near term. Through standards in meta-curation and minimum standards of reporting that uphold the tenets of scientific reproducibility, verifiability, sustainability and continuity of data resources, the knowledge preserved will underpin tomorrow's scientific research. Other issues include disambiguation of authors or database names, and unique identifiers to support non-repudiability, possibly in multiple languages. The International Conference on Bioinformatics and its publications are now in the process of making attempts at addressing these issues and this presentation will highlight some of the current efforts

Advancing standards for bioinformatics activities: persistence, reproducibility, disambiguation and Minimum Information About a Bioinformatics investigation (MIABi)

The 2010 International Conference on Bioinformatics, InCoB2010, which is the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet) has agreed to publish conference papers in compliance with the proposed Minimum Information about a Bioinformatics investigation (MIABi), proposed in June 2009. Authors of the conference supplements in BMC Bioinformatics, BMC Genomics and Immunome Research have consented to cooperate in this process, which will include the procedures described herein, where appropriate, to ensure data and software persistence and perpetuity, database and resource re-instantiability and reproducibility of results, author and contributor identity disambiguation and MIABi-compliance. Wherever possible, datasets and databases will be submitted to depositories with standardized terminologies. As standards are evolving, this process is intended as a prelude to the 100 BioDatabases (BioDB100) initiative whereby APBioNet collaborators will contribute exemplar databases to demonstrate the feasibility of standards-compliance and participate in refining the process for peer-review of such publications and validation of scientific claims and standards compliance. This testbed represents another step in advancing standards-based processes in the bioinformatics community which is essential to the growing interoperability of biological data, information, knowledge and computational resources

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.Comment: 5 page

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve subsystems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others , a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

International audienceThe I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve subsystems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others , a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation

Carrier control in 2D transition metal dichalcogenides with-Al2O3 dielectric (vol 9, 8769, 2019)

10.1038/s41598-021-96557-4SCIENTIFIC REPORTS11

Using the Johns Hopkins ACG Case-Mix System for population segmentation in a hospital-based adult patient population in Singapore

Objective Population health management involves risk characterisation and patient segmentation. Almost all population segmentation tools require comprehensive health information spanning the full care continuum. We assessed the utility of applying the ACG System as a population risk segmentation tool using only hospital data.Design Retrospective cohort study.Setting Tertiary hospital in central Singapore.Participants 100 000 randomly selected adult patients from 1 January to 31 December 2017.Intervention Hospital encounters, diagnoses codes and medications prescribed to the participants were used as input data to the ACG System.Primary and Secondary Outcome Measures Hospital costs, admission episodes and mortality of these patients in the subsequent year (2018) were used to assess the utility of ACG System outputs such as resource utilisation bands (RUBs) in stratifying patients and identifying high hospital care users.Results Patients placed in higher RUBs had higher prospective (2018) healthcare costs, and were more likely to have healthcare costs in the top five percentile, to have three or more hospital admissions, and to die in the subsequent year. A combination of RUBs and ACG System generated rank probability of high healthcare costs, age and gender that had good discriminatory ability for all three outcomes, with area under the receiver-operator characteristic curve (AUC) values of 0.827, 0.889 and 0.876, respectively. Application of machine learning methods improved AUCs marginally by about 0.02 in predicting the top five percentile of healthcare costs and death in the subsequent year.Conclusion A population stratification and risk prediction tool can be used to appropriately segment populations in a hospital patient population even with incomplete clinical data