Dataset Information

Committee machine that votes for similarity between materials.

ABSTRACT: A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

SUBMITTER: Nguyen DN

PROVIDER: S-EPMC6211525 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Committee machine that votes for similarity between materials.

Nguyen Duong-Nguyen DN Pham Tien-Lam TL Nguyen Viet-Cuong VC Ho Tuan-Dung TD Tran Truyen T Takahashi Keisuke K Dam Hieu-Chi HC

IUCrJ 20181030 Pt 6

A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well cha ...[more]

PMID: 30443367

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Project description:ImportanceThe US Food and Drug Administration (FDA) often relies on independent advisory committees when making decisions about the approval of prescription drugs or their withdrawal from the market. These committees provide the FDA with valuable insight and an opportunity to build public trust through transparent deliberation, but recent controversies have raised questions about the optimal use of FDA advisory committees.ObjectiveTo assess the frequency, purposes, and voting outcomes of human drug advisory committees convened from 2010 to 2021 and the FDA's corresponding actions.Design, setting, and participantsThis qualitative study used a manual review of meeting summaries prepared by FDA staff for the 18 human drug advisory committees active at any time from January 1, 2010, to December 31, 2021, as well as FDA announcements and press releases, drug labels and approval data, industry publications, and company press releases.Main outcomes and measuresOutcomes of votes on regulatory questions were recorded using meeting minutes. Alignment of FDA action with advisory votes for new drugs and indications was judged as of 1 year after the vote was held and as of November 30, 2022.ResultsThe FDA held 409 human drug advisory committee meetings from 2010 to 2021. Committees were convened less frequently over time, from a high of 50 in 2012 to a low of 18 in 2020 and 2021. Much of this decrease occurred at committee meetings involving votes on initial approvals, which declined from a high of 26 in 2012 to a low of 8 in 2021. Overall, FDA regulatory actions aligned with 262 of 298 advisory committee votes on initial approvals, supplemental approvals, withdrawals of approval, and safety actions (88%). Approval followed 142 of 147 positive votes for initial approvals (97%) and 33 of 36 positive votes for supplemental indications (92%), while nonapproval followed 40 of 60 negative votes on initial approvals (67%) and 18 of 21 negative votes on supplemental indications (86%).Conclusions and relevanceIn this qualitative study, there was consistent alignment between advisory votes and FDA action across years and subject areas, but the number of meetings decreased over time. Discordance between FDA actions and advisory committee votes was most frequently an approval after a negative vote. This study demonstrated that these committees have played a key role in the FDA's decision-making process but that the FDA sought independent expert advice less frequently over time even as it continued to follow it. The role of advisory committees in the current regulatory landscape should be more clearly and publicly defined.

Dataset Information

Committee machine that votes for similarity between materials.

Publications

Committee machine that votes for similarity between materials.

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