| 1. |
- Boström, Mathias, et al.
(författare)
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Lithium atom storage in nanoporous cellulose via surface-induced Li-2 breakage
- 2013
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Ingår i: Europhysics letters. - : IOP Publishing. - 0295-5075 .- 1286-4854. ; 104:6, s. 63003-
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Tidskriftsartikel (refereegranskat)abstract
- We demonstrate a physical mechanism that enhances a splitting of diatomic Li-2 at cellulose surfaces. The origin of this splitting is a possible surface-induced diatomic-excited-state resonance repulsion. The atomic Li is then free to form either physical or chemical bonds with the cellulose surface and even diffuse into the cellulose layer structure. This allows for an enhanced storage capacity of atomic Li in nanoporous cellulose.
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| 2. |
- de Vries, Paul S., et al.
(författare)
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Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study
- 2017
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5x10(-8) is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5x10(-8)), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
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| 3. |
- Kang, Yanlei, et al.
(författare)
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AFTGAN : prediction of multi-type PPI based on attention free transformer and graph attention network
- 2023
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 39:2
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Protein–protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework.Results: The experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset.Availability and implementation: The source code is publicly available at https://github.com/1075793472/AFTGAN.Supplementary information: Supplementary data are available at Bioinformatics online.
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