Additional file 5 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 5: Table S5. PRS performance comparisons for height in UK Bioban

Additional file 3 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 3: Table S3. rPRS and sPRS results on A

Additional file 6 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 6: Table S6. Performance comparisons between PRS and Lassosu

Additional file 4 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 4: Table S4. PRS performance comparisons for hypertension in UK Bioban

Additional file 2 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 2: Table S2. PRS performance after excluding genetically close individuals from the test se

Additional file 7 of Overestimated prediction using polygenic prediction derived from summary statistics

Additional file 7: Fig. S1. The number of test set subjects required to gain statistical significance (P < 0.01) for hypertension using UK Bioban

Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network

The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Fréchet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively.</p