Publications by authors named "Ewan Straiton"
Background: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression.
Method: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities.
Article Synopsis
- High-throughput phenomic projects often deal with complex data from various treatment and control groups, which can complicate analyses due to variations over time, necessitating a method to effectively use local controls to enhance analytic accuracy.
- The authors present 'soft windowing', a method that assigns weighted importance to control data based on their proximity in time to mutant data, leading to reduced false positives (10%) in analyses and increased significant P-values (30%).
- This method is implemented in an R package called SmoothWin, which is publicly accessible and can also be applied to large-scale human phenomic studies such as the UK Biobank.
Article Synopsis
- The text refers to a correction made to a previously published article with the DOI: 10.1038/s42003-018-0226-0.
- The correction is likely important for ensuring the accuracy and integrity of the research findings presented in the original article.
- Readers are encouraged to check the corrected version for updated information that may affect the conclusions or interpretations of the study.
Article Synopsis
- Advances in next generation sequencing have made it easier to study genetics, but understanding genetic causes of eye diseases is still tough due to cost and limited access to human genetic data.
- The International Mouse Phenotyping Consortium conducted a study evaluating 4,364 genes and found that 347 of them affect eye traits, with 75% being previously unknown in eye disease research.
- This significant increase in known genes related to eye conditions could have future implications for understanding eye development and diseases in humans.