Daniel Morgan
Science for Life Laboratory
12 Papers
1 Citations
Daniel Morgan is an academic researcher from Science for Life Laboratory. The author has contributed to research in topics: Gene regulatory network & Inference. The author has an hindex of 2, co-authored 9 publications. Previous affiliations of Daniel Morgan include Ohio State University & Brigham and Women's Hospital.
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Papers
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data
Deniz Seçilmiş,Thomas Hillerton,Daniel Morgan,Andreas Tjärnberg,Sven Nelander,Torbjörn E. M. Nordling,Erik L. L. Sonnhammer +6 more
- 09 Nov 2020
TL;DR: A gene reduction pipeline is developed in which uninformative genes are eliminated from the system using a selection criterion based on SNR, until reaching an informative subset, allowing inference of accurate subset GRNs.
A generalized framework for controlling FDR in gene regulatory network inference.
TL;DR: Nestedbootstrapping is developed, which applies a bootstrapping protocol to GRN inference, and by repeated bootstrap runs assesses the stability of the estimated support values, and provides a general method to control the false discovery rate of GRn inference that can be applied to any setting of inference parameters, noise level, or data properties.
gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit
TL;DR: GpuZoo as discussed by the authors is a GPU-accelerated implementation of gene regulatory network inference in large-scale genomic studies with cost-effective performance using MATLAB and Python.
2
Towards Reliable Gene Regulatory Network Inference
Daniel Morgan
- 01 Jan 2019
TL;DR: Phenotypic traits are now known to stem from the interplay between genetic variables across many if not every level of biology.
1
Using methylation data to improve transcription factor binding prediction
TL;DR: It is found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores, and there are several exceptions to this rule indicating that the role of methylation in TF binding may be cell-type and context specific.