Inaugural Charles River World Congress on Animal Models in Drug Discovery and Development

https://deepblue.lib.umich.edu/bitstream/2027.42/138112/1/12967_2017_Article_1274.pd

Generating Research Questions from Digital Trace Data: A Machine Learning Method for Discovering Patterns in a Dynamic Environment

Digital trace data derived from organizations’ information systems represent a wealth of possibilities in analyzing decision-making processes and organizational performance. While data-mining methods have advanced considerably over recent years, organizational process research has rarely analyzed this type of trace data with the objective of better understanding organizations’ decision-making processes. However, accurately tracking decision-making actions via digital trace data can produce numerous applications that represent new and unexplored opportunities for IS research. The paper presents a novel method developed to combine quantitative process mining approaches with a variance perspective. Its viability is demonstrated by looking at teams’ decision patterns from a dynamic business-simulation game. This exploratory data-driven method represents a promising starting point for translating complex raw process data into interesting research questions connected with dynamic decision-making environments.Peer reviewe

Generating Research Questions from Digital Trace Data: A Machine-Learning Method for Discovering Patterns in a Dynamic Environment

Digital trace data derived from organizations’ information systems represent a wealth of possibilities in analyzing decision-making processes and organizational performance. While data-mining methods have advanced considerably over recent years, organizational process research has rarely analyzed this type of trace data with the objective of better understanding organizations’ decision-making processes. However, accurately tracking decision-making actions via digital trace data can produce numerous applications that represent new and unexplored opportunities for IS research. The paper presents a novel method developed to combine quantitative process mining approaches with a variance perspective. Its viability is demonstrated by looking at teams’ decision patterns from a dynamic business-simulation game. This exploratory data-driven method represents a promising starting point for translating complex raw process data into interesting research questions connected with dynamic decision-making environments

Detailed genetic and functional analysis of the hDMDdel52/mdx mouse model.

Duchenne muscular dystrophy (DMD) is a severe, progressive neuromuscular disorder caused by reading frame disrupting mutations in the DMD gene leading to absence of functional dystrophin. Antisense oligonucleotide (AON)-mediated exon skipping is a therapeutic approach aimed at restoring the reading frame at the pre-mRNA level, allowing the production of internally truncated partly functional dystrophin proteins. AONs work in a sequence specific manner, which warrants generating humanized mouse models for preclinical tests. To address this, we previously generated the hDMDdel52/mdx mouse model using transcription activator like effector nuclease (TALEN) technology. This model contains mutated murine and human DMD genes, and therefore lacks mouse and human dystrophin resulting in a dystrophic phenotype. It allows preclinical evaluation of AONs inducing the skipping of human DMD exons 51 and 53 and resulting in restoration of dystrophin synthesis. Here, we have further characterized this model genetically and functionally. We discovered that the hDMD and hDMDdel52 transgene is present twice per locus, in a tail-to-tail-orientation. Long-read sequencing revealed a partial deletion of exon 52 (first 25 bp), and a 2.3 kb inversion in intron 51 in both copies. These new findings on the genomic make-up of the hDMD and hDMDdel52 transgene do not affect exon 51 and/or 53 skipping, but do underline the need for extensive genetic analysis of mice generated with genome editing techniques to elucidate additional genetic changes that might have occurred. The hDMDdel52/mdx mice were also evaluated functionally using kinematic gait analysis. This revealed a clear and highly significant difference in overall gait between hDMDdel52/mdx mice and C57BL6/J controls. The motor deficit detected in the model confirms its suitability for preclinical testing of exon skipping AONs for human DMD at both the functional and molecular level