TY  - JOUR
A1  - Perscheid, Cindy
T1  - Integrative biomarker detection on high-dimensional gene expression data sets
BT  - a survey on prior knowledge approaches
JF  - Briefings in bioinformatics
N2  - Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
KW  - gene selection
KW  - external knowledge bases
KW  - biomarker detection
KW  - gene
KW  - expression
KW  - prior knowledge
Y1  - 2021
U6  - https://doi.org/10.1093/bib/bbaa151
SN  - 1467-5463
SN  - 1477-4054
VL  - 22
IS  - 3
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
TY  - CHAP
A1  - Haase, Jennifer
A1  - Matthiesen, Julia
A1  - Schüffler, Arnulf
A1  - Kluge, Annette
T1  - Retentivity beats prior knowledge as predictor for the acquisition and adaptation of new production processes
T2  - Proceedings of the 53rd Hawaii International Conference on System Sciences
N2  - In the time of digitalization the demand for organizational change is rising and demands ways to cope with fundamental changes on the organizational as well as individual level. As a basis, learning and forgetting mechanisms need to be understood in order to guide a change process efficiently and successfully. Our research aims to get a better understanding of individual differences and mechanisms in the change context by performing an experiment where individuals learn and later re-learn a complex production process using a simulation setting. The individual’s performance, as well as retentivity and prior knowledge is assessed. Our results show that higher retentivity goes along with better learning and forgetting performances. Prior knowledge did not reveal such relation to the learning and forgetting performances. The influence of age and gender is discussed in detail.
KW  - Innovation in Organizations: Learning
KW  - learning
KW  - Unlearning
KW  - Intentional Forgetting
KW  - experiment
KW  - forgetting
KW  - prior knowledge
KW  - production process
KW  - retentivity
Y1  - 2020
U6  - https://doi.org/10125/64331
VL  - 53
SP  - 4797
EP  - 4805
PB  - Western Periodicals Co.
CY  - North Hollywood, Calif.
ER  -