364 research outputs found
Computational Biology in Acute Myeloid Leukemia with CEBPA Abnormalities
Get PDF__Abstract__
In the last decade, tiling-array and next-generation sequencing technologies allowed quantitative measurements of different cellular processes, such as mRNA expression, genomic changes including deletions or amplifications, DNA-methylation, chromatin modifications or Protein-DNA-binding interactions. Using these technologies, thousands of features can now be measured simultaneously in a patient cell sample. The use of for instance mRNA expression profiles or DNA-methylation profiles have already provided new insight into the molecular biology of patients with Acute Myeloid Leukemia (AML). AML is a blood cell malignancy, in which primitive myeloid cells have been transformed and accumulate in the bone marrow and blood. Different forms of AML exist with different molecular abnormalities that associate with distinct responses to therapy. Many subgroups with comparable mRNA expression or DNA-methylation patterns were identified. These studies also revealed the existence of novel previously undefined AML subtypes. Among those was a group of patients with a mutation in a gene called CEBPA. CEBPA is a gene that encodes the transcription factor CCAAT Enhancer Binding Protein Alpha (C/EBPα), which controls the expression of genes in myeloid progenitor cells. Mutated CEBPA encodes a dysfunctional C/EBPα-protein, which consequently results in aberrant control of “target genes”. In this thesis we focus particularly on the role of CEBPA. We studied the predictive and prognostic relevance of mutated CEBPA, and analyzed in a genome wide fashion the mRNA expression, DNA-methylation and the protein-DNA-binding levels corresponding to (mutated) CEBPA in AML. For the analysis of protein-DNA-binding, we developed a novel statistical methodology. With this statistical methodology we studied the fundamental role of (mutant) C/EBPα binding and the effect on gene expression levels. We also integrated gene expression with DNA-methylation profiles of hundreds of AML patients and revealed the existence of two previously unidentified AML subtypes
distfit is a python library for probability density fitting.
No full textChange title in plot_summary(title='new new title')If you use this software, please cite it using these metadata
PERBANDINGAN HUBUNGAN SIPIL-MILITER DI INDONESIA PADA MASA ABDURRAHMAN WAHID DENGAN ERDOGAN DI TURKI
Get PDFAbstract: This paper aims to compare civil-military relations in Indonesia during Abdurrahman Wahid's (1999-2001) period with Turkey during Erdogan's (2003-2011) by looking at civilian control over the military. The study looks at the differences and similarities between the two countries and the causes of Erdogan's success and Abdurrahman Wahid's failure to control the military. In this research, the author used a qualitative approach. The results showed that there were similarities and differences in civil-military relations between Gus Dur and Erdogan, as seen from civil control over the military. The similarities could bee seen at the beginning of their reign. Abdurrahman and Erdogan had strong civilian control over the military so that they could reduce the military's role in politics with various policies issued. This strong control is also supported by political conditions, political elites, and society. However, there were differences in civil-military relations at the end of the Gus Dur and Erdogan governments. Civilian control over the military weakened at the end of the Gus Dur’s reign which caused him to fall from his position as the President of the Republic of Indonesia, whereas Erdogan’s civilian control over the military was getting stronger. The failure factor for Abdurrahman to strengthen civilian control over the military was a radical change. In contrast to Erdogan who made changes gradually with the support of politics and society.Keywords: Turkey; Indonesia; Abdurrahman Wahid; Erdogan; Civil-Military Relation
distfit - Probability density fitting
No full textChanged title text of plot with scientific notation.If you use this software, please cite it using these metadata
Learning Bayesian Networks with the bnlearn Python Package.
No full textsklearnis changed to scikit-learn in the setup and requirements (issue #66 )If you use this software, please cite it using these metadata
Python package clustimage is for unsupervised clustering of images.
No full textFix for libpng error #16
Functionality to specify the number of clusters #17If you use this software, please cite it using these metadata
hgboost is a python package for hyperparameter optimization for xgboost, catboost and lightboost for both classification and regression tasks.
No full textdatazets is added for examples
wget removed
update docstringsIf you use this software, please cite it using these metadata
hgboost is a python package for hyperparameter optimization for xgboost, catboost and lightboost for both classification and regression tasks.
No full text<ul>
<li>Added GPU support. More information can be found <a href="https://erdogant.github.io/hgboost/pages/html/Performance.html#gpu-support">here</a></li>
</ul>If you use this software, please cite it using these metadata
Learning Bayesian Networks with the bnlearn Python Package.
No full text<ul>
<li>Fix the issue, sorting the continuous_columns in the correct order when discretizing continuous data @ankh1999</li>
</ul>If you use this software, please cite it using these metadata
- …
