A5043903426- Neural Networks and Applications
- Face and Expression Recognition
- Cancer Genomics and Diagnostics
- interferon and immune responses
- RNA modifications and cancer
- Cancer-related molecular mechanisms research
- Pancreatic and Hepatic Oncology Research
- Cancer Cells and Metastasis
- Renal cell carcinoma treatment
- Cancer Research and Treatments
- Cancer Immunotherapy and Biomarkers
- RNA regulation and disease
- Genetic factors in colorectal cancer
- Cytokine Signaling Pathways and Interactions
- Lung Cancer Research Studies
- Fault Detection and Control Systems
- Epigenetics and DNA Methylation
- Advanced Memory and Neural Computing
- Evolutionary Algorithms and Applications
- Image Processing and 3D Reconstruction
- Lung Cancer Treatments and Mutations
- Neural Networks Stability and Synchronization
- Machine Learning and Data Classification
- Cancer-related gene regulation
- Wnt/β-catenin signaling in development and cancer
University of Freiburg
2020-2024
Aarhus University Hospital
2019
Amirkabir University of Technology
2013-2015
Universität Ulm
2014-2015
The transcription factor TCF7L2 is indispensable for intestinal tissue homeostasis where it transmits mitogenic Wnt/β-Catenin signals in stem and progenitor cells, from which tumors arise. Yet, belongs to the most frequently mutated genes colorectal cancer (CRC), tumor-suppressive functions of were proposed. This apparent paradox warrants clarify role carcinogenesis. Here, we investigated dependence/independence CRC cells cellular molecular consequences loss-of-function. By genome editing...
Epithelial-mesenchymal transition (EMT) fosters cancer cell invasion and metastasis, the main cause of cancer-related mortality. Growing evidence that SNAIL ZEB transcription factors, typically portrayed as master regulators EMT, may be dispensable for this process, led us to re-investigate its mechanistic underpinnings. For this, we used an unbiased computational approach integrated time-resolved analyses chromatin structure differential gene expression, predict transcriptional...
Abstract Mitochondria react to infection with sub-lethal signals in the apoptosis pathway. Mitochondrial can be inflammatory but mechanisms are only partially understood. We show that activation of caspase-activated DNase (CAD) mediates mitochondrial pro-inflammatory functions and substantially contributes host defense against viral infection. In cells lacking CAD, activity was reduced. Experimental CAD caused transient DNA-damage a pronounced DNA damage response, involving major kinase...
Abstract Micronuclei are DNA-containing structures separate from the nucleus found in cancer cells. recognized by immune sensor axis cGAS/STING, driving metastasis. The mitochondrial apoptosis apparatus can be experimentally triggered to a non-apoptotic level, and this drive appearance of micronuclei through Caspase-activated DNAse (CAD). We tested whether spontaneously appearing cells linked sub-lethal apoptotic signals. Inhibition or CAD reduced number tumor cell lines as well chromosomal...
A cancer of unknown primary (CUP) is a metastatic for which standard diagnostic tests fail to locate the cancer. As treatments are based on type, such cases hard treat and have very poor prognosis. Using molecular data from predict site can make treatment choice easier enable targeted therapy. In this article, we first examine ability type using different types omics data. Methylation lead slightly better prediction than gene expression both these superior classification somatic mutations....
Background: Given the poor prognosis of metastatic pancreatic adenocarcinoma (mPDAC), closer disease monitoring through liquid biopsy, most frequently based on serial measurements cell-free mutated KRAS (KRASmut cfDNA), has become a highly active research focus, aimed at improving patients’ long-term outcomes. However, available data show only limited predictive and prognostic value single-parameter-based methods. We hypothesized that combined longitudinal analysis KRASmut cfDNA novel...
Mapping to Multidimensional Optimal Regions (M2OR) is the enhanced version of (MOR) which a special purposed method for multiclass classification task. Similar MOR, it reduces computational complexity; however, presents better accuracy. Theoretical and experimental results confirm that by using M2OR, minimum complexity multi-classification task approximately equal one inner product in feature space. As multi-classifier, MOR family generalizes upper bound Vapnik-Chervonenkis (V.C.) entropy...
Background: Given the poor prognosis of metastatic pancreatic adenocarcinoma (mPDAC), closer disease monitoring through liquid biopsy, most frequently based on serial measurements cell-free mutated KRAS (KRASmut cfDNA), has become a highly active research focus, aiming to improve patients´ long-term outcome. However, available data show only limited predictive and prognostic value single-parameter methods. We hypothesized that combined longitudinal analysis KRASmut cfDNA novel protein...
Manifold learning algorithms do not extract the structure of datasets in an abstract form or they have high performance for complex data.In this paper, a method Learning Inductive Riemannian Abstract (LIRMA) is presented which patterns determined by solving embedded dynamical system patterns. In order to model corresponding system, true sequence estimated using topology preserving method. LIRMA has advantage being inductive with low complexity. Additionally, it respect quantitative measures.
Classification of data is an important problem which has attracted many researchers to introduce new approaches. In this paper, we propose Mapping Optimal Regions (MOR) as a method for multi-class classification task reduce computational and memory complexities. It requires only one simple mapping from input space optimal regions. The domain estimated using multi objective cost function increase the region size generalization ability error. Finally, centers regions are determined with...
Reducing Computational complexity is a major issue in data mining. Mapping to Multidimensional Optimal Regions (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> OR) special purposed method for multiclass classification task. It reduces computational comparison the other concepts of classifiers. In this paper, accuracy M OR increases using Learning Inductive Riemannian Manifold Abstract from (LIRMA). LIRMA estimates underlying structure...