58 research outputs found
HeatMapper Expansion
No full textExpansion of an existing visualization tool for genomic data.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc
Predict Radiotherapy Plan Quality
No full textA person with cancer has several treatment options. One of which is radiotherapy. Radiotherapy is treatment of cancer with radiation. To minimize the damage to healthy tissue, radiation is applied from several directions into the body. When treating cancer with radiotherapy, the organs nearby the tumor are at high risk of getting damaged. In the treatment plan the dose to the organs at risk has to be balanced with the dose given to the target. These calculations are nowadays done by medical personnel. Although a lot of treatments succeed, without much damage to healthy tissue, a lot of treatments do serious damage to the organs at risk. Can treatment plans be optimized in terms of organ sparing? To reach optimization, several methods have been executed in order to create groups within a patient set. 115 patients of prostate cancer have been analyzed using Principal Component Analysis and Agglomerative Clustering. The data consist of Overlap Volume Histogram values of the bladder and rectum in a CSV file. Each CSV file contains 201 values. These CSVs are used as an input for both methods. This led to several figures as results. The principal component analysis showed that 80% of the data is covered by the first principal component and 92% by the first and second. Also, a scatterplot has been made, which shows the transformed data. This scatterplot shows no subgroups can be identified with the bladder and rectum data of the patient. The Agglomerative Clustering method results in six plots. A variation in linkages and connectivity has been used, but all six led to no clear distinction within the data. These results led to the conclusion that no subgroups are distinguishable based only on OVH data and no prediction can be made that optimizes radiotherapy plans based solely on OVH data of patients.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
2D Representation of Transcriptomes by t-SNE Exposes Relatedness between Human Tissues
No full textThe GTEx Consortium reported that hierarchical clustering of RNA profiles from 25 unique tissue types among 1641 individuals accurately distinguished the tissue types, but a multidimensional scaling failed to generate a 2D projection of the data that separates tissue-subtypes. In this study we show that a projection by t-Distributed Stochastic Neighbor Embedding is in line with the cluster analysis which allows a more detailed examination and visualization of human tissue relationships.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Computational Biology in Acute Myeloid Leukemia with CEBPA Abnormalities
No full text__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
An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in Acute Myeloid Leukemia
No full textBackground The wingless-Int (WNT) pathway has an essential role in cell regulation of hematopoietic stem cells (HSC). For Acute Myeloid Leukemia (AML), the malignant counterpart of HSC, currently only a selective number of genes of the WNT pathway are analyzed by using either gene expression or DNA-methylation profiles for the identification of prognostic markers and potential candidate targets for drug therapy. It is known that mRNA expression is controlled by DNA-methylation and that specific patterns can infer the ability to differentiate biological differences, thus a combined analysis using all WNT annotated genes could provide more insight in the WNT signaling. Approach We created a computational approach that integrates gene expression and DNA promoter methylation profiles. The approach represents the continuous gene expression and promoter methylation profiles with nine discrete mutually exclusive scenarios. The scenario representation allows for a refinement of patient groups by a more powerful statistical analysis, and the construction of a co-expression network. We focused on 268 WNT annotated signaling genes that are derived from the molecular signature database. Results Using the scenarios we identified seven prognostic markers for overall survival and event-free survival. Three genes are novel prognostic markers; two with favorable outcome (PSMD2, PPARD) and one with unfavorable outcome (XPNPEP). The remaining four genes (LEF1, SFRP2, RUNX1, and AXIN2) were previously identified but we could refine the patient groups. Three AML risk groups were further analyzed and the co-expression network showed that only the good risk group harbors frequent promoter hypermethylation and significantly correlated interactions with proteasome family members. Conclusion Our results provide novel insights in WNT signaling in AML, we discovered new and previously identified prognostic markers and a refinement of the patient groups.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Hypergeometric analysis of tiling-array and sequence data: Detection and interpretation of peaks
No full textProbing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Global existence and nonexistence of solutions for a Klein-Gordon equation with exponential type nonlinear term
No full textIn this paper, the global existence and nonexistence of solutions for a Klein-Gordon equation, appearing in a variety of physical situations, with exponential typesource term and supercritical initial energy (E(0) > d) are investigated in a boundeddomain. In the framework of the potential well, a functional including both of initial datais defined, and by sign invariance of this functional, the global existence of weak solutionsin the case of the high initial energy is proved. Moreover, under some conditions imposedon initial displacement and initial velocity, a finite time blow-up result is provided whichextends a result given in the literature.</p
GTEx (Genotype-Tissue Expression) data normalized
No full textThis is a normalized dataset from the original RNAseq dataset downloaded from Genotype-Tissue Expression (GTEx) project: www.gtexportal.org: RNA-SeQCv1.1.8 gene rpkm Pilot V3 patch1.
The data was used to analyze how tissue samples are related to each other in terms of gene expression data The data can be used to get insights in how gene expression levels behave in in the different human tissues
Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia
No full textBackground Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. Approach Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization. Results We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy. Conclusion We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Evaluation of Tracheostomy Patients in Our Pediatric Intensive Care Unit: A Single-Center Study
No full textObjectives: A tracheostomy is a surgical procedure that can be performed on critically ill patients of all ages in intensive care units as indicated, and its use has been increasing in recent years. The most common indications are prolonged mechanical ventilation and upper airway obstruction. This study aimed to examine the indications for tracheostomy, assess the outcomes of patients who underwent the procedure, and identify the factors affecting these outcomes. Material and method: A retrospective analysis of patients who underwent tracheostomy between 2013 and 2019 at Dicle University Faculty of Medicine Hospital Paediatric Intensive Care Unit (PICU). The patients' age, gender, distribution by age, primary diagnosis at admission to the intensive care unit, indication for tracheostomy, presence of additional disease, type of respiratory support before and after tracheostomy, development of complications (perioperative/postoperative), decannulation status, mortality, and discharge status were recorded. Results: A total of 61 patients were enrolled into the study. The average age of the patients was 81.72 months (SD = 17.5), with the youngest being eight months old and the oldest being 203 months old. Of the 61 patients included in the study, 32 (52%) were male and 29 (48%) were female. The majority of patients (32 patients) were in the preschool age group (25-84 months). The primary diagnosis of 27 patients (44.3%) who underwent tracheostomy was neuromuscular diseases, and the most common indication for tracheostomy was prolonged intubation (24 patients, 39.3%). Concomitant chronic diseases were present in 54 patients (88.5%). Patients received mechanical ventilation support for an average of 47.34 days before tracheostomy. Early tracheostomy (0-21 days after initiation of mechanical ventilation) was performed on 14 patients, and late tracheostomy (21 days and later) was performed on 47 patients. Complications developed in nine patients (14.8%) in the perioperative period and in 19 patients (31.1%) in the postoperative period, while no complications developed in 39 patients (63.9%). Six patients (9.8%) were decannulated. Furthermore, 28 patients (45.9%) died. No tracheostomy-related mortality was documented. Conclusion: Despite most patients being of preschool age, having prolonged intubation prior to tracheostomy, and having accompanying chronic illnesses, tracheostomy remains a frequently used procedure in paediatric intensive care units due to its low complication rates, making it an essential intervention that facilitates discharge from paediatric intensive care.Corresponding author: Emine Senka
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