| 1. |
- Beck, Dominik, et al.
(författare)
-
Integrative analysis of next generation sequencing for small non-coding RNAs and transcriptional regulation in Myelodysplastic Syndromes
- 2011
-
Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 4:19, s. 1-16
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundMyelodysplastic Syndromes (MDSS) are pre-leukemic disorders with increasing incident rates worldwide, but very limited treatment options. Little is known about small regulatory RNAs and how they contribute to pathogenesis, progression and transcriptome changes in MDS.MethodsPatients' primary marrow cells were screened for short RNAs (RNA-seq) using next generation sequencing. Exon arrays from the same cells were used to profile gene expression and additional measures on 98 patients obtained. Integrative bioinformatics algorithms were proposed, and pathway and ontology analysis performed.ResultsIn low-grade MDS, observations implied extensive post-transcriptional regulation via microRNAs (miRNA) and the recently discovered Piwi interacting RNAs (piRNA). Large expression differences were found for MDS-associated and novel miRNAs, including 48 sequences matching to miRNA star (miRNA*) motifs. The detected species were predicted to regulate disease stage specific molecular functions and pathways, including apoptosis and response to DNA damage. In high-grade MDS, results suggested extensive post-translation editing via transfer RNAs (tRNAs), providing a potential link for reduced apoptosis, a hallmark for this disease stage. Bioinformatics analysis confirmed important regulatory roles for MDS linked miRNAs and TFs, and strengthened the biological significance of miRNA*. The "RNA polymerase II promoters" were identified as the tightest controlled biological function. We suggest their control by a miRNA dominated feedback loop, which might be linked to the dramatically different miRNA amounts seen between low and high-grade MDS.DiscussionThe presented results provide novel findings that build a basis of further investigations of diagnostic biomarkers, targeted therapies and studies on MDS pathogenesis.
|
|
| 2. |
- Ng, Theam Foo, et al.
(författare)
-
Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes
- 2010
-
Konferensbidrag (refereegranskat)abstract
- With the fast development of multi‐dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering‐based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype‐image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ‐HMM) and a well‐known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG‐HMM) will be carried out. The experimental results show that the performances of both FDVQ‐HMM and LBG‐HMM are almost similar. Finally, we have justified the competitiveness of FDVQ‐HMM in classification of cellular phenotype image database by using hypotheses t‐test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome‐wide screening image data.
|
|
| 3. |
- Pham, Tuan D, et al.
(författare)
-
Analysis of Major Adverse Cardiac Events with Entropy-Based Complexity
- 2010
-
Ingår i: Information Technologies in Biomedicine. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642131042 - 9783642131059 ; , s. 261-272
-
Bokkapitel (refereegranskat)abstract
- Major adverse cardiac events (MACE) are referred to as unsuspected heart attacks that include death, myocardial infarction and target lesion revascularization. Feature extraction and classification methods for such cardiac events are useful tools that can be applied for biomarker discovery to allow preventive treatment and healthy-life maintenance. In this study we present an entropy-based analysis of the complexity of MACE-related mass spectrometry signals, and an effective model for classifying MACE and control complexity-based features. In particular, the geostatistical entropy is analytically rigorous and can provide better information about the predictability of this type of MACE data than other entropy-based methods for complexity analysis of biosignals. Information on the complexity of this type of time-series data can expand our knowledge about the dynamical behavior of a cardiac model and be useful as a novel feature for early prediction.
|
|
| 4. |
- Xiao, Yi, et al.
(författare)
-
Correlation-based cluster-space transform for major adverse cardiac event prediction
- 2010
-
Ingår i: IEEE International Conference on Systems Man and Cybernetics (SMC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781424465880 - 9781424465866 ; , s. 2003-2007
-
Konferensbidrag (refereegranskat)abstract
- This paper investigates the affect of variation of patterns in protein profiles to the identification of disease-specific biomarkers. A correlation-based cluster-space transform is applied to mass spectral data for predicting major adverse cardiac events (MACE). Training and testing data are transformed into cluster spaces by correlation distance based clustering, respectively. Data in the testing cluster that falls into a pair of training clusters is classified by a supervised classifier. Experiment results have shown that proteomic spectra of MACE which vary with certain patterns could be separated by the correlation-based clustering. The cluster-space transform allows better classification accuracy than single-clustered class method for separating disease and healthy samples.
|
|
| 5. |
- Xiao, Yi, et al.
(författare)
-
Symmetry-based presentation for stem-cell image segmentation
- 2011
-
Konferensbidrag (refereegranskat)abstract
- Cancer stem cells have been isolated from many tumors, including breast, brain, colon, head and neck, lung, pancreas, and prostate tumors. Advances in stem cell biology and animal models help better characterization of cancer stem cells, including the cells of origin, molecular and cellular properties, functions in cancer initiation and development, treatment response, and drug resistance. An important and challenging task in image analysis of stem cells is the image segmentation. A difficulty is to segment aggregated cells that are deformed and occluded. Watershed transform and multiscale morphological operation are the common methods for this purpose, as they are robust against arbitrary shaping and the occlusion of cells. Notwithstanding their high robustness, the two methods are still limited in their applications in the cases with cells suffering perturbations and deformation during cell growth. In this paper, we propose a novel symmetry axis transformation for stem-cell image segmentation. Our algorithm was validated by its comparison with both watershed transform and multiscale morphological operation. Improved segmentation performance in terms of precision (up to 2.2% comparing to watershed; and up to 0.6% comparing to multiscale morphological operation) was achieved using 5197 cell images in which 291 cells are three mutually touching.
|
|
| 6. |
- Xu, Jin Wei, et al.
(författare)
-
A double thresholding method for cancer stem cell detection
- 2011
-
Konferensbidrag (refereegranskat)abstract
- Image analysis of cancer cells is important for cancer diagnosis and therapy, because it recognized as the most efficient and effective way to observe its proliferation. For the purpose of adaptive and accurate cancer cell image segmentation, a double threshold segmentation method is proposed in this paper. Based on a single gray-value histogram of the RGB color space, a double threshold, the key parameters of threshold segmentation can be fixed by a fitted-curve of the RGB component histogram. As reasonable thresholds confirmed, binary segmentation dependent on two thresholds, will be put into practice and result in binary image. With the post-processing of mathematical morphology and division of whole image, the better segmentation result can be finally achieved. By the comparison with other advanced segmentation methods such as level set and active contour, the proposed double thresholding has been found as the simplest strategy with shortest processing time as well as highest accuracy. The proposed method can be effectively used in the detection and recognition of cancer stem cells in images.
|
|
