Machine Learning Approaches for Biological and Physiological Data

学位記番号:工博甲50

New Descriptor for Glomerulus Detection in Kidney Microscopy Image

Glomerulus detection is a key step in histopathological evaluation of microscopy images of kidneys. However, the task of automatic detection of glomeruli poses challenges due to the disparity in sizes and shapes of glomeruli in renal sections. Moreover, extensive variations of their intensities due to heterogeneity in immunohistochemistry staining are also encountered. Despite being widely recognized as a powerful descriptor for general object detection, the rectangular histogram of oriented gradients (Rectangular HOG) suffers from many false positives due to the aforementioned difficulties in the context of glomerulus detection. A new descriptor referred to as Segmental HOG is developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images to acquire robustness to deformations of glomeruli. Moreover, the novel segmentation technique employed herewith generates high quality segmentation outputs and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real world image data reveal that Segmental HOG achieves significant improvements in detection performance compared to Rectangular HOG. The proposed descriptor and method for glomeruli detection present promising results and is expected to be useful in pathological evaluation

Functional Insight into and Refinement of the Genomic Boundaries of the JARID2-Neurodevelopmental Disorder Episignature

DNA methylation; Episignature; Intellectual disabilityMetilació de l'ADN; Episignatura; Discapacitat intel·lectualMetilación del ADN; Epifirma; Discapacidad intelectualJARID2 (Jumonji, AT-rich interactive domain 2) haploinsufficiency is associated with a clinically distinct neurodevelopmental syndrome. It is characterized by intellectual disability, developmental delay, autistic features, behavior abnormalities, cognitive impairment, hypotonia, and dysmorphic features. JARID2 acts as a transcriptional repressor protein that is involved in the regulation of histone methyltransferase complexes. JARID2 plays a role in the epigenetic machinery, and the associated syndrome has an identified DNA methylation episignature derived from sequence variants and intragenic deletions involving JARID2. For this study, our aim was to determine whether patients with larger deletions spanning beyond JARID2 present a similar DNA methylation episignature and to define the critical region involved in aberrant DNA methylation in 6p22–p24 microdeletions. We examined the DNA methylation profiles of peripheral blood from 56 control subjects, 13 patients with (likely) pathogenic JARID2 variants or patients carrying copy number variants, and three patients with JARID2 VUS variants. The analysis showed a distinct and strong differentiation between patients with (likely) pathogenic variants, both sequence and copy number, and controls. Using the identified episignature, we developed a binary model to classify patients with the JARID2-neurodevelopmental syndrome. DNA methylation analysis indicated that JARID2 is the driver gene for aberrant DNA methylation observed in 6p22–p24 microdeletions. In addition, we performed analysis of functional correlation of the JARID2 genome-wide methylation profile with the DNA methylation profiles of 56 additional neurodevelopmental disorders. To conclude, we refined the critical region for the presence of the JARID2 episignature in 6p22–p24 microdeletions and provide insight into the functional changes in the epigenome observed when regulation by JARID2 is lost.Funding for this study is provided in part by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-188)

Identification of the DNA methylation signature of Mowat-Wilson syndrome

Mowat-Wilson syndrome (MOWS) is a rare congenital disease caused by haploinsufficiency of ZEB2, encoding a transcription factor required for neurodevelopment. MOWS is characterized by intellectual disability, epilepsy, typical facial phenotype and other anomalies, such as short stature, Hirschsprung disease, brain and heart defects. Despite some recognizable features, MOWS rarity and phenotypic variability may complicate its diagnosis, particularly in the neonatal period. In order to define a novel diagnostic biomarker for MOWS, we determined the genome-wide DNA methylation profile of DNA samples from 29 individuals with confirmed clinical and molecular diagnosis. Through multidimensional scaling and hierarchical clustering analysis, we identified and validated a DNA methylation signature involving 296 differentially methylated probes as part of the broader MOWS DNA methylation profile. The prevalence of hypomethylated CpG sites agrees with the main role of ZEB2 as a transcriptional repressor, while differential methylation within the ZEB2 locus supports the previously proposed autoregulation ability. Correlation studies compared the MOWS cohort with 56 previously described DNA methylation profiles of other neurodevelopmental disorders, further validating the specificity of this biomarker. In conclusion, MOWS DNA methylation signature is highly sensitive and reproducible, providing a useful tool to facilitate diagnosis

Identifying statistically significant combinatorial markers for survival analysis

Abstract Background Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance. Methods We address the issue of testing marker combinations that affect survival by adapting the recently developed Limitless Arity Multiple-testing Procedure (LAMP), a p-value correction technique for statistical tests for combination of markers. LAMP cannot handle survival data statistics, and hence we extended LAMP for the log-rank test, making it more appropriate for clinical data, with newly introduced theoretical lower bound of the p-value. Results We applied the proposed method to gene combination detection for cancer and obtained gene interactions with statistically significant log-rank p-values. Gene combinations with orders of up to 32 genes were detected by our algorithm, and effects of some genes in these combinations are also supported by existing literature. Conclusion The novel approach for detecting prognostic markers presented here can identify statistically significant markers with no limitations on the order of interaction. Furthermore, it can be applied to different types of genomic data, provided that binarization is possible