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.
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.
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.
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.
We study the problem of learning sparse structure changes between two Markov networks P and Q. Rather than fitting two Markov networks separately to two sets of data and figuring out their ...differences, a recent work proposed to learn changes directly via estimating the ratio between two Markov network models. In this paper, we give sufficient conditions for successful change detection with respect to the sample size np, nq, the dimension of data m and the number of changed edges d. When using an unbounded density ratio model, we prove that the true sparse changes can be consistently identified for ${\mathrm{n}}_{\mathrm{p}}=\mathrm{\Omega }({\mathrm{d}}^{2}\mathrm{log}\frac{{\mathrm{m}}^{2}+\mathrm{m}}{2})$ and ${\mathrm{n}}_{\mathrm{q}}=\mathrm{\Omega }\left({\mathrm{n}}_{\mathrm{p}}^{2}\right)$, with an exponentially decaying upper-bound on learning error. Such sample complexity can be improved to $\mathrm{min}({\mathrm{n}}_{\mathrm{p}},{\mathrm{n}}_{\mathrm{q}})=\mathrm{\Omega }({\mathrm{d}}^{2}\mathrm{log}\frac{{\mathrm{m}}^{2}+\mathrm{m}}{2})$ when the boundedness of the density ratio model is assumed. Our theoretical guarantee can be applied to a wide range of discrete/continuous Markov networks.
JARID2 (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.
Although many 3D structures have been solved for proteins to date, functions of some proteins remain unknown. To predict protein functions, comparison of local structures of proteins with pre-defined ...model structures, whose functions have been elucidated, is widely performed. For the comparison, the root mean square deviation (RMSD) has been used as a conventional index. In this work, adaptive deviation was incorporated, along with Bregmann Divergence Regularized Machine, in order to detect analogous local structures with such model structures more effectively than the conventional index.
In recent years, covariance descriptors have received considerable attention as a strong representation of a set of points. In this research, we propose a new metric learning algorithm for covariance ...descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and which runs in O(n3) time. We empirically demonstrate that randomizing the order of half-spaces in the proposed Dykstra-based algorithm significantly accelerates convergence to the optimal solution. Furthermore, we show that the proposed approach yields promising experimental results for pattern recognition tasks.
Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, like in any other discipline, ...determining suitable representation of data has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel between vector sets, the mean polynomial kernel, motivated by recent studies where data are approximated by linear subspaces, in particular, methods that were formulated on Grassmann manifolds. This kernel takes a more general approach given that it can also support input data that can be modeled as a vector sequence, and not necessarily requiring it to be a linear subspace. We discuss how the kernel can be associated with the Projection kernel, a Grassmann kernel. Experimental results using face image sequences and physiological signal data show that the mean polynomial kernel surpasses existing subspace-based methods on Grassmann manifolds in terms of predictive performance and efficiency.
Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed ...for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.
Mahalanobis Encodings for Visual Categorization Matsuzawa, Tomoki; Relator, Raissa; Takei, Wataru ...
IPSJ Transactions on Computer Vision and Applications,
2015, 2015-00-00, Volume:
7
Journal Article
Peer reviewed
Open access
Nowadays, the design of the representation of images is one of the most crucial factors in the performance of visual categorization. A common pipeline employed in most of recent researches for ...obtaining an image representation consists of two steps: the encoding step and the pooling step. In this paper, we introduce the Mahalanobis metric to the two popular image patch encoding modules, Histogram Encoding and Fisher Encoding, that are used for Bag-of-Visual-Word method and Fisher Vector method, respectively. Moreover, for the proposed Fisher Vector method, a close-form approximation of Fisher Vector can be derived with the same assumption used in the original Fisher Vector, and the codebook is built without resorting to time-consuming EM (Expectation-Maximization) steps. Experimental evaluation of multi-class classification demonstrates the effectiveness of the proposed encoding methods.
The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing ...to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.
A new descriptor referred to as Segmental HOG was 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 in order to acquire robustness for the detection of the 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 revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.
The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.
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