Search results for "LYN"

The Norm-P Estimation of Location, Scale and Simple Linear Regression Parameters

1989

A new formulation of the exponential power distributions is used as general error model to describe long-tailed and short -tailed distributed errors. The proposed estimators of the location, scale and structure parameters of this general model and of the simple linear regression parameters when the response variable is affected by errors coming from the previous model should be used instead of robust estimators and against the practice of rejecting outlying observations. Two Monte Carlo simulations prove the good properties of these norm-p estimators.

Involvement of large rearrangements in MSH6 and PMS2 genes in southern Italian patients with lynch syndrome

2018

Background and aim of the work: The Lynch Syndrome (LS) is associated with germline mutations in one of the MisMatch Repair (MMR) genes. Most of germline mutations are point variants, followed by large rearrangements that account to 15-55% of all pathogenic mutations. Many study reporting the frequency of large rearrangements in the MLH1 and MSH2 genes were performed, while, little is known about the contribution of large rearrangements in other MMR genes, as PMS2 and MSH6. Therefore, in this study we investigated the involvment of large rearrangements in MSH6 and PMS2 genes in a well-characterized series of 20 LS southern Italian patients. Methods: These large rearrangements are not usuall…

Abstract LB-382: Identification of predisposing genes for small bowel adenocarcinoma by exome sequencing

2018

Abstract Small bowel adenocarcinoma (SBA) is a rare but aggressive cancer type with limited treatment options. Known predisposing factors include Crohn's disease, celiac disease, and hereditary syndromes such as familial adenomatous polyposis (FAP), Lynch syndrome, and Peutz-Jeghers syndrome. Here, our aim was to further characterize genetic susceptibility to SBA in a large population-based cohort and simultaneously demonstrate the ability to utilize tumor-only data to cost-effectively but reliably call germline variants. Information on all SBAs diagnosed in Finland between the years 2003-2011 were collected utilizing the Finnish Cancer Registry that maintains a nation-wide database on all …

2003

Two species of small-medium size felids, Felis aff. silvestris and Caracal depereti nov. sp., have been identified from the Pliocene karstic locality of Layna (Soria, Spain). Caracal depereti nov. sp. shows close affinities with Caracal issiodorensis, species that has been traditionally classified in the genus Lynx. This new interpretation implies that there are no evidences of lynxes in the Pliocene of Westem Europe, and probably this consideration can be applicable to other Eurasiatic localities where Lynx issiodorensis has been determined.

The Links-Gould invariants as generalizations of the Alexander polynomial

2016

In this thesis we focus on the connections that exist between two link invariants: first the Alexander-Conway invariant ∆ that was the first polynomial link invariant to be discovered, and one of the most thoroughly studied since alongside with the Jones polynomial, and on the other hand the family of Links-Gould invariants LGn,m that are quantum link invariants derived from super Hopf algebras Uqgl(n|m). We prove a case of the De Wit-Ishii-Links conjecture: in some cases we can recover powers of the Alexander polynomial as evaluations of the Links-Gould invariants. So the LG polynomials are generalizations of the Alexander invariant. Moreover we give evidence that these invariants should s…

Gray code for permutations with a fixed number of cycles

2007

AbstractWe give the first Gray code for the set of n-length permutations with a given number of cycles. In this code, each permutation is transformed into its successor by a product with a cycle of length three, which is optimal. If we represent each permutation by its transposition array then the obtained list still remains a Gray code and this allows us to construct a constant amortized time (CAT) algorithm for generating these codes. Also, Gray code and generating algorithm for n-length permutations with fixed number of left-to-right minima are discussed.

Semisupervised nonlinear feature extraction for image classification

2012

Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…

Kernel-Based Inference of Functions Over Graphs

2018

Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…

Model selection based product kernel learning for regression on graphs

2013

The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…

A structural cluster kernel for learning on graphs

2012

In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in…