Robust Linear Regression Analysis - A Greedy Approach

The task of robust linear estimation in the presence of outliers is of particular importance in signal processing, statistics and machine learning. Although the problem has been stated a few decades ago and solved using classical (considered nowadays) methods, recently it has attracted more attention in the context of sparse modeling, where several notable contributions have been made. In the present manuscript, a new approach is considered in the framework of greedy algorithms. The noise is split into two components: a) the inlier bounded noise and b) the outliers, which are explicitly modeled by employing sparsity arguments. Based on this scheme, a novel efficient algorithm (Greedy Algorithm for Robust Denoising - GARD), is derived. GARD alternates between a least square optimization criterion and an Orthogonal Matching Pursuit (OMP) selection step that identifies the outliers. The case where only outliers are present has been studied separately, where bounds on the \textit{Restricted Isometry Property} guarantee that the recovery of the signal via GARD is exact. Moreover, theoretical results concerning convergence as well as the derivation of error bounds in the case of additional bounded noise are discussed. Finally, we provide extensive simulations, which demonstrate the comparative advantages of the new technique

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Reciprocal changes of serum adispin and visfatin levels in patients with type 2 diabetes after an overnight fast

Objective: In order to elucidate the interrelationship of adipokines in glucose hemiostasis, we determined the concentration of visfatin and adipsin in blood samples in patients with type 2 diabetes and age-matched controls after an overnight fast. Subjects and methods: We enrolled 37 patients with known type 2 diabetes -21 males and 16 females, aged 62.95 +/- 15.72 years and 43 controls- 28 males and 15 females, aged 60.79 +/- 12.67 years. Blood samples were collected after an overnight fast and routine biochemical parameters such as glucose, cholesterol, HDL, LDL, triglycerides along with Hb1Ac, insulin and c-peptide, in addition to circulating visfatin and adipsin were determined in all samples. Data were considered significant at a level of p < 0.05. Results: In patients with type 2 diabetes, circulating adipsin levels were decreased and inversely related with glucose levels while circulating visfatin was increased significantly in the fasting state. Conclusion: These results implicate the adipokines adipsin and visfatin as possible participants in the pathogenesis of type 2 diabetes

Reciprocal changes of serum adispin and visfatin levels in patients with type 2 diabetes after an overnight fast

ABSTRACT Objective In order to elucidate the interrelationship of adipokines in glucose hemiostasis, we determined the concentration of visfatin and adipsin in blood samples in patients with type 2 diabetes and age-matched controls after an overnight fast. Subjects and methods We enrolled 37 patients with known type 2 diabetes -21 males and 16 females, aged 62.95 ± 15.72 years and 43 controls- 28 males and 15 females, aged 60.79 ± 12.67 years. Blood samples were collected after an overnight fast and routine biochemical parameters such as glucose, cholesterol, HDL, LDL, triglycerides along with Hb1Ac, insulin and c-peptide, in addition to circulating visfatin and adipsin were determined in all samples. Data were considered significant at a level of p < 0.05. Results In patients with type 2 diabetes, circulating adipsin levels were decreased and inversely related with glucose levels while circulating visfatin was increased significantly in the fasting state. Conclusion These results implicate the adipokines adipsin and visfatin as possible participants in the pathogenesis of type 2 diabetes

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press