Evaluation Measures for Models Assessment over Imbalanced Data Sets

Imbalanced data learning is one of the challenging problems in data mining; among this matter, founding the right model assessment measures is almost a primary research issue. Skewed class distribution causes a misreading of common evaluation measures as well it lead a biased classification. This article presents a set of alternative for imbalanced data learning assessment, using a combined measures (G-means, likelihood ratios, Discriminant power, F-Measure Balanced Accuracy, Youden index, Matthews correlation coefficient), and graphical performance assessment (ROC curve, Area Under Curve, Partial AUC, Weighted AUC, Cumulative Gains Curve and lift chart, Area Under Lift AUL), that aim to provide a more credible evaluation. We analyze the applications of these measures in churn prediction models evaluation, a well known application of imbalanced data Keywords: imbalanced data, Model assessment, accuracy , G-means, likelihood ratios, F-Measure, Youden index, Matthews correlation coefficient, ROC, AUC, P-AUC,W-AUC, Lift, AU

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing

Peer reviewe

Kawarabi: Administrative Structuring of a Multicenter Research Collaborative to Study Kawasaki Disease in the Arab Countries

Kawasaki disease (KD), the leading cause of acquired heart disease in children in developed countries, merits conducting detailed studies in Arab countries. We introduce Kawarabi, as a multicenter research collaborative effort dedicated to improving diagnosis, care, and outcome of children and adults with KD in the Arab world. During the COVID-19 pandemic, there emerged a new multisystem inflammatory syndrome in children; a disease similar to KD. This highlighted the challenges that Arab physicians face in diagnosing and managing children with KD and KD-like illnesses. Kawarabi brings together experts in North America and Arab nations to study this family of diseases in a not-for-profit, voluntary scientific collaborative setting. Bylaws addressing the vision, objectives, structure, and governance of Kawarabi were established, and vetted by the 45 organizing members in 2021. An initial scientific publication showed evidence of a decreased level of awareness of the disease in the general population, as well as the lack of access to resources available for physicians caring for children with KD in Arab countries. Kawarabi has since held several educational webinars and an inaugural yearly meeting. The groundwork for future initiatives targeted at increasing awareness and understanding of the management and the long-term outcomes of children with KD in the region was established. Data on KD in the Arab world are lacking. Kawarabi is a multicenter research collaborative organization that has the unique resources, diversified ethnic makeup, and energy, to accomplish significant advances in our understanding and management of KD and its variants