A Factor Analysis Perspective on Linear Regression in the 'More Predictors than Samples' Case.

Linear regression (LR) is a core model in supervised machine learning performing a regression task. One can fit this model using either an analytic/closed-form formula or an iterative algorithm. Fitting it via the analytic formula becomes a problem when the number of predictors is greater than the number of samples because the closed-form solution contains a matrix inverse that is not defined when having more predictors than samples.

IL-9 is a key component of memory TH cell peanut-specific responses from children with peanut allergy.

Background: Differentiation between patients with peanut allergy (PA) and those with peanut sensitization (PS) who tolerate peanut but have peanut-specific IgE, positive skin prick test responses, or both represents a significant diagnostic difficulty. Previously, gene expression microarrays were successfully used to identify biomarkers and explore immune responses during PA immunotherapy.

Objective: We aimed to characterize peanut-specific responses from patients with PA, subjects with PS, and atopic children without peanut allergy (NA children).

MiRNA recognition with the yasMiR system: the quest for further improvements.

The paper "Using Base Pairing Probabilities for MiRNA Recognition" by Daniel Pasailă, Irina Mohorianu, and Liviu Ciortuz, that has been published in Proceedings of the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2008, IEEE Computer Society, pp. 519-525, has introduced a new SVM for microRNA identification, whose novelty is twofolded: first, many of its features incorporate the base-pairing probabilities provided by McCaskill's algorithm, and second the classification performance is improved using a certain similarity ("profile"-based) measure between the training and test microRNAs and a set of carefully chosen ("pivot") RNA sequences. Comparisons with some of the best existing SVMs for microRNA identification proved that our SVM obtains truly competitive results.