Antti Airola

TL;DR: In this paper, the effects of four factors that may lead to dramatic differences in the prediction results are investigated: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither).

TL;DR: A detailed evaluation of the effects of training and testing on different resources is performed, providing insight into the challenges involved in applying a system beyond the data it was trained on, and several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid are identified.

TL;DR: This first comparative evaluation of the diverse PPI corpora is presented, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties.

TL;DR: A system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction, which defines a wide array of features and makes extensive use of dependency parse graphs.

TL;DR: It is argued here that many medical applications of machine learning models in genetic disease risk prediction rely essentially on two factors: effective model regularization and rigorous model validation.