Random subwindows and extremely randomized trees for image classification in cell biology

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems

Extremely randomized trees

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.Peer reviewe

Segment and combine approach for Biological Sequence Classification

peer reviewedThis paper presents a new algorithm based on the segment and combine paradigm, for automatic classification of biological sequences. It classifies sequences by aggregating the information about their subsequences predicted by a classifier derived by machine learning from a random sample of training subsequences. This generic approach is combined with decision tree based ensemble methods, scalable both with respect to sample size and vocabulary size. The method is applied to three families of problems: DNA sequence recognition, splice junction detection, and gene regulon prediction. With respect to standard approaches based on n-grams, it appears competitive in terms of accuracy, flexibility, and scalability. The paper also highlights the possibility to exploit the resulting models to identify interpretable patterns specific of a given class of biological sequences