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RNA is one of the most important molecules in living organisms. One of its main functions is to regulate gene expression. This involves binding to and forming a joint structure with a messenger RNA. An RNAs functions is determined by its sequence and the structure it folds into. Accordingly, the prediction of individual as well as joint structures is an important area of research. In this thesis a method for the prediction of RNA-RNA joint structure using their minimum free energy (mfe) structures was developed. It is able to extensively explore the joint structural landscape of two interacting RNAs by taking advantage of the locality of changes in the RNAs structures as well as natural and energetic constraints. The method predicts the mfe joint structure as well as alternative stable joint structures while also computing non-optimal folding pathways from the unbound individual mfe structures to the predicted joint structures. It is shown how an enumeration approach is used which is able to deal with the enormous search space as well as to avoid any cyclic behaviour. The method is evaluated using two standard datasets of known interacting RNAs and shows good results.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.