FreePSI: an alignment-free approach to estimating exon-inclusion ratios without a reference transcriptome.

Alternative splicing plays an important role in many cellular processes of eukaryotic organisms. The exon-inclusion ratio, also known as percent spliced in, is often regarded as one of the most effective measures of alternative splicing events. The existing methods for estimating exon-inclusion ratios at the genome scale all require the existence of a reference transcriptome. In this paper, we propose an alignment-free method, FreePSI, to perform genome-wide estimation of exon-inclusion ratios from RNA-Seq data without relying on the guidance of a reference transcriptome. It uses a novel probabilistic generative model based on k-mer profiles to quantify the exon-inclusion ratios at the genome scale and an efficient expectation-maximization algorithm based on a divide-and-conquer strategy and ultrafast conjugate gradient projection descent method to solve the model. We compare FreePSI with the existing methods on simulated and real RNA-seq data in terms of both accuracy and efficiency and show that it is able to achieve very good performance even though a reference transcriptome is not provided. Our results suggest that FreePSI may have important applications in performing alternative splicing analysis for organisms that do not have quality reference transcriptomes. FreePSI is implemented in C++ and freely available to the public on GitHub

Doctor of Philosophy

dissertationMagnetic resonance guided high intensity focused ultrasound (MRgHIFU) is a promising minimal invasive thermal therapy for the treatment of breast cancer. This study develops techniques for determining the tissue parameters - tissue types and perfusion rate - that influence the local temperature during HIFU thermotherapy procedures. For optimal treatment planning for each individual patient, a 3D volumetric breast tissue segmentation scheme based on the hierarchical support vector machine (SVM) algorithm was developed to automatically segment breast tissues into fat, fibroglandular tissue, skin and lesions. Compared with fuzzy c-mean and conventional SVM algorithm, the presented technique offers tissue classification performance with the highest accuracy. The consistency of the segmentation results along both the sagittal and axial orientations indicates the stability of the proposed segmentation routine. Accurate knowledge of the internal anatomy of the breast can be utilized in the ultrasound beam simulation for the treatment planning of MRgHIFU therapy. Completely noninvasive MRI techniques were developed for visualizing blood vessels and determining perfusion rate to assist in the MRgHIFU therapy. Two-point Dixon fat-water separation was achieved using a 3D dual-echo SSFP sequence for breast vessel imaging. The performances of the fat-water separation with various readout gradient designs were evaluated on a water-oil phantom, ex vivo pork sample and in vivo breast imaging. Results suggested that using a dual-echo SSFP readout with bipolar readout gradient polarity, blood vasculature could be successfully visualized through the thin-slab maximum intensity projection SSFP water-only images. For determining the perfusion rate, we presented a novel imaging pulse sequence design consisting of a single arterial spin labeling (ASL) magnetization preparation followed by Look-Locker-like image readouts. This flow quantification technique was examined through simulation, in vitro and in vivo experiments. Experimental results from a hemodialyzer when fitted with a Bloch-equation-based model provide flow measurements that are consistent with ground truth velocities. With these tissue properties, it is possible to compensate for the dissipative effects of the flowing blood and ultimately improve the efficacy of the MRgHIFU therapies. Complete noninvasiveness of these techniques allows multiple measurements before, during and after the treatment, without the limitation of washout of the injected contrast agent

LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO

One fundamental problem in computational stereo reconstruction is correspondence. Correspondence is the method of detecting the real world object reflections in two camera views. This research focuses on correspondence, proposing an algorithm to improve such detection for low quality cameras (webcams) while trying to achieve real-time image processing. Correspondence plays an important role in computational stereo reconstruction and it has a vast spectrum of applicability. This method is useful in other areas such as structure from motion reconstruction, object detection, tracking in robot vision and virtual reality. Due to its importance, a correspondence method needs to be accurate enough to meet the requirement of such fields but it should be less costly and easy to use and configure, to be accessible by everyone. By comparing current local correspondence method and discussing their weakness and strength, this research tries to enhance an algorithm to improve previous works to achieve fast detection, less costly and acceptable accuracy to meet the requirement of reconstruction. In this research, the correspondence is divided into four stages. Two stages of preprocessing which are noise reduction and edge detection have been compared with respect to different methods available. In the next stage, the feature detection process is introduced and discussed focusing on possible solutions to reduce errors created by system or problem occurring in the scene such as occlusion. Lastly, in the final stage it elaborates different methods of displaying reconstructed result. Different sets of data are processed based on the steps involved in correspondence and the results are discussed and compared in detail. The finding shows how this system can achieve high speed and acceptable outcome despite of poor quality input. As a conclusion, some possible improvements are proposed based on ultimate outcome

Towards Informed Exploration for Deep Reinforcement Learning

In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact