Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

ON LEARNING COMPOSABLE AND DECOMPOSABLE GENERATIVE MODELS USING PRIOR INFORMATION

Within the field of machining learning, supervised learning has gained much success recently, and the research focus moves towards unsupervised learning. A generative model is a powerful way of unsupervised learning that models data distribution. Deep generative models like generative adversarial networks (GANs), can generate high-quality samples for various applications. However, these generative models are not easy to understand. While it is easy to generate samples from these models, the breadth of the samples that can be generated is difficult to ascertain. Further, most existing models are trained from scratch and do not take advantage of the compositional nature of the data. To address these deficiencies, I propose a composition and decomposition framework for generative models. This framework includes three types of components: part generators, composition operation, and decomposition operation. In the framework, a generative model could have multiple part generators that generate different parts of a sample independently. What a part generator should generate is explicitly defined by users. This explicit ”division of responsibility” provides more modularity to the whole system. Similar to software design, this modular modeling makes each module (part generators) more reusable and allows users to build increasingly complex generative models from simpler ones. The composition operation composes the parts from the part generators into a whole sample, whereas the decomposition operation is an inversed operation of composition. On the other hand, given the composed data, components of the framework are not necessarily identifiable. Inspired by other signal decomposition methods, we incorporate prior information to the model to solve this problem. We show that we can identify all of the components by incorporating prior information about one or more of the components. Furthermore, we show both theoretically and experimentally how much prior information is needed to identify the components of the model. Concerning the applications of this framework, we apply the framework to sparse dictionary learning (SDL) and offer our dictionary learning method, MOLDL. With MOLDL, we can easily include prior information about part generators; thus, we learn a generative model that results in a better signal decomposition operation. The experiments show our method decomposes ion mass signals more accurately than other signal decomposition methods. Further, we apply the framework to generative adversarial networks (GANs). Our composition/decomposition GAN learns the foreground part and background part generators that are responsible for different parts of the data. The resulting generators are easier to control and understand. Also, we show both theoretically and experimentally how much prior information is needed to identify different components of the framework. Precisely, we show that we can learn a reasonable part generator given only the composed data and composition operation. Moreover, we show the composable generators has better performance than their non-composable generative counterparts. Lastly, we propose two use cases that show transfer learning is feasible under this framework.Doctor of Philosoph

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance