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Full-Text Articles in Physical Sciences and Mathematics
Random Forests Based Rule Learning And Feature Elimination, Sheng Liu
Random Forests Based Rule Learning And Feature Elimination, Sheng Liu
Electronic Theses and Dissertations
Much research combines data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. We propose an efficient approach, combining rule extraction and feature elimination, based on 1-norm regularized random forests. This approach simultaneously extracts a small number of rules generated by random forests and selects important features. To evaluate this approach, we have applied it to …
Computational Methods For Comparative Non-Coding Rna Analysis: From Structural Motif Identification To Genome-Wide Functional Classification, Cuncong Zhong
Electronic Theses and Dissertations
Recent advances in biological research point out that many ribonucleic acids (RNAs) are transcribed from the genome to perform a variety of cellular functions, rather than merely acting as information carriers for protein synthesis. These RNAs are usually referred to as the non-coding RNAs (ncRNAs). The versatile regulation mechanisms and functionalities of the ncRNAs contribute to the amazing complexity of the biological system. The ncRNAs perform their biological functions by folding into specific structures. In this case, the comparative study of the ncRNA structures is key to the inference of their molecular and cellular functions. We are especially interested in …
New Computational Approaches For Multiple Rna Alignment And Rna Search, Daniel Deblasio
New Computational Approaches For Multiple Rna Alignment And Rna Search, Daniel Deblasio
Electronic Theses and Dissertations
In this thesis we explore the the theory and history behind RNA alignment. Normal sequence alignments as studied by computer scientists can be completed in O(n2) time in the naive case. The process involves taking two input sequences and finding the list of edits that can transform one sequence into the other. This process is applied to biology in many forms, such as the creation of multiple alignments and the search of genomic sequences. When you take into account the RNA sequence structure the problem becomes even harder. Multiple RNA structure alignment is particularly challenging because covarying mutations make sequence …