Structural Alignment of RNAs Using Profile-csHMMs and Its Application to RNA Homology Search: Overview and New Results

Systematic research on noncoding RNAs (ncRNAs) has revealed that many ncRNAs are actively involved in various biological networks. Therefore, in order to fully understand the mechanisms of these networks, it is crucial to understand the roles of ncRNAs. Unfortunately, the annotation of ncRNA genes that give rise to functional RNA molecules has begun only recently, and it is far from being complete. Considering the huge amount of genome sequence data, we need efficient computational methods for finding ncRNA genes. One effective way of finding ncRNA genes is to look for regions that are similar to known ncRNA genes. As many ncRNAs have well-conserved secondary structures, we need statistical models that can represent such structures for this purpose. In this paper, we propose a new method for representing RNA sequence profiles and finding structural alignment of RNAs based on profile context-sensitive hidden Markov models (profile-csHMMs). Unlike existing models, the proposed approach can handle any kind of RNA secondary structures, including pseudoknots. We show that profile-csHMMs can provide an effective framework for the computational analysis of RNAs and the identification of ncRNA genes

Clickstream Data Analysis: A Clustering Approach Based on Mixture Hidden Markov Models

Nowadays, the availability of devices such as laptops and cell phones enables one to browse the web at any time and place. As a consequence, a company needs to have a website so as to maintain or increase customer loyalty and reach potential new customers. Besides, acting as a virtual point-of-sale, the company portal allows it to obtain insights on potential customers through clickstream data, web generated data that track users accesses and activities in websites. However, these data are not easy to handle as they are complex, unstructured and limited by lack of clear information about user intentions and goals. Clickstream data analysis is a suitable tool for managing the complexity of these datasets, obtaining a cleaned and processed sequential dataframe ready to identify and analyse patterns. Analysing clickstream data is important for companies as it enables them to under stand differences in web user behaviour while they explore websites, how they move from one page to another and what they select in order to define business strategies tar geting specific types of potential costumers. To obtain this level of insight it is pivotal to understand how to exploit hidden information related to clickstream data. This work presents the cleaning and pre-processing procedures for clickstream data which are needed to get a structured sequential dataset and analyses these sequences by the application of Mixture of discrete time Hidden Markov Models (MHMMs), a statisti cal tool suitable for clickstream data analysis and profile identification that has not been widely used in this context. Specifically, hidden Markov process accounts for a time varying latent variable to handle uncertainty and groups together observed states based on unknown similarity and entails identifying both the number of mixture components re lating to the subpopulations as well as the number of latent states for each latent Markov chain. However, the application of MHMMs requires the identification of both the number of components and states. Information Criteria (IC) are generally used for model selection in mixture hidden Markov models and, although their performance has been widely studied for mixture models and hidden Markov models, they have received little attention in the MHMM context. The most widely used criterion is BIC even if its performance for these models depends on factors such as the number of components and sequence length. Another class of model selection criteria is the Classification Criteria (CC). They were defined specifically for clustering purposes and rely on an entropy measure to account for separability between groups. These criteria are clearly the best option for our purpose, but their application as model selection tools for MHMMs requires the definition of a suitable entropy measure. In the light of these considerations, this work proposes a classification criterion based on an integrated classification likelihood approach for MHMMs that accounts for the two latent classes in the model: the subpopulations and the hidden states. This criterion is a modified ICL BIC, a classification criterion that was originally defined in the mixture model context and used in hidden Markov models. ICL BIC is a suitable score to identify the number of classes (components or states) and, thus, to extend it to MHMMs we de fined a joint entropy accounting for both a component-related entropy and a state-related conditional entropy. The thesis presents a Monte Carlo simulation study to compare selection criteria per formance, the results of which point out the limitations of the most commonly used infor mation criteria and demonstrate that the proposed criterion outperforms them in identify ing components and states, especially in short length sequences which are quite common in website accesses. The proposed selection criterion was applied to real clickstream data collected from the website of a Sicilian company operating in the hospitality sector. Data was modelled by an MHMM identifying clusters related to the browsing behaviour of web users which provided essential indications for developing new business strategies. This thesis is structured as follows: after an introduction on the main topics in Chapter 1, we present the clickstream data and their cleaning and pre-processing steps in Chapter 2; Chapter 3 illustrates the structure and estimation algorithms of mixture hidden Markov models; Chapter 4 presents a review of model selection criteria and the definition of the proposed ICL BIC for MHMMs; the real clickstream data analysis follows in Chapter 5

The ITS2 Database III—sequences and structures for phylogeny

The internal transcribed spacer 2 (ITS2) is a widely used phylogenetic marker. In the past, it has mainly been used for species level classifications. Nowadays, a wider applicability becomes apparent. Here, the conserved structure of the RNA molecule plays a vital role. We have developed the ITS2 Database (http://its2.bioapps.biozentrum.uni-wuerzburg.de) which holds information about sequence, structure and taxonomic classification of all ITS2 in GenBank. In the new version, we use Hidden Markov models (HMMs) for the identification and delineation of the ITS2 resulting in a major redesign of the annotation pipeline. This allowed the identification of more than 160 000 correct full length and more than 50 000 partial structures. In the web interface, these can now be searched with a modified BLAST considering both sequence and structure, enabling rapid taxon sampling. Novel sequences can be annotated using the HMM based approach and modelled according to multiple template structures. Sequences can be searched for known and newly identified motifs. Together, the database and the web server build an exhaustive resource for ITS2 based phylogenetic analyses

Hidden Markov Model Variants and their Application

Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics. The approach is an on-going effort by its nature, as a multi-pass learning process, where each round is more informed than the last, and thereby allows a shift to the more powerful methods available for supervised learning at each iteration. It is envisaged that this "bootstrap" learning process will also be useful as a knowledge discovery tool. For such an ab initio prokaryotic gene-finder to work, however, it needs a mechanism to identify critical motif structure, such as those around the start of coding or start of transcription (and then, hopefully more). For eukaryotes, even with better start-of-coding identification, parsing of eukaryotic coding regions by the HMM is still limited by the HMM's single gene assumption, as evidenced by the poor performance in alternatively spliced regions. To address these complications an approach is described to expand the states in a eukaryotic gene-predictor HMM, to operate with two layers of DNA parsing. This extension from the single layer gene prediction parse is indicated after preliminary analysis of the C. elegans alt-splice statistics. State profiles have made use of a novel hash-interpolating MM (hIMM) method. A new implementation for an HMM-with-Duration is also described, with far-reaching application to gene-structure identification and analysis of channel current blockade data

Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models