Ensemble Of Hidden Markov Models Research Articles

UPP2: fast and accurate alignment of datasets with fragmentary sequences.

Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise. https://github.com/gillichu/sepp. Supplementary data are available at Bioinformatics online.

WITCH: Improved Multiple Sequence Alignment Through Weighted Consensus Hidden Markov Model Alignment.

Accurate multiple sequence alignment is challenging on many data sets, including those that are large, evolve under high rates of evolution, or have sequence length heterogeneity. While substantial progress has been made over the last decade in addressing the first two challenges, sequence length heterogeneity remains a significant issue for many data sets. Sequence length heterogeneity occurs for biological and technological reasons, including large insertions or deletions (indels) that occurred in the evolutionary history relating the sequences, or the inclusion of sequences that are not fully assembled. Ultra-large alignments using Phylogeny-Aware Profiles (UPP) (Nguyen et al. 2015) is one of the most accurate approaches for aligning data sets that exhibit sequence length heterogeneity: it constructs an alignment on the subset of sequences it considers "full-length," represents this "backbone alignment" using an ensemble of hidden Markov models (HMMs), and then adds each remaining sequence into the backbone alignment based on an HMM selected for that sequence from the ensemble. Our new method, WeIghTed Consensus Hmm alignment (WITCH), improves on UPP in three important ways: first, it uses a statistically principled technique to weight and rank the HMMs; second, it uses HMMs from the ensemble rather than a single HMM; and third, it combines the alignments for each of the selected HMMs using a consensus algorithm that takes the weights into account. We show that this approach provides improved alignment accuracy compared with UPP and other leading alignment methods, as well as improved accuracy for maximum likelihood trees based on these alignments.

Mixed-dependency models for multi-resident activity recognition in smart homes

Recent growing interest in ambient intelligent environments has driven a desire for effective models to reason about activities of multiple residents. Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this research, however current works tend to focus on developing statistical algorithms for prediction, whilst there still lacks a study to fully understand the relations of residents’ behaviours and how they are reflected through the sensors’ states. In this paper we investigate the dependencies of the activities from residents and their interaction with the environments. We represent such dependencies in Bayesian networks that leads to construction of six variants of Hidden Markov Models (HMMs). Furthermore, we argue that a complete model should embody more than one type of dependency. Therefore, we propose an ensemble of HMMs, and then generalize it to a novel mixed-dependency model. In the experiments we perform intensive evaluation of our study on multi-resident activity recognition task. The results show that the proposed models outperform other models in three smart home environments, thus asserting our hypothesis.

GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media.

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs. Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.

Ultra-large alignments using phylogeny-aware profiles.

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0688-z) contains supplementary material, which is available to authorized users.

Alignment of multiple proteins with an ensemble of hidden Markov models.

In this paper, we developed a new method that progressively constructs and updates a set of alignments by adding sequences in a certain order to each of the existing alignments. Each of the existing alignments is modelled with a profile Hidden Markov Model (HMM) and an added sequence is aligned to each of these profile HMMs. We introduced an integer parameter for the number of profile HMMs. The profile HMMs are then updated based on the alignments with leading scores. Our experiments on BaliBASE showed that our approach could efficiently explore the alignment space and significantly improve the alignment accuracy.

Finding words in alphabet soup: Inference on freeform character recognition for historical scripts

This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histograms of gradients as features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character detections to recognize complete words in ambiguous handwritten text, drawing on character n -gram and physical separation models. Experiments with two corpora of handwritten historic documents show that this approach recognizes known words more accurately than previous efforts, and can also recognize out-of-vocabulary words.