DNA ANALYSIS USING GRAMMATICAL INFERENCE

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA. An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm. Testing shows that the accuracy of inferred languages for components of DNA are consistently accurate. By using the proposed algorithm languages are inferred for coding DNA with average conditional probability over 80%. This reveals that languages for components of DNA can be inferred and are useful independent of the process that created them. These languages can then be analyzed or used for other tasks in computational biology. To illustrate potential applications of regular grammars for DNA components, an inferred language for exon sequences is applied as post processing to Hidden Markov exon prediction to reduce the number of wrong exons detected and improve the specificity of the model significantly

Animal vocal sequences: not the Markov chains we thought they were.

Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.This is the author's accepted manuscript and will be under embargo until the 20th of August 2015. This final version is published by Royal Society Publishing here: http://dx.doi.org/10.1098/rspb.2014.1370

New topic detection in microblogs and topic model evaluation using topical alignment

textThis thesis deals with topic model evaluation and new topic detection in microblogs. Microblogs are short and thus may not carry any contextual clues. Hence it becomes challenging to apply traditional natural language processing algorithms on such data. Graphical models have been traditionally used for topic discovery and text clustering on sets of text-based documents. Their unsupervised nature allows topic models to be trained easily on datasets meant for specific domains. However the advantage of not requiring annotated data comes with a drawback with respect to evaluation difficulties. The problem aggravates when the data comprises microblogs which are unstructured and noisy. We demonstrate the application of three types of such models to microblogs - the Latent Dirichlet Allocation, the Author-Topic and the Author-Recipient-Topic model. We extensively evaluate these models under different settings, and our results show that the Author-Recipient-Topic model extracts the most coherent topics. We also addressed the problem of topic modeling on short text by using clustering techniques. This technique helps in boosting the performance of our models. Topical alignment is used for large scale assessment of topical relevance by comparing topics to manually generated domain specific concepts. In this thesis we use this idea to evaluate topic models by measuring misalignments between topics. Our study on comparing topic models reveals interesting traits about Twitter messages, users and their interactions and establishes that joint modeling on author-recipient pairs and on the content of tweet leads to qualitatively better topic discovery. This thesis gives a new direction to the well known problem of topic discovery in microblogs. Trend prediction or topic discovery for microblogs is an extensive research area. We propose the idea of using topical alignment to detect new topics by comparing topics from the current week to those of the previous week. We measure correspondence between a set of topics from the current week and a set of topics from the previous week to quantify five types of misalignments: \textit{junk, fused, missing} and \textit{repeated}. Our analysis compares three types of topic models under different settings and demonstrates how our framework can detect new topics from topical misalignments. In particular so-called \textit{junk} topics are more likely to be new topics and the \textit{missing} topics are likely to have died or die out. To get more insights into the nature of microblogs we apply topical alignment to hashtags. Comparing topics to hashtags enables us to make interesting inferences about Twitter messages and their content. Our study revealed that although a very small proportion of Twitter messages explicitly contain hashtags, the proportion of tweets that discuss topics related to hashtags is much higher.Computer Science