Improving recognition accuracy on CVSD speech under mismatched conditions

Emerging technology in mobile communications is seeing increasingly high acceptance as a preferred choice for last-mile communication. There have been a wide range of techniques to achieve signal compression to suit to the smaller bandwidths available on mobile communication channels; but speech recognition methods have seen success mostly only in controlled speech environments. However, designing of speech recognition systems for mobile communications is crucial in order to provide voice enabled command and control and for applications like Mobile Voice Commerce. Continuously Variable Slope Delta (CVSD) modulation, a technique for low bitrate coding of speech, has been in use particularly in military wireless environments for over 30 years, and is now also adopted by BlueTooth. CVSD is particularly suitable for Internet and mobile environments due to its robustness against transmission errors, and simplicity of implementation and the absence of a need for synchronization. In this paper, we study some characteristics of the CVSD speech in the context of robust recognition of compressed speech, and present two methods of improving the recognition accuracy in Automatic Speech Recognition (ASR) systems. We study the characteristics of the features extracted for ASR and how they relate to the corresponding features computed from Pulse Coded Modulation (PCM) speech and apply this relation to correct the CVSD features to improve recognition accuracy. Secondly we show that the ASR done on bit-streams directly, gives a good recognition accuracy and when combined with our approach gives a better accuracy

A pilot study on the prevalence of DNA palindromes in breast cancer genomes

Background DNA palindromes are a unique pattern of repeat sequences that are present in the human genome. It consists of a sequence of nucleotides in which the second half is the complement of the first half but appearing in reverse order. These palindromic sequences may have a significant role in DNA replication, transcription and gene regulation processes. They occur frequently in human cancers by clustering at specific locations of the genome that undergo gene amplification and tumorigenesis. Moreover, some studies showed that palindromes are clustered in amplified regions of breast cancer genomes especially in chromosomes (chr) 8 and 11. With the large number of personal genomes and cancer genomes becoming available, it is now possible to study their association to diseases using computational methods. Here, we conducted a pilot study on chromosomes 8 and 11 of cancer genomes to identify computationally the differentially occurring palindromes. Methods We processed 69 breast cancer genomes from The Cancer Genome Atlas including serum-normal and tumor genomes, and 1000 Genomes to serve as control group. The Biological Language Modelling Toolkit (BLMT) computes palindromes in whole genomes. We developed a computational pipeline integrating BLMT to compute and compare prevalence of palindromes in personal genomes. Results We carried out a pilot study on chr 8 and chr 11 taking into account single nucleotide polymorphisms, insertions and deletions. Of all the palindromes that showed any variation in cancer genomes, 38% of what were near breast cancer genes happened to be the most differentiated palindromes in tumor (i.e. they ranked among the top 25% by our heuristic measure). Conclusions These results will shed light on the prevalence of palindromes in oncogenes and the mutations that are present in the palindromic regions that could contribute to genomic rearrangements, and breast cancer progression

Active machine learning for transmembrane helix prediction

Abstract Background About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others. Results An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins. Conclusion Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments

Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities

N-gram analysis of 970 microbial organisms reveals presence of biological language models

<p>Abstract</p> <p>Background</p> <p>It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree.</p> <p>Results</p> <p>We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of <it>Shigellae flexneri 2a</it>, which belongs to the <it>Gammaproteobacteria </it>class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from <it>S. flexneri</it>. The organisms of this genus, which happen to be pathotypes of <it>E.coli</it>, also have the closest perplexity values with <it>E. coli.</it></p> <p>Conclusion</p> <p>Whole proteome sequences of microbial organisms have been shown to contain particular n-gram sequences in abundance in one organism but occurring very rarely in other organisms, thereby serving as proteome signatures. Further it has also been shown that perplexity, a statistical measure of similarity of n-gram composition, can be used to predict evolutionary distance within a genus in the phylogenetic tree.</p

Application of language technologies in biology: Feature extraction and modeling for transmembrane helix prediction

This thesis provides new insights into the application of algorithms developed for language processing towards problems in mapping of protein sequences to their structure and function, in direct analogy to the mapping of words to meaning in natural language. While there have been applications of language algorithms previously in computational biology, most notably hidden Markov models, there has been no systematic investigation of what are appropriate word equivalents and vocabularies in biology to date. In this thesis, we consider amino acids, chemical vocabularies and amino acid properties as fundamental building blocks of protein sequence language and study n-grams and other positional word-associations and latent semantic analysis towards prediction transmembrane helices. First, a toolkit referred to as the Biological Language Modeling Toolkit has been developed for biological sequence analysis through amino acid n-gram and amino acid word-association analysis. N-gram comparisons across genomes showed that biological sequence language differs from organism to organism, and has resulted in identification of genome signatures. Next, we used a biologically well established mapping problem, namely the mapping of protein sequences to their secondary structures, to quantitatively compare the utility of different fundamental building blocks in representing protein sequences. We found that the different vocabularies capture different aspects of protein secondary structure best. Finally, the conclusions from the study of biological vocabularies were used, in combination with the latent semantic analysis and signal processing techniques to address the biologically important but technically challenging and unsolved problem of predicting transmembrane segments. This work led to the development of TMpro, which achieves reduced transmembrane segment prediction error rate by 20-50% compared to previous state-of-the-art methods. The method is a novel approach of analyzing amino-acid property sequences as opposed to analyzing amino acid sequences: following our work, it has already been applied towards protein remote homology detection and protein structural type classifications by others

Antibody Exchange: Information Extraction of Biological Antibody Donation and a Web-Portal to Find Donors and Seekers

Bio-molecular reagents, like antibodies that are required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Antibody Exchange (or more general ‘Bio-Resource Exchange’) that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines, and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, we also studied the extent of antibody donations by mining the acknowledgement sections of scientific articles. Specifically, we extracted the name of the donor, his/her affiliation, and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we adopted two approaches—a rule based algorithm and a bootstrapped pattern learning algorithm. The algorithms extracted donor names, affiliations, and antibody names with average accuracies of 57% and 62%, respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future