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

Estimating the Worldwide Extent of Illegal Fishing

Illegal and unreported fishing contributes to overexploitation of fish stocks and is a hindrance to the recovery of fish populations and ecosystems. This study is the first to undertake a world-wide analysis of illegal and unreported fishing. Reviewing the situation in 54 countries and on the high seas, we estimate that lower and upper estimates of the total value of current illegal and unreported fishing losses worldwide are between $10 bn and$23.5 bn annually, representing between 11 and 26 million tonnes. Our data are of sufficient resolution to detect regional differences in the level and trend of illegal fishing over the last 20 years, and we can report a significant correlation between governance and the level of illegal fishing. Developing countries are most at risk from illegal fishing, with total estimated catches in West Africa being 40% higher than reported catches. Such levels of exploitation severely hamper the sustainable management of marine ecosystems. Although there have been some successes in reducing the level of illegal fishing in some areas, these developments are relatively recent and follow growing international focus on the problem. This paper provides the baseline against which successful action to curb illegal fishing can be judged

Recruitment of rare 3-grams at functional sites: Is this a mechanism for increasing enzyme specificity?

<p>Abstract</p> <p>Background</p> <p>A wealth of unannotated and functionally unknown protein sequences has accumulated in recent years with rapid progresses in sequence genomics, giving rise to ever increasing demands for developing methods to efficiently assess functional sites. Sequence and structure conservations have traditionally been the major criteria adopted in various algorithms to identify functional sites. Here, we focus on the distributions of the 20³different types of <it>3</it>-grams (or triplets of sequentially contiguous amino acid) in the entire space of sequences accumulated to date in the UniProt database, and focus in particular on the rare <it>3</it>-grams distinguished by their high entropy-based information content.</p> <p>Results</p> <p>Comparison of the UniProt distributions with those observed near/at the active sites on a non-redundant dataset of 59 enzyme/ligand complexes shows that the active sites preferentially recruit <it>3</it>-grams distinguished by their low frequency in the UniProt. Three cases, Src kinase, hemoglobin, and tyrosyl-tRNA synthetase, are discussed in details to illustrate the biological significance of the results.</p> <p>Conclusion</p> <p>The results suggest that recruitment of rare <it>3</it>-grams may be an efficient mechanism for increasing specificity at functional sites. Rareness/scarcity emerges as a feature that may assist in identifying key sites for proteins function, providing information complementary to that derived from sequence alignments. In addition it provides us (for the first time) with a means of identifying potentially functional sites from sequence information alone, when sequence conservation properties are not available.</p

Active learning for human protein-protein interaction prediction

Abstract Background Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. Conclusion Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.</p

Active Learning for Human Protein-Protein Interaction Prediction

Background: Biological processes in cells are carried out by means of protein protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results: Random forest (RF) has previously been shown to be effective for predicting proteinprotein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. Conclusion: Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.</p

Syllable - A Promising Recognition Unit for LVCSR

Ganapathiraju A, Goel V, Picone J, et al. Syllable - A Promising Recognition Unit for LVCSR. In: Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop. Santa Barbara, California, USA; 1997: 207-214

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

Rare and Frequent N-grams in Whole-genome Protein Sequences

The precise relationship between a primary protein sequence, its three-dimensional structure and its function in a complex cellular environment is one of the most fundamental unanswered questions in biology. Unprecedented amounts of genomic and proteomic data create an opportunity for attacking the sequence-structure-function mapping problem with data-driven methods. The mapping of biological sequences to form and function of proteins is conceptually similar to the mapping of words to meaning. This analogy is being studied by a growing body of research ([1] and pointers thereof). Thus, n-gram analysis (statistical analysis of co-occurrence of words in a text) has found applications to biological sequences, using various types of “vocabulary”, for example nucleotides and amino acids. Here, we investigate n-gram statistics in whole-genome sequences to address the following questions: How characteristic is the amino acid n-gram distribution for specific organisms? Do different organisms tend to use different “phrases”? What is the “meaning” of a rare sequence in a protein? The long-term goal is to provide a useful starting point to derive language models with defined vocabulary and phrase preferences and grammatical rules for protein sequences of different organisms