FEATURE GENERATION AND ANALYSIS APPLIED TO SEQUENCE CLASSIFICATION FOR SPLICE-SITE PREDICTION

Sequence classification is an important problem in many real-world applications. Sequence data often contain no explicit "signals," or features, to enable the construction of classification algorithms. Extracting and interpreting the most useful features is challenging, and hand construction of good features is the basis of many classification algorithms. In this thesis, I address this problem by developing a feature-generation algorithm (FGA). FGA is a scalable method for automatic feature generation for sequences; it identifies sequence components and uses domain knowledge, systematically constructs features, explores the space of possible features, and identifies the most useful ones. In the domain of biological sequences, splice-sites are locations in DNA sequences that signal the boundaries between genetic information and intervening non-coding regions. Only when splice-sites are identified with nucleotide precision can the genetic information be translated to produce functional proteins. In this thesis, I address this fundamental process by developing a highly accurate splice-site prediction model that employs our sequence feature-generation framework. The FGA model shows statistically significant improvements over state-of-the-art splice-site prediction methods. So that biologists can understand and interpret the features FGA constructs, I developed SplicePort, a web-based tool for splice-site prediction and analysis. With SplicePort the user can explore the relevant features for splicing, and can obtain splice-site predictions for the sequences based on these features. For an experimental biologist trying to identify the critical sequence elements of splicing, SplicePort offers flexibility and a rich motif exploration functionality, which may help to significantly reduce the amount of experimentation needed. In this thesis, I present examples of the observed feature groups and describe efforts to detect biological signals that may be important for the splicing process. Naturally, FGA can be generalized to other biologically inspired classification problems, such as tissue-specific regulatory elements, polyadenylation sites, promoters, as well as other sequence classification problems, provided we have sufficient knowledge of the new domain

SplicePort—An interactive splice-site analysis tool

SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org

Structural footprinting in protein structure comparison: the impact of structural fragments

<p>Abstract</p> <p>Background</p> <p>One approach for speeding-up protein structure comparison is the <it>projection approach</it>, where a protein structure is mapped to a high-dimensional vector and structural similarity is approximated by distance between the corresponding vectors. <it>Structural footprinting methods </it>are projection methods that employ the same general technique to produce the mapping: first select a representative set of structural fragments as <it>models </it>and then map a protein structure to a vector in which each dimension corresponds to a particular model and "counts" the number of times the model appears in the structure. The main difference between any two structural footprinting methods is in the set of models they use; in fact a large number of methods can be generated by varying the type of structural fragments used and the amount of detail in their representation. How do these choices affect the ability of the method to detect various types of structural similarity?</p> <p>Results</p> <p>To answer this question we benchmarked three structural footprinting methods that vary significantly in their selection of models against the CATH database. In the first set of experiments we compared the methods' ability to detect structural similarity characteristic of evolutionarily related structures, i.e., structures within the same CATH superfamily. In the second set of experiments we tested the methods' agreement with the boundaries imposed by classification groups at the Class, Architecture, and Fold levels of the CATH hierarchy.</p> <p>Conclusion</p> <p>In both experiments we found that the method which uses secondary structure information has the best performance on average, but no one method performs consistently the best across all groups at a given classification level. We also found that combining the methods' outputs significantly improves the performance. Moreover, our new techniques to measure and visualize the methods' agreement with the CATH hierarchy, including the threshholded affinity graph, are useful beyond this work. In particular, they can be used to expose a similar composition of different classification groups in terms of structural fragments used by the method and thus provide an alternative demonstration of the continuous nature of the protein structure universe.</p

Comprehensively identifying Long Covid articles with human-in-the-loop machine learning

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid collection shows that (1) most Long Covid articles do not refer to Long Covid by any name (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovi

Click-words: learning to predict document keywords from a user perspective

Motivation: Recognizing words that are key to a document is important for ranking relevant scientific documents. Traditionally, important words in a document are either nominated subjectively by authors and indexers or selected objectively by some statistical measures. As an alternative, we propose to use documents' words popularity in user queries to identify click-words, a set of prominent words from the users' perspective. Although they often overlap, click-words differ significantly from other document keywords