Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics最新文献

{"title":"Interpretable Machine Learning Approach Reveals Developmental Gene Expression Biomarkers for Cancer Patient Outcomes at Early Stages","authors":"Alisha Kamat, Ting Jin, So Yeon Min, F. Talos, Jonas S. Almeida, Daifeng Wang","doi":"10.1145/3233547.3233619","DOIUrl":"https://doi.org/10.1145/3233547.3233619","url":null,"abstract":"Understanding the molecular mechanisms underlying early cancer development is still a challenge. To address this, we developed an interpretable, data-driven machine learning approach to identify the gene biomarkers that predict the clinical outcomes of early cancer patients. As a demonstration, we applied this approach into large-scale pan-cancer datasets including TCGA to find out how effective it would be at identifying the developmental gene expression biomarkers across tumor stages for various cancer types. Results confirmed that artificial neural network prediction embedding nonlinear feature selection outperforms other classifiers. Moreover, and more relevant to the goal of machine learning interpretable classifiers, we found that early cancer patient groups clustered by the biomarkers selected have significantly more survival differences than ones by early TNM stages, suggesting that this method identified novel early cancer molecular biomarkers. Furthermore, using lung cancer as a study case, we leveraged the hierarchical architectures of neural network to identify the developmental regulatory networks controlling the expression of early cancer biomarkers, providing mechanistic insights of functional genomics driving the onset of cancer development. Finally, we reported the drugs targeting early cancer biomarkers, revealing potential genomic medicine affecting the early cancer development.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115711372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sub-Class Differences of PH-Dependent HIV GP120-CD4 Interactions","authors":"S. Morton, Jonathan Howton, Joshua L. Phillips","doi":"10.1145/3233547.3233711","DOIUrl":"https://doi.org/10.1145/3233547.3233711","url":null,"abstract":"Research in the field of HIV transmission has yet to provide a vaccine for this imponderable virus. Though progress has been made to extend the life of those chronically infected, a solution to the transmission of the disease remains elusive. Previous studies involving electrostatic surface charge analysis revealed the sensitivity of gp120 envelope (Env) protein function to changes in pH across levels consistent with those found in the human body. A prototype computational approach was developed and found to agree with these results. A refined process was developed capable of classifying Env sequences/structures through machine learning techniques. We expound this analytical procedure to encompass residue-level analysis and include minimization steps to ensure the integrity of the protein models. Additionally, the process has been enhanced with advanced data compression techniques to allow for more in-depth analysis of the systems. In this research we explore a new technique termed electrostatic variance masking (EVM), that reveals what we hypothesize to be the mechanistic residues responsible for the pH sensitivity of Env binding site. The data implies that a conserved set of core residues may be responsible for modulation of the binding process in varying environmental conditions mainly involving pH.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpreting Negative Data on Antipeptide Paratope Binding to Support Development of B-Cell Epitope Prediction for Vaccine Design and Other Translational Applications","authors":"S. Caoili","doi":"10.1145/3233547.3233627","DOIUrl":"https://doi.org/10.1145/3233547.3233627","url":null,"abstract":"The design of synthetic vaccine peptides and other constructs (e.g., for developing immunodiagnostics) is informed by B-cell epitope prediction for antipeptide paratopes, which crucially depends on physicochemically and biologically meaningful interpretation of pertinent experimental data as regards paratope-epitope binding, with negative data being particularly problematic as they may be due to artefacts of immunization and immunoassays. Yet, the problem posed by negative data remains to be comprehensively addressed in a manner that clearly defines their role in the further development of B-cell epitope prediction. Hence, published negative data were surveyed and analyzed herein to identify key issues impacting on B-cell epitope prediction. Data were retrieved via searches using the Immune Epitope Database (IEDB) and review of underlying primary sources in literature to identify said issues, which include (1) inherent tendency toward false-negative data with use of solid-phase immunoassays and/or monoclonal paratopes, (2) equivocal data (i.e., both positive and negative data obtained from similar experiments), and (3) failure of antipeptide paratopes to cross-react with antigens of covalent structure and/or conformation different from that of the peptide immunogens despite apparent identity between curated epitope sequences. Analysis of experimental details thus focused on negative data from fluid-phase (e.g., immunoprecipitation) assays for detection of polyclonal paratope-epitope binding. Underlying literature references were reviewed to confirm the identification of negative data included for analysis. Furthermore, data from assays to detect cross-reaction of antipeptide antibody with protein antigen were included only if supported by positive data on either the corresponding reaction of the same antibody with peptide antigen or cross-reaction of said antibody with denatured protein antigen, to exclude the possibility that negative data on cross-reaction were due to absence of antipeptide paratopes in the first place (e.g., because of failed immunization due to insufficient immunogenicity and/or immune tolerance). Among currently available negative binding data on antipeptide antibodies, very few are on polyclonal responses yet also clearly attributable to conformational differences between peptide immunogens and native cognate proteins thereof. This dearth of negative data suitable for benchmarking B-cell epitope prediction conceivably could be addressed by generating positive data on binding of polyclonal antipeptide antibodies to cognate-protein sequences (e.g., in solid-phase immunoassays using unfolded protein antigen) to complement negative data on failure of the same antibodies to cross-react with native protein (e.g., in fluid-phase immunoassays, without artefactual covalent modification of antigens that tends to produce false-negative results). As regards cross-reactive binding of native cognate proteins by antipeptide antibodies (e.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114646502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Tools for Predicting the Functional Impact of mtDNA Variants","authors":"Madeline P. Griffin, Catherine E. Welsh","doi":"10.1145/3233547.3233643","DOIUrl":"https://doi.org/10.1145/3233547.3233643","url":null,"abstract":"Recent advances in DNA sequencing technologies has transformed the study of DNA sequence variation. Over the last decade, the development of a number of functional impact predictors and annotation tools have been implemented to aid in this DNA variant analysis. While many annotation tools and pipelines have been built to annotate nuclear genome variants, only a few software predictors address the thousands of variants found in human mitochondrial DNA. Many prediction tools built for nuclear DNA have been retrofitted to annotate mitochondrial DNA, but because of the vast differences between the two, nuclear annotators fail to produce accurate predictions for mitochondrial mutations. Conventional annotation tools and predictors such as SIFT and PolyPhen2 are a few of the tools that produce less than accurate pathogenicity scores for mitochondrial variants. More recently, tools such as APOGEE have addressed the need for specialized tools to annotate mtDNA exonic variants with high-confidence. In addition, most of the annotation tools only annotate exonic mutations, but variants in mitochondrial tRNA and rRNA are important and are a common cause of mitochondrial disease. A few papers have addressed the need to accurately predict the pathogenicity of tRNA variants, such as MitoTIP, while no known tools exist for annotating rRNA variant pathogenicity for mtDNA variants. We have constructed a comparative analysis of both standard and non-standard annotation tools and their ability to accurately predict the pathogenicity of mitochondrial mutations. We carefully curated a complete list of all potential non-synonymous exonic, tRNA and rRNA mitochondrial mutations and ran selected tools for each dataset. We have analyzed the accuracy and precision of each tool compared to the consensus among the tools combined with pathogenicity predictions from MITOMAP disease associations. Over the course of our testing, we confirmed that many of the prediction tools typically used for nuclear DNA were subpar when tested on mitochondrial DNA. Newer annotation tools built specifically for mtDNA such as APOGEE had higher overall assessment scores. Based on our analysis, we are creating an online annotation tool specifically for mtDNA variants that integrates pathogencity scores from our top-rated prediction tools.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117264609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tutorial: Rapidly Identifying Disease-associated Rare Variants using Annotation and Machine Learning at Whole-genome Scale Online","authors":"Alex V. Kotlar, T. Wingo","doi":"10.1145/3233547.3233666","DOIUrl":"https://doi.org/10.1145/3233547.3233666","url":null,"abstract":"Accurately identifying disease-associated alleles from large sequencing experiments remains challenging. During this tutorial, participants will learn how to use a new variant annotation and filtering web app called Bystro (https://bystro.io/) to analyze sequencing experiments. Bystro is the first online, cloud-based application that makes variant annotation and filtering accessible to all researchers for even the largest, terabyte-sized whole-genome experiments containing thousands of samples. Using its general-purpose, natural-language filtering engine, attendees will be shown how to perform quality control measures and identify alleles of interest. They will then be guided in exporting those variants, and using them in both a regression context by performing rare-variant association tests in R, as well as classification context by training new machine learning models in Python's scikit-learn library.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"2 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120845744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Experimental Evidence in Biomedical Abstracts Relevant to Drug-Drug Interactions","authors":"Gongbo Zhang, Debarati Roychowdhury, Pengyuan Li, Heng-Yi Wu, Shijun Zhang, Lang Li, H. Shatkay","doi":"10.1145/3233547.3233568","DOIUrl":"https://doi.org/10.1145/3233547.3233568","url":null,"abstract":"Drug-drug interactions (DDIs) may cause significant adverse effects. As prescribing multiple drugs becomes increasingly common, it is necessary to verify potential interactions among drugs that are used at the same time. Likely DDIs can be identified with higher confidence if supporting experimental evidence is provided. Such information is usually published in biomedical literature. While current retrieval and classification methods can identify publications related to DDIs, not all articles that discuss DDIs contain experimental evidence. A publication that does present evidence typically contains sentences conveying information about specific experimental methods and their results. A classifier that can readily identify such sentences can be useful for obtaining explicit and reliable information concerning DDIs. In this work, we develop two text classifiers to distinguish scientific sentence-fragments bearing experimental evidence from fragments that do not present such evidence. We focus on a corpus of text containing biomedical abstracts related to drug interactions. The classifiers are trained and tested on a manually curated set of sentence-fragments in these abstracts. Our experiments demonstrate a high level of performance (at least 89% precision and recall) suggesting the applicability of these classifiers toward improving retrieval of reliable information pertaining to drug interactions.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124090642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agile Clinical Decision Support Development and Implementation","authors":"M. Basit, V. Kannan, D. Willett","doi":"10.1145/3233547.3233663","DOIUrl":"https://doi.org/10.1145/3233547.3233663","url":null,"abstract":"Designing effective Clinical Decision Support (CDS) tools in an Electronic Health Record (EHR) can prove challenging, due to complex real-world scenarios and newly-discovered requirements. Deploying new CDS tools shares much in common with new product development, where \"agile\" principles and practices consistently prove effective. Agile methods can thus prove helpful on CDS projects, including time-boxed \"sprints\" and lightweight requirements gathering with User Stories. Modeling CDS behavior promotes unambiguous shared understanding of desired behavior, but risks analysis paralysis: an Agile Modeling approach can foster effective rapid-cycle CDS design and optimization. The agile practice of automated testing for test-driven design and regression testing can be applied to CDS development using open-source tools. Ongoing monitoring of CDS behavior once released to production can identify anomalies and prompt rapid-cycle redesign to further enhance CDS effectiveness. The tutorial participant will learn about these topics in interactive didactic sessions, with time for practicing the techniques taught.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Updates in Network Synthesis Models to Create An Improved Benchmark for Network Alignment Algorithms","authors":"Hyun-Myung Woo, Hyundoo Jeong, Byung-Jun Yoon","doi":"10.1145/3233547.3233684","DOIUrl":"https://doi.org/10.1145/3233547.3233684","url":null,"abstract":"Network synthesis models in NAPAbench provide effective means to generate synthetic network families that can be used to rigorously assess the performance of network alignment algorithms. In recent years, the protein-protein-interaction (PPI) databases have been significantly updated, hence the network synthesis models in NAPAbench need to be updated to be able to create synthetic network families whose characteristics are close to those of real PPI networks. In this work, we present updated models based on an extensive analysis of real-world PPI networks and their key features.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RegMIL","authors":"Mohammad Arifur Rahman, H. Rangwala","doi":"10.1145/3233547.3233585","DOIUrl":"https://doi.org/10.1145/3233547.3233585","url":null,"abstract":"Advances in DNA sequencing technologies have paved the way for Metagenomics; defined as the collective sequencing of co-existing microbial communities in a host environment. Several researchers and clinicians have embarked on studying the role of these microbes with respect to human health and diseases, whereas others are using metagenomics to monitor the impact of external factors (e.g., Gulf oil spill) on ecosystems. Lack of accurate and efficient analytical methods is an impediment for the identification of the function and presence of microbial organism within different clinical samples, reducing our ability to elucidate the microbial-host interactions and discover novel therapeutics. In this paper, we present a Multiple Instance Learning (MIL) based computational pipeline to predict clinical phenotype from metagenomic data. We use data from well-known metagenomic studies of Liver Cirrhosis and Inflammatory Bowel Disease (IBD) to evaluate our approach. We show that our proposed pipeline outperforms comparative MIL as well as non-MIL methods.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129626054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Algorithmic Approach to the Representation of Biological Information and Long-term Memory","authors":"J. Pfaltz","doi":"10.1145/3233547.3233679","DOIUrl":"https://doi.org/10.1145/3233547.3233679","url":null,"abstract":"Any concept of a long-term, relatively permanent, memory must include a mechanism for encoding episodic experiences into some appropriate format and later retrieving and recreating a reasonable approximation of the original \"episode\". Psychologists commonly call these processes \"consolidation\" and \"recall\" [8]. In this paper, we present two computable functions tht closely imitate these processes. Of necessity, we must posit a recording format. It will consist of a system of cycles which support very high information content and are, furthermore, commonly found as protein polymer structures within the membranes of all our cells.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127074268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}