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Pacific Symposium on Biocomputing | Page 3

Dragutin Petkovic, Russ Altman, Mike Wong, Arthur Vigil
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often difficult to understand and explain resulting in barriers to their adoption and validation. Our work (RFEX) focuses on enhancing Random Forest (RF) classifier explainability by developing easy to interpret explainability summary reports from trained RF classifiers as a way to improve the explainability for (often non-expert) users...
2018: Pacific Symposium on Biocomputing
Randal S Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H Moore
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset...
2018: Pacific Symposium on Biocomputing
Kipp W Johnson, Benjamin S Glicksberg, Rachel A Hodos, Khader Shameer, Joel T Dudley
Hypertension is a major risk factor for ischemic cardiovascular disease and cerebrovascular disease, which are respectively the primary and secondary most common causes of morbidity and mortality across the globe. To alleviate the risks of hypertension, there are a number of effective antihypertensive drugs available. However, the optimal treatment blood pressure goal for antihypertensive therapy remains an area of controversy. The results of the recent Systolic Blood Pressure Intervention Trial (SPRINT) trial, which found benefits for intensive lowering of systolic blood pressure, have been debated for several reasons...
2018: Pacific Symposium on Biocomputing
Hyun-Hwan Jeong, Hari Krishna Yalamanchili, Caiwei Guo, Joshua M Shulman, Zhandong Liu
Transposable elements (TEs) are DNA sequences which are capable of moving from one location to another and represent a large proportion (45%) of the human genome. TEs have functional roles in a variety of biological phenomena such as cancer, neurodegenerative disease, and aging. Rapid development in RNA-sequencing technology has enabled us, for the first time, to study the activity of TE at the systems level.However, efficient TE analysis tools are not yet developed. In this work, we developed SalmonTE, a fast and reliable pipeline for the quantification of TEs from RNA-seq data...
2018: Pacific Symposium on Biocomputing
Lia X Harrington, Gregory P Way, Jennifer A Doherty, Casey S Greene
Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways - such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives...
2018: Pacific Symposium on Biocomputing
Benjamin S Glicksberg, Riccardo Miotto, Kipp W Johnson, Khader Shameer, Li Li, Rong Chen, Joel T Dudley
Accurate and robust cohort definition is critical to biomedical discovery using Electronic Health Records (EHR). Similar to prospective study designs, high quality EHR-based research requires rigorous selection criteria to designate case/control status particular to each disease. Electronic phenotyping algorithms, which are manually built and validated per disease, have been successful in filling this need. However, these approaches are time-consuming, leading to only a relatively small amount of algorithms for diseases developed...
2018: Pacific Symposium on Biocomputing
Tiffany J Callahan, William A Baumgartner, Michael Bada, Adrianne L Stefanski, Ignacio Tripodi, Elizabeth K White, Lawrence E Hunter
Our knowledge of the biological mechanisms underlying complex human disease is largely incomplete. While Semantic Web technologies, such as the Web Ontology Language (OWL), provide powerful techniques for representing existing knowledge, well-established OWL reasoners are unable to account for missing or uncertain knowledge. The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge. Therefore, robust methods which facilitate inductive inference on rich OWL-encoded knowledge are needed...
2018: Pacific Symposium on Biocomputing
Brett K Beaulieu-Jones, Patryk Orzechowski, Jason H Moore
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient's interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers...
2018: Pacific Symposium on Biocomputing
Monica Agrawal, Marinka Zitnik, Jure Leskovec
Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins. However, the success of such methods has been limited, and failure cases have not been well understood...
2018: Pacific Symposium on Biocomputing
Shefali Setia Verma, Anurag Verma, Anna Okula Basile, Marta-Byrska Bishop, Christian Darabos
The analysis of large biomedical data often presents with various challenges related to not just the size of the data, but also to data quality issues such as heterogeneity, multidimensionality, noisiness, and incompleteness of the data. The data-intensive nature of computational genomics problems in biomedical informatics warrants the development and use of massive computer infrastructure and advanced software tools and platforms, including but not limited to the use of cloud computing. Our session aims to address these challenges in handling big data for designing a study, performing analysis, and interpreting outcomes of these analyses...
2018: Pacific Symposium on Biocomputing
Jielin Xu, Kelly Regan-Fendt, Siyuan Deng, William E Carson, Philip R O Payne, Fuhai Li
The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources...
2018: Pacific Symposium on Biocomputing
Gregory P Way, Casey S Greene
The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors...
2018: Pacific Symposium on Biocomputing
Milo R Smith, Benjamin S Glicksberg, Li Li, Rong Chen, Hirofumi Morishita, Joel T Dudley
High and increasing prevalence of neurodevelopmental disorders place enormous personal and economic burdens on society. Given the growing realization that the roots of neurodevelopmental disorders often lie in early childhood, there is an urgent need to identify childhood risk factors. Neurodevelopment is marked by periods of heightened experience-dependent neuroplasticity wherein neural circuitry is optimized by the environment. If these critical periods are disrupted, development of normal brain function can be permanently altered, leading to neurodevelopmental disorders...
2018: Pacific Symposium on Biocomputing
Emily K Mallory, Ambika Acharya, Stefano E Rensi, Peter J Turnbaugh, Roselie A Bright, Russ B Altman
Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response...
2018: Pacific Symposium on Biocomputing
Yunan Luo, Sheng Wang, Jinfeng Xiao, Jian Peng
A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data...
2018: Pacific Symposium on Biocomputing
Rachel Hodos, Ping Zhang, Hao-Chih Lee, Qiaonan Duan, Zichen Wang, Neil R Clark, Avi Ma'ayan, Fei Wang, Brian Kidd, Jianying Hu, David Sontag, Joel Dudley
Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i...
2018: Pacific Symposium on Biocomputing
Peyton Greenside, Maureen Hillenmeyer, Anshul Kundaje
Identification of small molecule ligands that bind to proteins is a critical step in drug discovery. Computational methods have been developed to accelerate the prediction of protein-ligand binding, but often depend on 3D protein structures. As only a limited number of protein 3D structures have been resolved, the ability to predict protein-ligand interactions without relying on a 3D representation would be highly valuable. We use an interpretable confidence-rated boosting algorithm to predict protein-ligand interactions with high accuracy from ligand chemical substructures and protein 1D sequence motifs, without relying on 3D protein structures...
2018: Pacific Symposium on Biocomputing
Xintong Chen, Sander Houten, Kimaada Allette, Robert P Sebra, Gustavo Stolovitzky, Bojan Losic
We characterize the transcriptional splicing landscape of a prostate cancer cell line treated with a previously identified synergistic drug combination. We use a combination of third generation long-read RNA sequencing technology and short-read RNAseq to create a high-fidelity map of expressed isoforms and fusions to quantify splicing events triggered by treatment. We find strong evidence for drug-induced, coherent splicing changes which disrupt the function of oncogenic proteins, and detect novel transcripts arising from previously unreported fusion events...
2018: Pacific Symposium on Biocomputing
Richard Bourgon, Frederick E Dewey, Zhengyan Kan, Shuyu D Li
As the impact of genetics, genomics, and bioinformatics on drug discovery has been increasingly recognized, this session of the 2018 Pacific Symposium on Biocomputing (PSB) aims to facilitate scientific discussions between academia and pharmaceutical industry on how to best apply genetics, genomics and bioinformatics to enable drug discovery. The selected papers focus on developing and applying computational approaches to understand drug mechanisms of action and develop drug combination strategies, to enable in silico drug screening, and to further delineate disease pathways for target identification and validation...
2018: Pacific Symposium on Biocomputing
Philip R O Payne, Kun Huang, Nigam H Shah, Jessica Tenenbaum
The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing...
2017: Pacific Symposium on Biocomputing
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