Automated extraction of potential migraine biomarkers using a semantic graph

Problem Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers. Method We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance. Results Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974. Discussion Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers

Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations

Large Graph Analysis in the GMine System

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Learning Language from a Large (Unannotated) Corpus

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa