A Method to Identify and Analyze Biological Programs through Automated Reasoning.
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ABSTRACT:
SUBMITTER: Yordanov B
PROVIDER: S-EPMC5034891 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWith the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.MethodologyIn this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.Conclusion/significanceTo evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.
| S-EPMC3402456 | biostudies-literature
Project description:BackgroundHigh quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.ResultsWe developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented.ConclusionsGROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.
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Project description:MotivationThe 3D structures of RNA play a critical role in understanding their functionalities. There exist several computational methods to study RNA 3D structures by identifying structural motifs and categorizing them into several motif families based on their structures. Although the number of such motif families is not limited, a few of them are well-studied. Out of these structural motif families, there exist several families that are visually similar or very close in structure, even with different base interactions. Alternatively, some motif families share a set of base interactions but maintain variation in their 3D formations. These similarities among different motif families, if known, can provide a better insight into the RNA 3D structural motifs as well as their characteristic functions in cell biology.ResultsIn this work, we proposed a method, RNAMotifComp, that analyzes the instances of well-known structural motif families and establishes a relational graph among them. We also have designed a method to visualize the relational graph where the families are shown as nodes and their similarity information is represented as edges. We validated our discovered correlations of the motif families using RNAMotifContrast. Additionally, we used a basic Naïve Bayes classifier to show the importance of RNAMotifComp. The relational analysis explains the functional analogies of divergent motif families and illustrates the situations where the motifs of disparate families are predicted to be of the same family.Availability and implementationSource code publicly available at https://github.com/ucfcbb/RNAMotifFamilySimilarity.
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Project description:Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset.
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Project description:Setup and optimisation of a high throughput pipeline for ChIPseq. The protocol used is ChIP carried out using the Agilent Bravo robot with subsequent Ilumina sequencing library preparation.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
2013-02-12 | E-ERAD-77 | biostudies-arrayexpress
Project description:In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools. In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results.
| S-EPMC2878845 | biostudies-literature
Project description:IntroductionFibrocytes are emerging myeloid-derived circulating cells that can migrate into damaged tissues and usually contribute to their repair. Key features of fibrocytes include the expression myeloid markers, production of extracellular matrix proteins, and secretion of various humoral factors that activate resident fibroblasts; they also have the potential to differentiate into fibroblasts. However, no specific surface markers have been identified to identify fibrocytes in vivo. One reason could be that the method used to detect fibrocytes requires intracellular collagen staining.MethodsIn the present study, to establish an improved method for the detection of lung fibrocytes and to analyze viable fibrocytes, we used collagen I(α)2-green fluorescent protein (Col-GFP) reporter mice, which had undergone the intratracheal instillation of bleomycin (BLM).ResultsUsing flow cytometry to gate out cells with autofluorescence, we clearly found that CD45+ GFP+ cells resided in the lungs of Col-GFP mice at a steady state and these cells increased after BLM injury, peaking at Day 14. These cells expressed not only known cell surface markers of fibrocytes, but also some novel markers, in addition to a low level of collagen I in comparison to CD45- GFP+ cells.ConclusionOur findings suggest that the improved method can be a useful for the detection of pure lung fibrocytes and allows us to further analyze the characteristics of viable fibrocytes.
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Project description:Community curation-harnessing community intelligence in knowledge curation, bears great promise in dealing with the flood of biological knowledge. To exploit the full potential of the scientific community for knowledge curation, multiple biological wikis (bio-wikis) have been built to date. However, none of them have achieved a substantial impact on knowledge curation. One of the major limitations in bio-wikis is insufficient community participation, which is intrinsically because of lack of explicit authorship and thus no credit for community curation. To increase community curation in bio-wikis, here we develop AuthorReward, an extension to MediaWiki, to reward community-curated efforts in knowledge curation. AuthorReward quantifies researchers' contributions by properly factoring both edit quantity and quality and yields automated explicit authorship according to their quantitative contributions. AuthorReward provides bio-wikis with an authorship metric, helpful to increase community participation in bio-wikis and to achieve community curation of massive biological knowledge.http://cbb.big.ac.cn/software.zhangzhang@big.ac.cnSupplementary data are available at Bioinformatics online.
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Project description:Stem-cell research. Euthanasia. Personhood. Marriage equality. School shootings. Gun control. Death penalty. Ethical dilemmas regularly spark fierce debate about the underlying moral fabric of societies. How do we prepare today's children to be fully informed and thoughtful citizens, capable of moral and ethical decisions? Current approaches to moral education are controversial, requiring adults to serve as either direct ('top-down') or indirect ('bottom-up') conduits of information about morality. A common thread weaving throughout these two educational initiatives is the ability to take multiple perspectives - increases in perspective taking ability have been found to precede advances in moral reasoning. We propose gesture as a behavior uniquely situated to augment perspective taking ability. Requiring gesture during spatial tasks has been shown to catalyze the production of more sophisticated problem-solving strategies, allowing children to profit from instruction. Our data demonstrate that requiring gesture during moral reasoning tasks has similar effects, resulting in increased perspective taking ability subsequent to instruction. A video abstract of this article can be viewed at http://www.youtube.com/watch?v/gAcRIClU_GY.
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Project description:Multiple reports highlight the increasingly quantitative nature of biological research and the need to innovate means to ensure that students acquire quantitative skills. We present a tool to support such innovation. The Biological Science Quantitative Reasoning Exam (BioSQuaRE) is an assessment instrument designed to measure the quantitative skills of undergraduate students within a biological context. The instrument was developed by an interdisciplinary team of educators and aligns with skills included in national reports such as BIO2010, Scientific Foundations for Future Physicians, and Vision and Change Undergraduate biology educators also confirmed the importance of items included in the instrument. The current version of the BioSQuaRE was developed through an iterative process using data from students at 12 postsecondary institutions. A psychometric analysis of these data provides multiple lines of evidence for the validity of inferences made using the instrument. Our results suggest that the BioSQuaRE will prove useful to faculty and departments interested in helping students acquire the quantitative competencies they need to successfully pursue biology, and useful to biology students by communicating the importance of quantitative skills. We invite educators to use the BioSQuaRE at their own institutions.
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