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Identifying promoter features of co-regulated genes with similar network motifs

BMC bioinformatics 2009年第s4期10卷 S1-S1页

作者： Harari, Oscar del Val, Coral Romero-Zaliz, Rocio Shin, Dongwoo Huang, Henry Groisman, Eduardo A. Zwir, Igor Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Sungkyunkwan Univ Sch Med Samsung Biomed Res Inst Dept Mol Cell Biol Suwon 440746 South Korea Washington Univ Sch Med Dept Mol Microbiol Howard Hughes Med Inst St Louis MO 63110 USA

Background: A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined. Results: We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern;binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites;the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif. Conclusion: Global transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regul

关键词： Intergenic Region Transcription Factor Binding Site Fuzzy Membership Function Network Motif Promoter Feature

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MQML: Multi-Omic Quantum Machine Learning Based Cancer Classification, Biomarker Identification in Human Lung Adenocarcinoma

MQML: Multi-Omic Quantum Machine Learning Based Cancer Class...

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Quantum Computing and Engineering (QCE), ieee International conference on

作者： Mandeep Kaur Saggi Sabre Kais Department of Chemistry Purdue University West Lafayette IN USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182

关键词： Training Quantum computing Lungs Transcriptomics Lung cancer Machine learning Feature extraction Encoding bioinformatics Tumors

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Automatic Metadata Generation for Fish Specimen Image Collections 21

Automatic Metadata Generation for Fish Specimen Image Collec...

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Proceedings of the 2021 ACM/ieee Joint conference on Digital Libraries

作者： Joel Pepper Jane Greenberg Yasin Bakiş Xiaojun Wang Henry Bart, Jr. David Breen Department of Computer Science Drexel University Philadelphia PA USA Department of Information Science Drexel University Philadelphia PA USA Biodiversity Research Institute Tulane University New Orleans LA USA

ISBN: (纸本)9781665417709

Metadata are key descriptors of research data, particularly for researchers seeking to apply machine learning (ML) to the vast collections of digitized specimens. Unfortunately, the available metadata is often sparse and, at times, erroneous. Additionally, it is prohibitively expensive to address these limitations through traditional, manual means. This paper reports on research that applies machine-driven approaches to analyzing digitized fish images and extracting various important features from them. The digitized fish specimens are being analyzed as part of the biology Guided Neural Networks (BGNN) initiative, which is developing a novel class of artificial neural networks using phylogenies and anatomy ontologies. Automatically generated metadata is crucial for identifying the high-quality images needed for the neural network's predictive analytics. Methods that combine ML and image informatics techniques allow us to rapidly enrich the existing metadata associated with the 7,244 images from the Illinois Natural History Survey (INHS) used in our study. Results show we can accurately generate many key metadata properties relevant to the BGNN project, as well as general image quality metrics (e.g. brightness and contrast). Results also show that we can accurately generate bounding boxes and segmentation masks for fish, which are needed for subsequent machine learning analyses. The automatic process outperforms humans in terms of time and accuracy, and provides a novel solution for leveraging digitized specimens in ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories worldwide.

关键词： bioinformatics

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Preface

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in bioinformatics) 2006年 3939 LNBI卷

作者： Priami, Corrado The Microsoft Research University of Trento Centre for Computational and Systems Biology Piazza Manci 17 PovoTN38050 Italy

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Correction to "Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data"

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ieee/ACM Transactions on computational biology and bioinformatics 2007年第1期4卷 157-157页

作者： Wai-ho Au Keith C.c. Chan Andrew K.c. Wong Yang Wang One Microsoft Way Microsoft Corporation Redmond WA Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Department of Systems Design Engineering University of Waterloo Waterloo Ontario Canada Pattern Discovery Software Systems Ltd. Waterloo Ontario Canada

This is a correction to a typographical error in (11) in [1] which present the calculation of the sum of the multiple significant interdependence redundancy measure. Equation (11) in [1] should be: $$k=\arg\max\nolimits_{k\in\{2,\ldots,p\}}\sum_{r=1}^k \sum_{A_i\in\{C_r-\eta_r\}}R(A_i:\eta_r).$$(11)We remark that the experimental results reported in [1] are based on (11) above not (11) in [1].

关键词： Gene expression Gold biology computing Machine intelligence bioinformatics Error correction Redundancy Equations computational biology Design engineering

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Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

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bioinformatics 2010年第18期26卷 i645-i652页

作者： Qi, Yanjun Tastan, Oznur Carbonell, Jaime G. Klein-Seetharaman, Judith Weston, Jason NEC Labs Amer Princeton NJ 08540 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Google Res NY New York NY 10011 USA

Motivation: Protein-protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins ( labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction ( partially labeled). Results: We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives);(ii) ranking (to rate partial positive more likely than negatives);(iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information.

关键词： Algorithms Artificial intelligence computational biology/methods Data Interpretation, Statistical HIV-1/physiology Human Immunodeficiency Virus Proteins/metabolism Humans Models, Statistical Protein Interaction Mapping/methods Proteins/metabolism

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Learning biophysically-motivated parameters for alpha helix prediction

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BMC bioinformatics 2007年第s5期8卷 S3-S3页

作者： Gassend, Blaise O'Donnell, Charles W. Thies, William Lee, Andrew van Dijk, Marten Devadas, Srinivas MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudoenergies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q a value of 77.6% and an SOV a value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusion: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.

关键词： Support Vector Machine Hide Markov Model Protein Data Bank Finite State Machine Alpha Helix

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A probabilistic model for mining implicit 'chemical compound-gene' relations from literature

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bioinformatics 2005年第Sup2期21卷 245-251页

作者： Zhu, SF Okuno, Y Tsujimoto, G Mamitsuka, H Kyoto Univ Bioinformat Ctr Chem Res Inst Uji Kyoto 6110011 Japan Kyoto Univ Grad Sch Pharmaceut Sci Sakyo Ku Kyoto 6068501 Japan

Motivation: The importance of chemical compounds has been emphasized more in molecular biology, and 'chemical genomics' has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit 'chemical compound and gene' relations from the co-occurrence in the literature. Results: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound-gene, gene-gene and compound-compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound-compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound-gene (more specifically, drug-gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.

关键词： Artificial intelligence Biopolymers/chemistry Biopolymers/metabolism Computer Simulation Genes Information Storage and Retrieval/methods Models, Statistical Natural Language Processing Periodicals as Topic Structure-Activity Relationship

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Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo

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bioinformatics 2006年第14期22卷 E417-E423页

作者： Qi, Yuan Missiuro, Patrycja E. Kapoor, Ashish Hunter, Craig P. Jaakkola, Tommi S. Gifford, David K. Ge, Hui MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Cambridge Ctr 9 Whitehead Inst Cambridge MA 02142 USA Microsoft Res Redmond WA 98052 USA Harvard Univ Dept Mol & Cellular Biol Cambridge MA 02138 USA

Motivation: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. Results: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments.

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A maximum common substructure-based algorithm for searching and predicting drug-like compounds

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bioinformatics 2008年第13期24卷 I366-I374页

作者： Cao, Yiqun Jiang, Tao Girke, Thomas Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Univ Calif Riverside Dept Bot & Plant Sci Riverside CA 92521 USA

Motivation: The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics. Many computational methods in this area focus on measuring the structural similarities between chemical structures. However, traditional similarity measures are often too rigid or consider only global similarities between structures. The maximum common substructure (MCS) approach provides a more promising and flexible alternative for predicting bioactive compounds. Results: In this article, a new backtracking algorithm for MCS is proposed and compared to global similarity measurements. Our algorithm provides high flexibility in the matching process, and it is very efficient in identifying local structural similarities. To predict and cluster biologically active compounds more efficiently, the concept of basis compounds is proposed that enables researchers to easily combine the MCS-based and traditional similarity measures with modern machine learning techniques. Support vector machines (SVMs) are used to test how the MCS-based similarity measure and the basis compound vectorization method perform on two empirically tested datasets. The test results show that MCS complements the well-known atom pair descriptor-based similarity measure. By combining these two measures, our SVM-based model predicts the biological activities of chemical compounds with higher specificity and sensitivity.

关键词： Algorithms Artificial intelligence Drug Design Pattern Recognition, Automated/methods Pharmaceutical Preparations/chemistry

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