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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Mohammad Alsulmi Ben Carterette Department of Computer & Information Sciences University of Delaware Newark DE USA

Learning to Rank (LtR) is a new emerging research area in which machine learning techniques are used to solve the problem of ranking search results. In this paper, we consider applying LtR to the tasks performed by physicians in seeking out relevant information for providing better care to their patients. More specifically, we present a general approach for applying learning to rank (LtR), broadly applicable across many different algorithms, to clinical search tasks. Our approach consists of a set of learning features as well as a feature selection method which can help in learning effective models for retrieving relevant biomedical literature. In our experiments, we examine our approach using several state-of-the-art LtR algorithms and show that the proposed LtR ranking can effectively promote search results for the clinical domain resulting in a performance increase up to 28% compared to traditional ranking models.

关键词： Feature extraction Task analysis computational modeling Biological system modeling Medical services Training Medical diagnostic imaging

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Parameter selection for modeling of epidemic networks

Parameter selection for modeling of epidemic networks

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Michael Dube Sheridan Houghten Daniel Ashlock Dept. of Computer Science Brock University St. Catharines ON Canada Dept. of Mathematics and Statistics University of Guelph Guelph ON Canada

The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.

关键词： Evolutionary computation Diseases computational modeling Computer science Sociology Statistics Aerospace electronics

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Predicting Drug-Disease Treatment Associations Based on Topological Similarity and Singular Value Decomposition

Predicting Drug-Disease Treatment Associations Based on Topo...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Guangsheng Wu Juan Liu School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781728118680

To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods need the similarities of drugs and diseases, and they will not work if the biological or chemical features for computing similarities are missing. Besides, being lack of validated negative samples in the drug-disease associations data, most methods simply select some unlabeled samples as negative ones, which may introduce noises. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-protein-disease heterogeneous network, we consider the common neighbors of drugs and diseases to obtain the topological similarity. Then the topological similarity matrix of drugs (diseases) will be used to get the low dimensional embedding representations of drug-disease pairs. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the k-step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods.

关键词： biology computing diseases drugs feature extraction learning (artificial intelligence) medical computing molecular biophysics patient treatment pattern classification proteins singular value decomposition Singular value decomposition biology computing maladjustment medical computing Pharmaceutical Preparations molecular biology patient treatment Pattern recognition Feature extraction Proteins Association (Psychology)

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CCA based multi-view feature selection for multi-omics data integration

CCA based multi-view feature selection for multi-omics data ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Yasser El-Manzalawy College of Information Sciences and Technologies Pennsylvania State University USA

Recent technological advances in high-throughput omics technologies and their applications in genomic medicine have opened up outstanding opportunities for individualized medicine. However, several challenges arise in the integrative analysis of such data including heterogeneity and high dimensionality of the omics data. In this study, we present a novel multi-view feature selection algorithm based on the well- known canonical correlation analysis (CCA) statistical method for jointly selecting discriminative features from multi-omics data sources (multi-views). Our results demonstrate that models for predicting kidney renal clear cell carcinoma (KIRC) survival using our proposed method for jointly selecting discriminative features from copy number alteration (CNA), gene expression RNA-Seq, and reverse-phase protein arrays (RPPA) views outperform models trained using single-view data as well as three integrated models developed using data fusion approaches including CCA-based feature fusion.

关键词： Feature extraction Radio frequency Data models Correlation Machine learning algorithms Data integration bioinformatics

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On the generalizability of linear and non-linear region of interest-based multivariate regression models for fMRI data

On the generalizability of linear and non-linear region of i...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Ethan C. Jackson James Alexander Hughes Mark Daley Computer Science Department University of Western Ontario London Ontario Canada Western University London ON CA

In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two contemporary approaches for multivariate regression on task-based fMRI data: linear regression with ridge regularization and non-linear symbolic regression using genetic programming. The data for this project is representative of a contemporary fMRI experimental design for visual stimuli. Linear and non-linear models were generated for 10 subjects, with another 4 withheld for validation. Model quality is evaluated by comparing R scores (Pearson product-moment correlation) in various contexts, including single run self-fit, within-subject generalization, and between-subject generalization. Propensity for modelling strategies to overfit is estimated using a separate resting state scan. Results suggest that neither method is objectively or inherently better than the other.

关键词： computational modeling Data models Functional magnetic resonance imaging Analytical models Linear regression Time series analysis Task analysis

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Integrating Sequence and Network Information to Enhance Protein-Protein Interaction Prediction Using Graph Convolutional Networks

Integrating Sequence and Network Information to Enhance Prot...

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ieee International Conference on bioinformatics and Biomedicine

作者： Leilei Liu Yi Ma Xianglei Zhu Yaodong Yang Xiaotian Hao Li Wang Jiajie Peng Tianjin University College of Intelligence and Computing Tianjin University/CATARC College of Intelligence and Computing/Automotive Data Center Northwestern Polytechnical University School of Computer Science

ISBN: (纸本)9781728118680

Identification of protein-protein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of large-scale high-throughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the protein's position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the state-of-the-art sequence-based methods on several benchmark datasets and our work computationally is more efficient compared with previous works.

关键词： protein-protein interactions amino acid sequence graph structure information graph convolutional networks Amino Acid Sequence Protein-Protein Interaction Line graph POSITION INFORMATION network information information Proteins

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Introduction to the Special Issue on bioinformatics Research and Applications

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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ieee TRANSACTIONS ON NANOBIOSCIENCE 2017年第2期16卷 79-80页

作者： Skums, Pavel Zelikovsky, Alexander Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

This special issue includes a selection of papers presented at the $12^{th}$ International symposium on bioinformatics Research and Application (ISBRA) , which was held at Belorussian State University in Minsk, Belarus on June 5–8, 2016. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2016, 77 papers were submitted in response to the call for papers, out of which 22 extended abstracts appeared in the ISBRA proceedings published as volume 9683 of Springer Verlag's Lecture Notes in bioinformatics series.

关键词： Special issues and sections Meetings bioinformatics computational biology Sequential analysis

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Atherosclerotic Plaque Growth Prediction in Coronary Arteries using a computational Multi-level Model: The Effect of Diabetes

Atherosclerotic Plaque Growth Prediction in Coronary Arterie...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Dimitris Pleouras Antonis Sakellarios Georgia Karanasiou Savvas Kyriakidis Panagiota Tsompou Vassiliki Kigka Dimitrios I. Fotiadis FORTH-Biomedical Research Institute Ioannina Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece University of Ioannina Ioannina Greece Department of Biomedical Research Dept. of Materials Science and Engineering Institute of Molecular Biology and Biotechnology Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.

关键词： Diabetes Atherosclerosis Glucose Arteries Blood Solid modeling Geometry

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DMIL-III: Isoform-isoform interaction prediction using deep multi-instance learning method

DMIL-III: Isoform-isoform interaction prediction using deep ...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jie Zeng Guoxian Yu Jun Wang Maozu Guo Xiangliang Zhang College of Computer and Information Science Southwest University Chongqing China CEMSE division KAUST Computational Bioscience Research Center Thuwal SA School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781728118680

Alternative splicing modulates protein-protein and other ligand interactions, it results in proteoforms, translated from isoforms that are alternatively spliced from the same gene, to interact with different partners and have distinct or even opposing functions. Therefore, systematically identifying protein-protein interaction at the isoform-level is crucial to explore the function of proteoforms. Constructing the isoform-level interaction network currently is prohibited by the lack of a large golden set of experimentally validated interacting isoforms, which enable computationally predicting isoform-isoform interactions. In this paper, a deep convolution neural network based multi-instance learning approach called DMIL-III is proposed to predict isoform interactions. DMIL-III takes a gene pair as `bag' and two isoforms of the pairwise genes as the `instance' of the bag. DMIL-III follows the principle of multi-instance learning that at least one isoform-isoform interaction exists for a positive gene pair and none interacting isoforms occurs for a negative gene pair. DMIL-III integrates RNA-seq, nucleotide sequence, domain-domain interaction and exon array data. Experimental results indicate that DMIL-III achieves a superior performance with Accuracy of 93% on single-instance gene bags and of 94% on multi-instance gene bags, which are at least 14% and 29% higher than those of state-of-the-art methods. In addition, we further test DMIL-III on a set of experimentally confirmed isoform-isoform interactions and obtain an Accuracy of 65%, which is at least 10% higher than those of comparing methods at the isoform-level. All these results show the effectiveness of DMIL-III for predicting isoform-isoform interactions.

关键词： biochemistry biology computing data analysis genetics learning (artificial intelligence) molecular biophysics neural nets proteins RNA biology computing molecular biology Neural network biochemistry Data analysis PURSE Genetics Protein Isoforms RNA Bag device Alternative Splicing Protein-Protein Interaction Proteins Genes Statistical analysis

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Cross-validation and cross-study validation of kidney cancer with machine learning and whole exome sequences from the National Cancer Institute

Cross-validation and cross-study validation of kidney cancer...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Abdulrhman Aljouie Nihir Patel Usman Roshan Department of Biostatistics and Bioinformatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Department of Genetics and Genomics Sciences Hess Center for Science and Medicine New York City New York Department of Computer Science New Jersey Institute of Technology Newark New Jersey

Accurate cancer risk prediction from genetic and environment variables is a key problem in medicine. One approach is to use somatic mutations which could potentially be used in early detection and prevention. SNP based studies are the most common ones utilizing this approach, however most studies lack a cross-study validation component across at least two independent studies. Here we explore the cross-validation and cross-study validation of predicting kidney cancer case and controls with SNPs obtained from whole exome sequences at the National Cancer Institute. From the Genomics Data Commons portal we obtained aligned whole exome sequences of two different kidney cancer studies: 110 cases and controls of KIRP for renal papillary cell carcinoma and 34 cases and controls of KICH for kidney chromophobe cell carcinoma. We performed a rigorous quality control procedure to obtain SNPs and rank them with feature selection. On top ranked SNPs we find the support vector machine to obtain a cross-validation accuracy of 71% (with 10 SNPs) and 72% (with 20 SNPs) in KIRP and KICH respectively. We then learn a model on KIRP and with 10 SNPs achieve an accuracy of 66% on the KICH samples. Our work shows that we can predict kidney chromophobe carcinoma from a kidney papillary carcinoma dataset with better than a random classification which would have 50% accuracy. In continuing work we are expanding these sample sizes and extending cross- study to other kidney cancer datasets in the NCI GDC portal.

关键词： Cancer Kidney Support vector machines bioinformatics Genomics Correlation Machine learning

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