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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Gaddi Blumrosen Yonatan Wexler Doron Shkolnik Alex Golberg Faculty of engineering Bar-Ilan university Ramat-Gan Israel Faculty of life sciences Tel-aviv university Tel aviv Israel Plant Sciences and Genetics in Agriculture Hebrew university Rehovot Israel Faculty of Exact Sciences School of Environment and Earth Sciences Tel aviv Israel

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

Plant behavior and response to environmental stimuli has tremendous importance in science and agriculture. In particular, a plant's root continuously senses changes in the environment, and responds in ways that optimize dynamically different essential parameters like its stability, and adequate food and water supplies. Some of the plant behavioral changes in response to environmental changes, like water shortage, can be reversible, and after certain “stress” time, the plant can get back to its normal behavioral patterns. In other cases, the plant behavior after the stress stimulus ends, is changed, due to effects on internal mechanisms, facilitating long-term behavioral changes. The main aim of this work is to derive a preliminary physical model and analysis tools to quantify the behavioral changes of a plant in response to a stimuli. To demonstrate the model, we examined, without loss of generality, the change in plant growth rate in response to electrical simuli. We showed how the suggested plant behavioral model can assist in computational analysis of short and long term plant response to changing stimuli, construct a common baseline for comparison with other stimuli, and derive new quantitative measurements that can be correlated with internal plant mechanism and assist in assessing behavioral plant patterns and in the design of more efficient agricultural technologies.

关键词： Analytical models Plants (biology) computational modeling Conferences Tools Stability analysis Stress

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Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第5期16卷 1753-1759页

作者： Zhao, Zhenni Gong, Xinqi Renmin Univ China Inst Math Sci Math Intelligence Applicat LAB 59 Zhongguancun St Beijing 100872 Peoples R China

Motivation: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interaction between two monomer proteins. The correct prediction of interface residue pairs from two monomer proteins is still an open question and has great significance for practical experimental applications in the life sciences. We hope to build a method for the prediction of interface residue pairs that is suitable for those applications. Results: Here, we developed a novel deep network architecture called the multi-layered Long-Short Term Memory networks (LSTMs) approach for the prediction of protein interface residue pairs. First, we created three new descriptions and used other six worked characterizations to describe an amino acid, then we employed these features to discriminate between interface residue pairs and non-interface residue pairs. Second, we used two thresholds to select residue pairs that are more likely to be interface residue pairs. Furthermore, this step increases the proportion of interface residue pairs and reduces the influence of imbalanced data. Third, we built deep network architectures based on Long-Short Term Memory networks algorithm to organize and refine the prediction of interface residue pairs by employing features mentioned above. We trained the deep networks on dimers in the unbound state in the international Protein-protein Docking Benchmark version 3.0. The updated data sets in the versions 4.0 and 5.0 were used as the validation set and test set respectively. For our best model, the accuracy rate was over 62 percent when we chose the top 0.2 percent pairs of every dimer in the test set as predictions, which will be very helpful for the understanding of protein-protein interaction mechanisms and for guidance in biological experiments.

关键词： Proteins Computer architecture Recurrent neural networks Logic gates Machine learning Benchmark testing Deep network architecture interface residue pair prediction long-short term memory networks (LSTMs) protein-protein interaction

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A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第2期16卷 425-433页

作者： Peng, Shaoliang Cui, Yingbo Yang, Shunyun Su, Wenhe Zhang, Xiaoyu Zhang, Tenglilang Liu, Weiguo Zhao, Xing-Ming Hunan Univ Natl Supercomp Ctr Changsha Coll Informat Sci & Engn Changsha 410006 Hunan Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410006 Hunan Peoples R China Shandong Univ Sch Software Jinan 250101 Shandong Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence IS Shanghai 200433 Peoples R China

Molecular Dynamics (MD) is the simulation of the dynamic behavior of atoms and molecules. As the most popular software for molecular dynamics, GROMACS cannot work on large-scale data because of limit computing resources. In this paper, we propose a CPU and Intel (R) Xeon Phi Many Integrated Core (MIC) collaborated parallel framework to accelerate GROMACS using the offload mode on a MIC coprocessor, with which the performance of GROMACS is improved significantly, especially with the utility of Tianhe-2 supercomputer. Furthermore, we optimize GROMACS so that it can run on both the CPU and MIC at the same time. In addition, we accelerate multi-node GROMACS so that it can be used in practice. Benchmarking on real data, our accelerated GROMACS performs very well and reduces computation time significantly. Source code: https://***/tianhe2/gromacs-mic.

关键词： GROMACS molecular dynamics simulation high performance computing parallel framework

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Identification of Cancer Development Related Pathways Based on Co-Expression Analyses

Identification of Cancer Development Related Pathways Based ...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Zhihang Wang Chaoyi Yin Peishuo Sun Ying Xu Huiyan Sun School of Artificial Intelligence Jilin University Changchun Innovation Group of Marine Engineering Materials and Corrosion Control Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China Computational Systems Biology Lab Institute of Bioinformatics University of Georgia Athens USA School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.

关键词： Cancer Correlation Indexes Biological processes Physiology Immune system Genetic expression

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Better Link Prediction for Protein-Protein Interaction Networks

Better Link Prediction for Protein-Protein Interaction Netwo...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Ho Yin Yuen Jesper Jansson The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

In functional genomics, experimentally obtained protein-protein interaction (PPI) data is often incomplete. To deal with this issue, computational approaches are used to infer missing data and to evaluate confidence scores. Link prediction is one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. In a recent study by Kovács et al., a novel PPI-specific link predictor was proposed. Their link predictor is biologically motivated by the so-called L3 principle and it was shown to be superior to other general link predictors when applied to PPI data. However, the L3 link predictor is only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, it may even penalize candidate PPIs that otherwise fit the L3 principle. In this paper, we formulate an L3-based link predictor without approximation, coined ExactL3. We show computationally that ExactL3 is better than the previously proposed methods on four major PPI datasets (STRING, BioGRID, IntAct/HuRI, and MINT). The predicted PPIs are also shown to be much more functionally relevant. This confirms that ExactL3 is a better link predictor for PPI networks, and demonstrates its ability to characterize PPIs by only the topological features of binary PPI networks.

关键词： Proteins Conferences Genomics biology bioinformatics Biomedical engineering

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Optimizing Pattern-Matching for Memory-Efficient Heterogeneous DNA Processing in bioinformatics 12

Optimizing Pattern-Matching for Memory-Efficient Heterogeneo...

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12th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Pungila, Ciprian Galis, Darius Negru, Viorel West Univ Timisoara Fac Math & Informat Timisoara Romania Politehn Univ Timisoara Fac Automat & Comp Timisoara Romania

ISBN: (纸本)9781538646403

We are proposing a new, memory-efficient approach to optimizing DNA pattern-matching in bioinformatics through a heterogeneous implementation and new architectural layout, that poses several advantages over usual approaches, which we discuss in detail. We are applying our approach on a subset of DNA sequences part of the FASTA open database, under different hardware settings, and observe a significant performance increase in our heterogeneous implementation. With a practical reduction of 23 times less memory usage than a classic implementation of the same algorithm, and massive scaling capabilities for high-throughput DNA-matching, our approach proves its feasibility for scalable heterogeneous architectures.

关键词： Automata DNA Graphics processing units Random access memory Memory management bioinformatics Layout"

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Special Section on Swarm-Based Algorithms and Applications in computational biology and bioinformatics

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第6期15卷 1863-1864页

作者： Tan, Ying Shi, Yuhui Peking Univ Sch EECS Dept Machine Intelligence Key Lab Machine PerceptMOE Beijing 100871 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The seven papers in this special section were presented at the ICSI 2016 Conference. These articles are primarily dealing with either novel bioinspired swarm intelligence algorithms and their improvements aswell as some practical applications inmulti-objective optimization, network community detection, curve fitting, and swarm robotics, etc.

关键词： Special Issues And Sections Particle Swarm Optimization Bio Inspired Computing Evolutionary Computation Meetings

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Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural bioinformatics Survey

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ieee ACCESS 2018年 6卷 11760-11771页

作者： Chen, Huaming Guo, William Shen, Jun Wang, Lei Song, Jiangning Univ Wollong Sch Comp & Informat Technol Fac Engn & Informat Sci Wollongong NSW 2522 Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia Monash Univ Dept Biochem & Mol Biol Melbourne Vic 3800 Australia

computational-intelligence methods in bioinformatics and systems biology show promising potential for leveraging abundant, large-scale molecular data. These methods can facilitate analysis and prediction of the principles of biological systems through the construction of statistical and visualized models. Specifically, structural data from exogenous and endogenous protein-protein interactions are of vital significance in this context, encompassing primarily 3-D structural information for a cohort of macromolecules underpinning the biological system. In this paper, we surveyed the main methodologies and algorithms for the reconstruction and modeling of the structural-interaction networks (SINs) of host-pathogen protein-protein interactions (HPPPIs), regarding how the protein domains interact with each other to constitute a SIN. Surveying the pattern and the organization of the SIN delivers a state-of-theart view of HPPPIs and illustrates prospective future research directions. In addition to the binary PPI network, we distilled the relevant data sources into several branching research areas and further expanded the discussions into computational-intelligence methods according to the algorithms applied, including machine learning statistical models, to shed light on effective method design. In particular, atomic resolution level investigations can reveal novel insights into the underlying principles of the organization and the complexity of HPPPIs networks. Combining data analytics and machine-learning technologies, we anticipate that our systematic overview will serve as a useful guide for interested researchers to carry out related studies on this exciting and challenging research topic in system biology.

关键词： Host-pathogen interactions structural-interaction network bioinformatics machine learning data analytics

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Parameter Selection for Modeling of Epidemic Networks

Parameter Selection for Modeling of Epidemic Networks

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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Dube, Michael Houghten, Sheridan Ashlock, Daniel Brock Univ Dept Comp Sci St Catharines ON Canada Univ Guelph Dept Math & Stat Guelph ON Canada

ISBN: (纸本)9781538613993

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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Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset

Evaluating the Predictability of Cancer Types from 536 Somat...

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Annual International Conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Taher Dehkharghanian Shahryar Rahnamayan Hamid R. Tizhoosh Nature-Inspired Computational Intelligence Lab Ontario Tech University Oshawa On Canada Kimia Lab University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

In this paper, we introduce a new dataset for cancer research containing somatic mutation states of 536 genes of the Cancer Gene Census (CGC). We used somatic mutation information from the Cancer Genome Atlas (TCGA) projects to create this dataset. As preliminary investigations, we employed machine learning techniques, including k-Nearest Neighbors, Decision Tree, Random Forest, and Artificial Neural Networks (ANNs) to evaluate the potential of these somatic mutations for classification of cancer types. We compared our models on accuracy, precision, recall, and F1-score. We observed that ANNs outperformed the other models with F1-score of 0.36 and overall classification accuracy of 40%, and precision ranging from 12% to 92% for different cancer types. The 40% accuracy is significantly higher than random guessing which would have resulted in 3% overall classification accuracy. Although the model has relatively low overall accuracy, it has an average classification specificity of 98%. The ANN achieved high precision scores (> 0.7) for 5 of the 33 cancer types. The introduced dataset can be used for research on TCGA data, such as survival analysis, histopathology image analysis and content-based image retrieval. The dataset is available online for download: https://***/kimia/.

关键词： Genomics bioinformatics Genetic mutations Forestry Melanoma Machine learning

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