2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Conference Information
Name: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Location: San Diego, United States
Date: 2019-11-18 - 2019-11-21

Latest articles from this conference

Binh Vu, Yanxin Wu, , Paul Mc Kevitt, Paul Walsh, Felix Engel, Michael Fuchs, Matthias Hemmje
The reduced cost of DNA sequencing allows metagenomics to be applied on a larger scale. With metagenomic analysis, we have better insight into supplement usage, methane production, and feed conversion efficiency in livestock systems. Nevertheless, sequencing machines generate an enormous amount of complex data. Conventional methods used in the analysis of genomic data involve pre-processing and synchronous reconstruction by multiple systems, which is time consuming and prone to failure. Furthermore, the sequencing datasets and analysis results need to be organized and stored properly in order for scientists to search and access them. To tackle these challenges, a new workflow for metagenomic analysis with improved infrastructure is needed. The MetaPlat project supports experts in both academic and non-academic sectors dealing with challenges in the field of metagenomics by focusing on improved hardware and software platforms. High-performance, fault-tolerant, flexible, and scalable processors and analysis systems will help to increase the effectiveness and efficiency of current metagenomics studies. In this paper, we propose such as an infrastructure applying emerging technologies, such as Kafka, Docker, and Hadoop. Details of the infrastructure solution and some preliminary results are also discussed.
Santisudha Panigrahi, Tripti Swarnkar
In latest years, convolutional neural networks (CNNs) have accomplished state-of-the-art performance in many computer vision tasks such as classification of images, object detection, instance segmentation etc. CNN has a robust learning capacity and can enhance the use of datasets for feature extraction. Identifying the key visual features from the oral squamous cells are the significant and compulsory task for the clinicians to detect the different stages of oral cancer. The computer-aided instrument performing the same identifying job would provide clinicians with a vital guidance during diagnosis for evaluating histological images. In this study, we suggest the use of 4-layered (5X5X3) patches of convolutional neural networks (CNNs) for feature extraction and classification from oral cancer images. To prevent overfitting the images were augmented by rotating, inverting and flipping. The proposed model has achieved 96.77 % accuracy, with 10 fold cross validation, which is at par with the accuracy of cytotechnologists and pathologists. Therefore, this model is helpful in classifying oral cancer microscopic images.
Lei Deng, Hui Wu, Hui Liu
Prediction of in-vivo protein-DNA binding is an important, but challenging task in the broad field of computational biology. Although some methods based on deep learning have succeed in modeling in-vivo protein-DNA binding, they often simply extract the sequence features from the original DNA sequence without consideration of other sequence features, such as their reverse, complementary and reverse complementary sequences. Also, one-hot encoding of DNA sequence is vulnerable to the curse of dimensionality, which leads to unwanted equidistance of pairwise sequences. To address these problems, we propose D2VCB (dna2vec, convolution, bi-LSTM), a novel hybrid deep neural network framework using dna2vec to predict in-vivo protein-DNA binding events. We extract input features from DNA original sequences, reverse sequences, complementary and complementary reverse sequences, and then use dna2vec to compute a distributed representation of k-mer. In our D2VCB model, the convolution layer captures motif features, while the recurrent layer captures long-term dependencies among motif features so as to improve prediction accuracy. Our performance comparison experiments show that D2VCB outperforms significantly other existing methods in terms of multiple performance metrics.
Chen Yang, Hui Gao, Xuan Yang, Suiyu Huang, Yulong Kan, Jianxiao Liu
Mining epistatic gene locus which influence complex disease has great research significance. Bayesian network (BN) has been widely used in many researches of epistasis mining. However, Bayesian network methods have disadvantages of being easily trapped into local optimum, low learning efficiency and not being able to handle large-scale network. In this work, we propose an epistasis mining approach based on artificial bee colony algorithm optimizing Bayesian network (BnBeeEpi). We apply artificial bee colony algorithm into the heuristic search strategy of Bayesian network, and then use two kinds of BN scoring functions (BIC and MIT) to calculate the network fitness value to avoid overfitting and reduce false positive rate. Moreover, we introduce decomposable BIC scoring to solve the large-scale network learning problem. Finally, we compare BnBeeEpi with current popular epistasis mining algorithms by using both simulated and real datasets. Experiment results show that omb-Fast has very short running time with its accuracy is as good as other methods, and BnBeeEpi has better F1-score and lower false positive rate compared to others. Availability and implementation: codes and visualization platform are available at:
Wei Wang, Qiqige Wuyun, Kevin J. Liu
Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that input observations are identically and independently distributed (i.i.d.). However, within the area of computational biology and bioinformatics, many different factors can contribute to intra-sequence dependence, such as recombination and other evolutionary processes governing sequence evolution. The SEquential RESampling (“SERES”) framework was previously proposed to relax the simplifying assumption of i.i.d. input observations. SERES resampling takes the form of random walks on an input of either aligned or unaligned biomolecular sequences. This study introduces the first application of SERES random walks on aligned sequence inputs and is also the first to demonstrate the utility of SERES as a data perturbation technique to yield improved statistical estimates. We focus on the classical problem of recombination-aware local genealogical inference. We show in a simulation study that coupling SERES resampling and re-estimation with recHMM, a hidden Markov model-based method, produces local genealogical inferences with consistent and often large improvements in terms of topological accuracy. We further evaluate method performance using an empirical HIV genome sequence dataset.
Carla R. B. Bonin, Marcelo Lobosco, Guilherme C. Fernandes, Reinaldo M. Menezes, Luiz A. B. Camacho, Licia M. H. da Mota, Sheila M. B. de Lima, Ana Carolina Campi-Azevedo, Olindo A. Martins-Filho,
An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents the results of a computational model of the human immune response to vaccination against yellow fever. The model takes into account essential cells and molecules of the human immune system, such as antigen-presenting cells, B and T lymphocytes, memory cells, and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data.
Raihanul Bari Tanvir, Ananda Mohan Mondal
Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.
Haifeng Xu, Jianfei Pang, Xi Yang, Jinghui Yu, Dongsheng Zhao
Modeling clinical activities plays an important role in process mining, which is essential for improving medical quality. Traditional process mining methods focus on control flow of events, ignoring the data perspective including time and resources properties. Besides, clinical experts often have difficulties to classify the event attributes from a computer's point of view for model representation. We present a constraint-based approach with multi-perspective declarative process mining, which supports modeling medical process by clinical staff themselves. The event attributes are classified according to openEHR and the created model could be shared among medical institutions.
Kimia Ameri, Kathryn Cooper
The mechanism for the formation of antibiotic resistance is not clearly understood and has remained challenging due to the evolving nature of the bacterial genome. Staphylococcus aureus is a commensal of the human microbiota found in the upper and occasionally lower respiratory airways and adherent to adnexal regions. The S. aureus is responsible for several acute and chronic illnesses such as osteomyelitis, endocarditis, and infection from implanted devices. It can cause mild to life-threatening infections. In addition to its potential for hostility, S. aureus demonstrates the exceptional diversity of resistance mechanisms against antimicrobial efforts. S. aureus resistance can be attributed to permanent mutations at the genetic level. SANVA (Staphylococcus aureus Network-Based Variant Analysis) is an analytic method based on a network that can analyze the staphylococcal isolates to find the compelling mutations in Protein-Protein Interaction network. These mutated genes might be able to introduce new targets for antimicrobial drugs.
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