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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Jean Louis Raisaro, Juan Troncoso-Pastoriza, Mickael Misbach, Joao Sa Sousa, Sylvain Pradervand, Edoardo Missiaglia, Olivier Michielin, Bryan Ford, Jean-Pierre Hubaux
The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns...
July 13, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Atsuko Miyawaki-Kuwakado, Soichiro Komori, Fumihide Shiraishi
The calculation of steady-state metabolite concentrations in metabolic reaction network models is the first step in the sensitivity analy-sis of a metabolic reaction system described by differential equations. However, this calculation becomes very difficult when the number of differential equations is more than 100. In the present study, therefore, we investigated a calculation procedure for obtaining true steady-state metabolite concentrations both efficiently and accurately even in large-scale network models...
July 11, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Donghe Li, Wonji Kim, Longfei Wang, Kyong-Ah Yoon, Boyoung Park, Charny Park, Sun-Young Kong, Yongdeuk Hwang, Daehyun Baek, Eun Sook Lee, Sungho Won
Insertions and deletions (INDELs) comprise a significant proportion of human genetic variation, and recent papers have revealed that many human diseases may be attributable to INDELs. With the development of next-generation sequencing (NGS) technology, many statistical/computational tools have been developed for calling INDELs. However, there are differences among those tools, and comparisons among them have been limited. In order to better understand these inter-tool differences, five popular and publicly available INDEL calling tools - GATK HaplotypeCaller, Platypus, VarScan2, Scalpel, and GotCloud - were evaluated using simulation data, 1000 Genomes Project data, and family-based sequencing data...
July 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jake Weidman, William Aurite, Jens Grossklags
Genetics and genetic data have been the subject of recent scholarly work, with significant attention paid towards understanding consent practices for the acquisition and usage of genetic data as well as genetic data security. Attitudes and perceptions concerning the trustworthiness of governmental institutions receiving test-taker data have been explored, with varied findings, but no robust models or deterministic relationships have been established that account for these differences. These results also do not explore in detail the perceptions regarding other types of organizations (e...
July 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jean Louis Raisaro, Gwangbae Choi, Sylvain Pradervand, Raphael Colsenet, Nathalie Jacquemont, Nicolas Rosat, Vincent Mooser, Jean-Pierre Hubaux
Re-use of patients' health records can provide tremendous benefits for clinical research. Yet, when researchers need to access sensitive/identifying data, such as genomic data, in order to compile cohorts of well-characterized patients for specific studies, privacy and security concerns represent major obstacles that make such a procedure extremely difficult if not impossible. In this paper, we address the challenge of designing and deploying in a real operational setting an efficient privacy-preserving explorer for genetic cohorts...
July 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Syed Sazzad Ahmed, Swarup Roy, Jugal K Kalita
Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference...
July 6, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Laraib Malik, Rob Patro
Recent studies involving the 3-dimensional conformation of chromatin have revealed the important role it has to play in different processes within the cell. These studies have also led to the discovery of densely interacting segments of the chromosome, called topologically associating domains. The accurate identification of these domains from Hi-C interaction data is an interesting and important computational problem for which numerous methods have been proposed. Unfortunately, most existing algorithms designed to identify these domains assume that they are non-overlapping whereas there is substantial evidence to believe a nested structure exists...
June 28, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hao Jiang, Yushan Qiu, Wenpin Hou, Xiaoqing Cheng, Manyi Yim, Wai-Ki Ching
The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy...
June 28, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks...
June 27, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Chengsheng Mao, Yuan Zhao, Mengxin Sun, Yuan Luo
Privacy is a major concern in sharing human subject data to researchers for secondary analyses. A simple binary consent (opt-in or not) may significantly reduce the amount of sharable data, since many patients might only be concerned about a few sensitive medical conditions rather than the entire medical records. We propose event-level privacy protection, and develope a feature ablation method to protect event-level privacy in electronic medical records. Using a list of 13 sensitive diagnoses, we evaluate the feasibility and the efficacy of the proposed method...
June 25, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI) and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping...
June 25, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Meshari Alazmi, Ahmed Abbas, Xianrong Guo, Ming Fan, Lihua Li, Xin Gao
Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, 1H-detected experiments are the dominant NMR technique used due to the high sensitivity of 1H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of 13C nuclei. Consequently, 13C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins...
June 25, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yicheng He, Junfeng Liu, Xia Ning
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this manuscript, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1). the ranking positions of sensitive drugs and 2). the ranking orders among sensitive drugs in cancer cell lines based on their responses to cancer drugs. We have developed a new learning-to-rank method, denoted as pLETORg, that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors...
June 25, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ye Liu, Michael K Ng, Stephen Wu
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding. In many problems, different domains may have different cluster structures. Due to growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering...
June 25, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiangtao Li, Shixiong Zhang, Ka-Chun Wong
In recent years, the detection of epistatic interactions of multiple genetic variants on the causes of complex diseases brings a significant challenge in genome-wide association studies (GWAS). However, most of the existing methods still suffer from algorithmic limitations such as single-objective optimization, intensive computational requirement, and premature convergence. In this paper, we propose and formulate an epistatic interaction multi-objective artificial bee colony algorithm based on decomposition (EIMOABC/D) to address those problems for genetic interaction detection in genome-wide association studies...
June 22, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jordan Haack, Eli Zupke, Andrew Ramirez, Yi-Chieh Wu, Ran Libeskind-Hadas
Phylogenetic tree reconciliation is widely used in the fields of molecular evolution, cophylogenetics, parasitology, and biogeography to study the evolutionary histories of pairs of entities. In these contexts, reconciliation is often performed using maximum parsimony under the DTL (Duplication-Transfer-Loss) event model. In general, the number of maximum parsimony reconciliations (MPRs) can grow exponentially with the size of the trees. While a number of previous efforts have been made to count the number of MPRs, find representative MPRs, and compute the frequencies of events across the space of MPRs, little is known about the structure of MPR space...
June 22, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jiulun Cai, Hongmin Cai, JIazhou Chen, Xi Yang
Identifying gene-drug patterns is a critical step in pharmacology for unveiling disease mechanisms and drug discovery. The availability of high-throughput technologies accumulates massive large-scale pharmacological and genomic data, and thus provides a new substantial opportunity to deeply understand how the oncogenic genes and the therapeutic drugs relate to each other. However, most previous studies merely used the pharmacological and genomic datasets without any prior knowledge to infer the gene-drug patterns...
June 22, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ramon Delgado, Zhiyong Chen, Richard H Middleton
This paper focuses on constructing genotypic predictors for antiretroviral drug susceptibility of HIV. To this end, a method to recover the largest elements of an unknown vector in a least squares problem is developed. The proposed method introduces two novel ideas. The first idea is a novel forward stepwise selection procedure based on the magnitude of the estimates of the candidate variables. To implement this newly introduced procedure, we revise Tikhonov regularisation from a sparse representations' perspective...
June 21, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Sudipta Acharya, Sriparna Saha, Prasanna Pradhan
To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component and Molecular-function. It has several applications in the field of bioinformatics like annotating/measuring gene-gene or protein-protein semantic similarity, identifying genes/proteins by their GO annotations for disease gene and target discovery etc. To determine semantic similarity between genes, several semantic measures have been proposed in literature, which involve information content of GO-terms, GO tree structure or the combination of both...
June 21, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jing Wei Tan, Siow-Wee Chang, Sameem Binti Abdul Kareem, Hwa Jen Yap, Kien-Thai Yong
Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbour (k-NN), Naïve-Bayes (NB) and CNN...
June 19, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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