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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Saad Raza, Ghulam Abbas, Syed Sikander Azam
In-silico pipeline is applied for identifying and designing novel inhibitors against ZIKV NS1 protein. Comparative molecular docking studies are performed to explore the binding of structurally diverse compounds to ZIKV NS1 by AutoDock/Vina and GOLD. The Zika virus (ZIKV) is a flavivirus, responsible for life-threatening infections and transmitted by Aedes mosquitoes in other organisms. It is associated with Guillain Barre Syndrome (GBS) and microcephaly. This epidemic increase in GBS and microcephaly convoyed the World Health Organization to affirm ZIKV a public health crisis...
April 15, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Maryam Shahdoust, Hossein Mahjub, Hamid Pezeshk, Mehdi Sadeghi
Joint graphical lasso(JGL) approach is a Gaussian graphical model to estimate multiple graphical models corresponding to distinct but related groups. Molecular apocrine (MA) breast cancer tumor has similar characteristics to luminal and basal subtypes. Due to the relationship between MA tumor and two other subtypes, this paper investigates the similarities and differences between the MA genes association network and the ones corresponding to other tumors by taking advantageous of JGL properties. Two distinct JGL graphical models are applied to two sub-datasets including the gene expression information of the MA and the luminal tumors and also the MA and the basal tumors...
April 15, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Aisharjya Sarkar, Yilmaz Atay, Alana Lorraine Erickson, Ivan Arisi, Cesare Saltini, Tamer Kahveci
Protein-protein interaction (PPI) network models interconnections between protein-encoding genes. Group of proteins that perform similar functions are often connected to each other in PPI network. The corresponding genes form pathways or functional modules. Mutation in protein-encoding genes affect behavior of pathways. This results in initiation, progression and severity of diseases that propagates through pathways. In this work, we integrate mutation, survival information of patients and PPI network to identify connected subnetworks associated with survival...
April 15, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Sushmita Paul, Madhumita Singh
Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging...
April 15, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Lan Zhao, Hong Yan
In the age of personalized medicine, there is a great need to classify cancer (from the same organ site) into homogeneous subtypes. Recent technology advancements in genome-wide molecular profiling have made it possible to profiling multiple molecular datasets to characterize the genomic changes in various cancer types. How to take full advantage of the availability of these omics data? And how to integrate these molecular data with patient clinical data to do a more systematic subtyping of cancer are the focuses of the paper...
April 11, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Zhen Shen, Su-Ping Deng, De-Shuang Huang
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-protein binding. These methods can hardly meet the need of consideration of the dependencies between subsequence and the various motif lengths of different translation factors (TFs). To overcome such limitations, we propose a predictive model that utilizes a combination of multi-scale convolutional layers and bidirectional gated recurrent unit (GRU) layer...
April 11, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Matthew McDermott, Jennifer Wang, Wen Ning Zhao, Steven D Sheridan, Peter Szolovits, Isaac Kohane, Stephen J Haggarty, Roy H Perlis
Gene expression data can offer deep, physiological insights beyond the static coding of the genome alone. We believe that realizing this potential requires specialized, high-capacity machine learning methods capable of using underlying biological structure, but the development of such models is hampered by the lack of published benchmark tasks and well characterized baselines.
April 11, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yong Liu, Min Wu, Chenghao Liu, Xiaoli Li, Jie Zheng
Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL 2 MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors...
April 9, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Chen Peng, Yang Zheng, De-Shuang Huang
Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD)...
April 9, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Laiyi Fu, Qinke Peng, Ling Chai
DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, there are several sequence-based deep learning models designed to predict DNA methylation states, which gain better performance than traditional methods like random forest and SVM. However, convolutional network based deep learning models that use one-hot encoding DNA sequence as input may discover limited information and cause unsatisfactory prediction performance, so more data and model structures of diverse angles should be considered...
April 3, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keisuke Kawano, Satoshi Koide, Chie Imamura
Engineering stable proteins is crucial to various industrial purposes. Several machine learning methods have been developed to predict changes in the stability of proteins upon single point mutations. To improve accuracy of the prediction, we propose a new unsupervised descriptor for protein sequences that is based on a sequence-to-sequence (seq2seq) neural network model combined with a sequence-compression method called byte-pair encoding (BPE). Our results exhibit that BPE can encode a protein sequence into a sequence of shorter length, thereby enabling efficient training of the seq2seq model...
April 1, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Cheng Yan, Guihua Duan, Fangxiang Wu, Yi Pan, Jianxin Wang
Many current studies have evidenced that microbes play important roles in human diseases. Therefore, discovering the associations between microbes and diseases is beneficial to systematically understanding the mechanisms of diseases, diagnosing and treating complex diseases. It is well known that finding new potential microbe-disease associations via biological experiments is a time-consuming and expensive process. However, the computation methods can provide an opportunity to effectively predict microbe-disease associations...
March 26, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiangxiang Zeng, Yinglai Lin, Yuying He, Linyuan Lv, Xiaoping Min, Alfonso Rodriguez-Paton
Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well established framework and its superior performance in predicting gene-disease associations. However, the IMC method does not hierarchically extract deep features, which might limit the quality of recovery. In this case, the architecture of deep learning, which obtains high-level representations and handles noises and outliers presented in large-scale biological datasets, is introduced into the side information of genes in our Deep Collaborative Filtering (DCF) model...
March 26, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jiri Filipovic, Ondrej Vavra, Jan Plhak, David Bednar, Sergio M Marques, Jan Brezovsky, Ludek Matyska, Jiri Damborsky
Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories...
March 26, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Pourya Naderi Yeganeh, M Taghi Mostafavi
Pathway enrichment analysis models (PEM) are the premier methods for interpreting genetic perturbations from high-throughput experiments. PEM often use a priori background knowledge to infer the underlying biological functions and mechanisms. A shortcoming of standard PEM is their disregarding of genetic interactions for mathematical simplicity, which potentially results in partial and inaccurate inference. In this study, we introduce a graph-based PEM, namely Causal Disturbance Analysis (CADIA), that leverages genetic interactions to quantify the topological importance of perturbations in pathway organizations...
March 25, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiangtao Li, Ka-Chun Wong
In recent years, single-cell RNA sequencing reveals diverse cell genetics at unprecedented resolutions. Such technological advances enable researchers to uncover the functionally distinct cell subtypes such as hematopoietic stem cell subpopulation identification. However, most of the related algorithms have been hindered by the high-dimensionality and sparse nature of single-cell RNA sequencing (RNA-seq) data. To address those problems, we propose a multiobjective evolutionary clustering based on adaptive non-negative matrix factorization (MCANMF) for multiobjective single-cell RNA-seq data clustering...
March 25, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Dongjin Choi, Sael Lee
How do we integratively profile large-scale multi-platform genomic data that are high dimensional and sparse? Furthermore, how can we incorporate prior knowledge, such as the association between genes, in the analysis systematically to find better latent relationships? To solve this problem, we propose a Scalable Network Constrained Tucker decomposition method (SNeCT). SNeCT adopts parallel stochastic gradient descent approach on the proposed parallelizable network constrained optimization function. SNeCT decomposition is applied to a tensor constructed from a large scale multi-platform multi-cohort cancer data, PanCan12, constrained on a network built from PathwayCommons database...
March 19, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Russell Harmer, Yves-Stan Le Cornec, Sebastien Legare, Eugenia Oshurko
The general question of what constitutes bio-curation for rule-based modelling of cellular signalling is posed. A general approach to the problem is presented, based on rewriting in hierarchies of graphs, together with a specific instantiation of the methodology that addresses our particular bio-curation problem. The current state of the ongoing development of the KAMI bio-curation tool, based on this approach, is outlined along with our plans for future development.
March 19, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Fuyan Hu, Yuxuan Zhou, Qing Wang, Zhiyuan Yang, Yu Shi, Qingjia Chi
As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self-organizing map (SOM) neural network, and hierarchical clustering methods to classify LUAD into four subtypes. We determined that 24 differentially expressed genes could be used as therapeutic targets, and five genes (i...
March 18, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ali Ahmed AlMatouq, Taous-Meriem Laleg-Kirati, Carlo Novara, Ivana Rabbone, Tyrone Vincent
A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. The technique assumes knowledge of patient parameters relevant to the glucose, insulin and endogoneous glucose production subsystems. A convex Lasso formulation is used to estimate the glucose fluxes that combines (1) the known patient parameters; (2) a sparse vector space encoding the space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates and (6) an estimate of the initial conditions...
March 15, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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