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# IEEE/ACM Transactions on Computational Biology and Bioinformatics

## Issue 1 • Jan.-Feb. 1 2018

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## Filter Results

Displaying Results 1 - 25 of 33
• ### A Graphical Model of Smoking-Induced Global Instability in Lung Cancer

Publication Year: 2018, Page(s):1 - 14
| | PDF (4747 KB) | HTML

Smoking is the major cause of lung cancer and the leading cause of cancer-related death in the world. The most current view about lung cancer is no longer limited to individual genes being mutated by any carcinogenic insults from smoking. Instead, tumorigenesis is a phenotype conferred by many systematic and global alterations, leading to extensive heterogeneity and variation for both the genotype... View full abstract»

• ### Algorithms for the Majority Rule (+) Consensus Tree and the Frequency Difference Consensus Tree

Publication Year: 2018, Page(s):15 - 26
| | PDF (1327 KB)

This article presents two new deterministic algorithms for constructing consensus trees. Given an input of $k$  phylogenetic trees with identical leaf label sets and $n$ ... View full abstract»

• ### Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Publication Year: 2018, Page(s):27 - 37
| | PDF (587 KB) |  Media

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approa... View full abstract»

• ### Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm

Publication Year: 2018, Page(s):38 - 45
| | PDF (509 KB)

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines cor... View full abstract»

• ### Bi-level and Bi-objective p-Median Type Problems for Integrative Clustering: Application to Analysis of Cancer Gene-Expression and Drug-Response Data

Publication Year: 2018, Page(s):46 - 59
| | PDF (936 KB) | HTML Media

Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the mo... View full abstract»

• ### Calculating the Expected Time to Eradicate HIV-1 Using a Markov Chain

Publication Year: 2018, Page(s):60 - 67
| | PDF (291 KB) |  Media

In this study, the expected time required to eradicate HIV-1 completely was found as the conditional absorbing time in a finite state space continuous-time Markov chain model. The Markov chain has two absorbing states: one corresponds to HIV eradication and another representing the possible disaster. This method allowed us to calculate the expected eradication time by solving systems of linear equ... View full abstract»

• ### Classification of State Trajectories in Gene Regulatory Networks

Publication Year: 2018, Page(s):68 - 82
Cited by:  Papers (2)
| | PDF (876 KB) | HTML

Gene-expression-based phenotype classification is used for disease diagnosis and prognosis relating to treatment strategies. The present paper considers classification based on sequential measurements of multiple genes using gene regulatory network (GRN) modeling. There are two networks, original and mutated, and observations consist of trajectories of network states. The problem is to classify an... View full abstract»

• ### Combinatorics of Tandem Duplication Random Loss Mutations on Circular Genomes

Publication Year: 2018, Page(s):83 - 95
| | PDF (960 KB) |  Media

The tandem duplication random loss operation (TDRL) is an important genome rearrangement operation in metazoan mitochondrial genomes. A TDRL consists of a duplication of a contiguous set of genes in tandem followed by a random loss of one copy of each duplicated gene. This paper presents an analysis of the combinatorics of TDRLs on circular genomes, e.g., the mitochondrial genome. In particular, r... View full abstract»

• ### Complexity and Algorithms for Finding a Perfect Phylogeny from Mixed Tumor Samples

Publication Year: 2018, Page(s):96 - 108
| | PDF (749 KB) | HTML

Hajirasouliha and Raphael (WABI 2014) proposed a model for deconvoluting mixed tumor samples measured from a collection of high-throughput sequencing reads. This is related to understanding tumor evolution and critical cancer mutations. In short, their formulation asks to split each row of a binary matrix so that the resulting matrix corresponds to a perfect phylogeny and has the minimum number of... View full abstract»

• ### Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information

Publication Year: 2018, Page(s):109 - 116
Cited by:  Papers (1)
| | PDF (496 KB) |  Media

The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topologica... View full abstract»

• ### Efficient Algorithms for Sequence Analysis with Entropic Profiles

Publication Year: 2018, Page(s):117 - 128
| | PDF (4583 KB) | HTML

Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algori... View full abstract»

• ### GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity

Publication Year: 2018, Page(s):129 - 146
| | PDF (1150 KB)

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes... View full abstract»

• ### HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme

Publication Year: 2018, Page(s):147 - 156
| | PDF (1178 KB) | HTML Media

Heme is an essential biomolecule that widely exists in numerous extant organisms. Accurately identifying heme binding residues (HEMEs) is of great importance in disease progression and drug development. In this study, a novel predictor named HEMEsPred was proposed for predicting HEMEs. First, several sequence- and structure-based features, including amino acid composition, motifs, surface preferen... View full abstract»

• ### Inferring the Functions of Proteins from the Interrelationships between Functional Categories

Publication Year: 2018, Page(s):157 - 167
| | PDF (928 KB) | HTML

This study proposes a new method to determine the functions of an unannotated protein. The proteins and amino acid residues mentioned in biomedical texts associated with an unannotated protein $p$ can be considered as characteristics terms for View full abstract»

• ### Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data

Publication Year: 2018, Page(s):168 - 180
| | PDF (939 KB)

Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inf... View full abstract»

• ### Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction

Publication Year: 2018, Page(s):181 - 190
| | PDF (1540 KB) | HTML

The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional m... View full abstract»

• ### Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding

Publication Year: 2018, Page(s):191 - 204
| | PDF (943 KB)

This study presents a machine learning method that increases the number of identified bases in Sanger Sequencing. The system post-processes a KB basecalled chromatogram. It selects a recoverable subset of N-labels in the KB-called chromatogram to replace with basecalls (A,C,G,T). An N-label correction is defined given an additional read of the same sequence, and a human finished sequence. Correcti... View full abstract»

• ### Nonbinary Tree-Based Phylogenetic Networks

Publication Year: 2018, Page(s):205 - 217
| | PDF (1427 KB) | HTML

Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. H... View full abstract»

• ### Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error

Publication Year: 2018, Page(s):218 - 230
Cited by:  Papers (2)
| | PDF (573 KB) | HTML

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajecto... View full abstract»

• ### Petri Net Siphon Analysis and Graph Theoretic Measures for Identifying Combination Therapies in Cancer

Publication Year: 2018, Page(s):231 - 243
| | PDF (724 KB) | HTML Media

Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-MAPK pathway is implicated in the development and progression of cancer and is a major focus of targeted combination therapies. Physiochemical models have been used for identifying and testing the signal-inhibiting potential of targeted therapies; however, their application to larger multi-pathway networks is limited by the availability ... View full abstract»

• ### RAFP-Pred: Robust Prediction of Antifreeze Proteins Using Localized Analysis of n-Peptide Compositions

Publication Year: 2018, Page(s):244 - 250
| | PDF (399 KB) | HTML

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction of the AFPs is considered to be a challenging task. In this research, we propose to handle this arduous manifold learning task using the notion of loc... View full abstract»

• ### Random Sets of Stadiums in Square and Collective Behavior of Bacteria

Publication Year: 2018, Page(s):251 - 256
| | PDF (932 KB) | HTML

Collective motion of swimmers can be detected by hydrodynamic interactions through the effective (macroscopic) viscosity. It follows from the general hydrodynamics that the effective viscosity of non-dilute random suspensions depends on the shape of particles and of their spacial probabilistic distribution. Therefore, a comparative analysis of disordered and collectively interacting particles of t... View full abstract»

• ### Region Growing for Segmenting Green Microalgae Images

Publication Year: 2018, Page(s):257 - 270
| | PDF (1505 KB) | HTML

We describe a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae. The goal is to obtain representative algae shapes to extract morphological features to be employed in a posterior step of taxonomical classification of the species. The proposed methodology relies on the seeded region growing principle and on a fine-tuned filtering preprocessing stage ... View full abstract»

• ### Sampled-Data Stabilization for Fuzzy Genetic Regulatory Networks with Leakage Delays

Publication Year: 2018, Page(s):271 - 285
Cited by:  Papers (2)
| | PDF (412 KB) | HTML

This paper deals with the sampled-data stabilization problem for Takagi-Sugeno (T-S) fuzzy genetic regulatory networks with leakage delays. A novel Lyapunov-Krasovskii functional (LKF) is established by the non-uniform division of the delay intervals with triplex and quadruplex integral terms. Using such LKFs for constant and time-varying delay cases, new stability conditions are obtained in the T... View full abstract»

• ### Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images

Publication Year: 2018, Page(s):286 - 299
| | PDF (1578 KB) | HTML Media

Computing similarity or dissimilarity between protein structures is an important task in structural biology. A conventional method to compute protein structure dissimilarity requires structural alignment of the proteins. However, defining one best alignment is difficult, especially when the structures are very different. In this paper, we propose a new similarity measure for protein structure comp... View full abstract»

## Aims & Scope

This bimonthly publishes archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Aidong Zhang, PhD, IEEE Fellow
Dept. of Computer Science and Engineering
State University of New York at Buffalo
Buffalo, New York 14260 USA
azhang@buffalo.edu

Associate Editor-in-Chief
Dong Xu
University of Missouri
xudong@missouri.edu