Articles

To know the objective is not (necessarily) to know the objective function

Authors: Moshe Sipper, Ryan J. Urbanowicz and Jason H. Moore

Grasping frequent subgraph mining for bioinformatics applications

Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network struct...

Authors: Aida Mrzic, Pieter Meysman, Wout Bittremieux, Pieter Moris, Boris Cule, Bart Goethals and Kris Laukens

Functional relevance for central cornea thickness-associated genetic variants by using integrative analyses

The genetic architecture underlying central cornea thickness (CCT) is far from understood. Most of the CCT-associated variants are located in the non-coding regions, raising the difficulty of following functio...

Authors: Jing Zhang, Dan Wu, Yiqin Dai and Jianjiang Xu

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases

The redundancy of information is becoming a critical issue for epidemiologists. High-dimensional datasets require new effective variable selection methods to be developed. This study implements an advanced evo...

Authors: Christina Brester, Jussi Kauhanen, Tomi-Pekka Tuomainen, Sari Voutilainen, Mauno Rönkkö, Kimmo Ronkainen, Eugene Semenkin and Mikko Kolehmainen

Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm

The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction ...

Authors: Cheng-Hong Yang, Kuo-Chuan Wu, Yu-Shiun Lin, Li-Yeh Chuang and Hsueh-Wei Chang

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

Biologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information abou...

Authors: Jorge Parraga-Alava, Marcio Dorn and Mario Inostroza-Ponta

TRIQ: a new method to evaluate triclusters

Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of tric...

Authors: David Gutiérrez-Avilés, Raúl Giráldez, Francisco Javier Gil-Cumbreras and Cristina Rubio-Escudero

PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activit...

Authors: Kathleen M. Chen, Jie Tan, Gregory P. Way, Georgia Doing, Deborah A. Hogan and Casey S. Greene

Integrative analysis of gene expression and methylation data for breast cancer cell lines

The deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenet...

Authors: Catherine Li, Juyon Lee, Jessica Ding and Shuying Sun

Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

Human microbiome studies in clinical settings generally focus on distinguishing the microbiota in health from that in disease at a specific point in time. However, microbiome samples may be associated with dis...

Authors: Laura Tipton, Karen T. Cuenco, Laurence Huang, Ruth M. Greenblatt, Eric Kleerup, Frank Sciurba, Steven R. Duncan, Michael P. Donahoe, Alison Morris and Elodie Ghedin

Soft document clustering using a novel graph covering approach

In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organi...

Authors: Jens Dörpinghaus, Sebastian Schaaf and Marc Jacobs

Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi

The Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources (“assays”), such as in vitro bioas...

Authors: Kimberly T. To, Rebecca C. Fry and David M. Reif

Feature selection for gene prediction in metagenomic fragments

Computational approaches, specifically machine-learning techniques, play an important role in many metagenomic analysis algorithms, such as gene prediction. Due to the large feature space, current de novo gene...

Authors: Amani Al-Ajlan and Achraf El Allali

Gene set analysis methods: a systematic comparison

Gene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have be...

Authors: Ravi Mathur, Daniel Rotroff, Jun Ma, Ali Shojaie and Alison Motsinger-Reif

Connecting genetics and gene expression data for target prioritisation and drug repositioning

Developing new drugs continues to be a highly inefficient and costly business. By repurposing an existing compound for a different indication, drug repositioning offers an attractive alternative to traditional...

Authors: Enrico Ferrero and Pankaj Agarwal

Collective feature selection to identify crucial epistatic variants

Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remai...

Authors: Shefali S. Verma, Anastasia Lucas, Xinyuan Zhang, Yogasudha Veturi, Scott Dudek, Binglan Li, Ruowang Li, Ryan Urbanowicz, Jason H. Moore, Dokyoon Kim and Marylyn D. Ritchie

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)

Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning...

Authors: Elizabeth R. Piette and Jason H. Moore

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be use...

Authors: Juan A. Nepomuceno, Alicia Troncoso, Isabel A. Nepomuceno-Chamorro and Jesús S. Aguilar-Ruiz

A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, res...

Authors: Wenyi Qin and Hui Lu

Investigating the parameter space of evolutionary algorithms

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, an...

Authors: Moshe Sipper, Weixuan Fu, Karuna Ahuja and Jason H. Moore

Identification of influential observations in high-dimensional cancer survival data through the rank product test

Survival analysis is a statistical technique widely used in many fields of science, in particular in the medical area, and which studies the time until an event of interest occurs. Outlier detection in this co...

Authors: Eunice Carrasquinha, André Veríssimo, Marta B. Lopes and Susana Vinga

Scalable non-negative matrix tri-factorization

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disea...

Authors: Andrej Čopar, Marinka žitnik and Blaž Zupan

An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images

In the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefo...

Authors: Qiwei Xie, Xi Chen, Hao Deng, Danqian Liu, Yingyu Sun, Xiaojuan Zhou, Yang Yang and Hua Han

Sparse generalized linear model with L ₀ approximation for feature selection and prediction with big omics data

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L ₁, SCAD and MC+. However, none of...

Authors: Zhenqiu Liu, Fengzhu Sun and Dermot P. McGovern

TSPmap, a tool making use of traveling salesperson problem solvers in the efficient and accurate construction of high-density genetic linkage maps

Recent advances in nucleic acid sequencing technologies have led to a dramatic increase in the number of markers available to generate genetic linkage maps. This increased marker density can be used to improve...

Authors: J. Grey Monroe, Zachariah A. Allen, Paul Tanger, Jack L. Mullen, John T. Lovell, Brook T. Moyers, Darrell Whitley and John K. McKay

Cluster ensemble based on Random Forests for genetic data

Clustering plays a crucial role in several application domains, such as bioinformatics. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic data...

Authors: Luluah Alhusain and Alaaeldin M. Hafez

PMLB: a large benchmark suite for machine learning evaluation and comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world ...

Authors: Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz and Jason H. Moore

Ten quick tips for machine learning in computational biology

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experi...

Authors: Davide Chicco

Artificial intelligence: more human with human

Authors: Moshe Sipper and Jason H. Moore

OCDD: an obesity and co-morbid disease database

Obesity is a medical condition that is known for increased body mass index (BMI). It is also associated with chronic low level inflammation. Obesity disrupts the immune-metabolic homeostasis by changing the se...

Authors: Indrani Ray, Anindya Bhattacharya and Rajat K. De

Metrics to estimate differential co-expression networks

Detecting the differences in gene expression data is important for understanding the underlying molecular mechanisms. Although the differentially expressed genes are a large component, differences in correlati...

Authors: Elpidio-Emmanuel Gonzalez-Valbuena and Víctor Treviño

Methods for enhancing the reproducibility of biomedical research findings using electronic health records

The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of biomedical research by the wider community. However, a substantial proportion o...

Authors: Spiros Denaxas, Kenan Direk, Arturo Gonzalez-Izquierdo, Maria Pikoula, Aylin Cakiroglu, Jason Moore, Harry Hemingway and Liam Smeeth

RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study

Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess glo...

Authors: Bork A. Berghoff, Torgny Karlsson, Thomas Källman, E. Gerhart H. Wagner and Manfred G. Grabherr

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects ...

Authors: Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, Scott H. Harrison and Abdollah Homaifar

Genetically improved BarraCUDA

BarraCUDA is an open source C program which uses the BWA algorithm in parallel with nVidia CUDA to align short next generation DNA sequences against a reference genome. Recently its source code was optimised u...

Authors: W. B. Langdon and Brian Yee Hong Lam

nRC: non-coding RNA Classifier based on structural features

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically...

Authors: Antonino Fiannaca, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo and Alfonso Urso

Evolutionary computation: the next major transition of artificial intelligence?

Authors: Moshe Sipper, Randal S. Olson and Jason H. Moore

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits...

Authors: Emily R. Holzinger, Shefali S. Verma, Carrie B. Moore, Molly Hall, Rishika De, Diane Gilbert-Diamond, Matthew B. Lanktree, Nathan Pankratz, Antoinette Amuzu, Amber Burt, Caroline Dale, Scott Dudek, Clement E. Furlong, Tom R. Gaunt, Daniel Seung Kim, Helene Riess…

The Dark Proteome Database

Recently we surveyed the dark-proteome, i.e., regions of proteins never observed by experimental structure determination and inaccessible to homology modelling. Surprisingly, we found that most of the dark pro...

Authors: Nelson Perdigão, Agostinho C. Rosa and Seán I. O’Donoghue

Arete – candidate gene prioritization using biological network topology with additional evidence types

Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of spec...

Authors: Artem Lysenko, Keith Anthony Boroevich and Tatsuhiko Tsunoda

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic ...

Authors: Yingxia Sun, Junliang Shang, Jin-Xing Liu, Shengjun Li and Chun-Hou Zheng

EFS: an ensemble feature selection tool implemented as R-package and web-application

Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies...

Authors: Ursula Neumann, Nikita Genze and Dominik Heider

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for u...

Authors: Yulan Liang and Arpad Kelemen

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had ...

Authors: Jason H. Moore, Peter C. Andrews, Randal S. Olson, Sarah E. Carlson, Curt R. Larock, Mario J. Bulhoes, James P. O’Connor, Ellen M. Greytak and Steven L. Armentrout

Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype

Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Ana...

Authors: Vinicius Tragante, Johannes M. I. H. Gho, Janine F. Felix, Ramachandran S. Vasan, Nicholas L. Smith, Benjamin F. Voight, Colin Palmer, Pim van der Harst, Jason H. Moore and Folkert W. Asselbergs

Vinasse fertirrigation alters soil resistome dynamics: an analysis based on metagenomic profiles

Every year around 300 Gl of vinasse, a by-product of ethanol distillation in sugarcane mills, are flushed into more than 9 Mha of sugarcane cropland in Brazil. This practice links fermentation waste management...

Authors: Lucas P. P. Braga, Rafael F. Alves, Marina T. F. Dellias, Acacio A. Navarrete, Thiago O. Basso and Siu M. Tsai

The optimal crowd learning machine

Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness.

Authors: Bilguunzaya Battogtokh, Majid Mojirsheibani and James Malley

Study of Meta-analysis strategies for network inference using information-theoretic approaches

Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data ha...

Authors: Ngoc C. Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi and Patrick E. Meyer

Feature analysis for classification of trace fluorescent labeled protein crystallization images

Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The exc...

Authors: Madhav Sigdel, Imren Dinc, Madhu S. Sigdel, Semih Dinc, Marc L. Pusey and Ramazan S. Aygun

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model comp...

Authors: Nathaniel M. Crabtree, Jason H. Moore, John F. Bowyer and Nysia I. George