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  1. Research

    Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification

    An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating clas...

    Jinyan Li, Simon Fong, Yunsick Sung, Kyungeun Cho, Raymond Wong and Kelvin K. L. Wong

    BioData Mining 2016 9:37

    Published on: 1 December 2016

  2. Research

    Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

    Biomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there ...

    Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, Theodor Baars, Julia Kälsch, Ali Canbay and Dominik Heider

    BioData Mining 2016 9:36

    Published on: 18 November 2016

  3. Research

    On the evaluation of the fidelity of supervised classifiers in the prediction of chimeric RNAs

    High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in bot...

    Sacha Beaumeunier, Jérôme Audoux, Anthony Boureux, Florence Ruffle, Thérèse Commes, Nicolas Philippe and Ronnie Alves

    BioData Mining 2016 9:34

    Published on: 2 November 2016

  4. Research

    Low-mass-ion discriminant equation (LOME) for ovarian cancer screening

    A low-mass-ion discriminant equation (LOME) was constructed to investigate whether systematic low-mass-ion (LMI) profiling could be applied to ovarian cancer (OVC) screening.

    Jun Hwa Lee, Byong Chul Yoo, Yun Hwan Kim, Sun-A Ahn, Seung-Gu Yeo, Jae Youl Cho, Kyung-Hee Kim and Seung Cheol Kim

    BioData Mining 2016 9:32

    Published on: 12 October 2016

  5. Methodology

    Functional networks inference from rule-based machine learning models

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the si...

    Nicola Lazzarini, Paweł Widera, Stuart Williamson, Rakesh Heer, Natalio Krasnogor and Jaume Bacardit

    BioData Mining 2016 9:28

    Published on: 5 September 2016

  6. Software article

    A biologically informed method for detecting rare variant associations

    BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin col...

    Carrie Colleen Buchanan Moore, Anna Okula Basile, John Robert Wallace, Alex Thomas Frase and Marylyn DeRiggi Ritchie

    BioData Mining 2016 9:27

    Published on: 30 August 2016


    msBiodat analysis tool, big data analysis for high-throughput experiments

    Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of pr...

    Pau M. Muñoz-Torres, Filip Rokć, Robert Belužic, Ivana Grbeša and Oliver Vugrek

    BioData Mining 2016 9:26

    Published on: 19 August 2016


    Mango: combining and analyzing heterogeneous biological networks

    Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing softwar...

    Jennifer Chang, Hyejin Cho and Hui-Hsien Chou

    BioData Mining 2016 9:25

    Published on: 2 August 2016

  9. Methodology

    Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

    Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint...

    Gordon Okimoto, Ashkan Zeinalzadeh, Tom Wenska, Michael Loomis, James B. Nation, Tiphaine Fabre, Maarit Tiirikainen, Brenda Hernandez, Owen Chan, Linda Wong and Sandi Kwee

    BioData Mining 2016 9:24

    Published on: 29 July 2016

  10. Review

    Representing and querying disease networks using graph databases

    Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we des...

    Artem Lysenko, Irina A. Roznovăţ, Mansoor Saqi, Alexander Mazein, Christopher J Rawlings and Charles Auffray

    BioData Mining 2016 9:23

    Published on: 25 July 2016

  11. Research

    Data integration to prioritize drugs using genomics and curated data

    Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer ...

    Riku Louhimo, Marko Laakso, Denis Belitskin, Juha Klefström, Rainer Lehtonen and Sampsa Hautaniemi

    BioData Mining 2016 9:21

    Published on: 26 May 2016


    SePIA: RNA and small RNA sequence processing, integration, and analysis

    Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual ...

    Katherine Icay, Ping Chen, Alejandra Cervera, Ville Rantanen, Rainer Lehtonen and Sampsa Hautaniemi

    BioData Mining 2016 9:20

    Published on: 20 May 2016

  13. Research

    Machine learning algorithms for mode-of-action classification in toxicity assessment

    Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probi...

    Yile Zhang, Yau Shu Wong, Jian Deng, Cristina Anton, Stephan Gabos, Weiping Zhang, Dorothy Yu Huang and Can Jin

    BioData Mining 2016 9:19

    Published on: 13 May 2016

  14. Methodology

    Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network

    The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies i...

    Ruowang Li, Scott M. Dudek, Dokyoon Kim, Molly A. Hall, Yuki Bradford, Peggy L. Peissig, Murray H. Brilliant, James G. Linneman, Catherine A. McCarty, Le Bao and Marylyn D. Ritchie

    BioData Mining 2016 9:18

    Published on: 10 May 2016

  15. Research

    Building a glaucoma interaction network using a text mining approach

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods ha...

    Maha Soliman, Olfa Nasraoui and Nigel G. F. Cooper

    BioData Mining 2016 9:17

    Published on: 5 May 2016

  16. Review

    Visual programming for next-generation sequencing data analytics

    High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework in biological and medical sciences for all basic and translational research. Pr...

    Franco Milicchio, Rebecca Rose, Jiang Bian, Jae Min and Mattia Prosperi

    BioData Mining 2016 9:16

    Published on: 27 April 2016

  17. Editorial

    The golden era of biomedical informatics has begun

    Biomedical informatics has become a central focus for many academic medical centers and universities as biomedical research because increasingly reliant on the processing, analysis, and interpretation of large...

    Jason H. Moore and John H. Holmes

    BioData Mining 2016 9:15

    Published on: 11 April 2016

  18. Methodology

    Detecting gene-gene interactions using a permutation-based random forest method

    Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodolog...

    Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas and Jason H. Moore

    BioData Mining 2016 9:14

    Published on: 6 April 2016

  19. Methodology

    Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads

    Gene isoforms are commonly found in both prokaryotes and eukaryotes. Since each isoform may perform a specific function in response to changing environmental conditions, studying the dynamics of gene isoforms ...

    Ma Liang, Castle Raley, Xin Zheng, Geetha Kutty, Emile Gogineni, Brad T. Sherman, Qiang Sun, Xiongfong Chen, Thomas Skelly, Kristine Jones, Robert Stephens, Bin Zhou, William Lau, Calvin Johnson, Tomozumi Imamichi, Minkang Jiang…

    BioData Mining 2016 9:13

    Published on: 5 April 2016

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