| # | Title | Journal | Year | Citations |
|---|
| 1 | Ten quick tips for machine learning in computational biology | BioData Mining | 2017 | 588 |
| 2 | Using graph theory to analyze biological networks | BioData Mining | 2011 | 547 |
| 3 | The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation | BioData Mining | 2021 | 364 |
| 4 | Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks | BioData Mining | 2015 | 360 |
| 5 | Visualising associations between paired ‘omics’ data sets | BioData Mining | 2012 | 261 |
| 6 | PMLB: a large benchmark suite for machine learning evaluation and comparison | BioData Mining | 2017 | 188 |
| 7 | GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures | BioData Mining | 2012 | 184 |
| 8 | Visualizing genomic information across chromosomes with PhenoGram | BioData Mining | 2013 | 175 |
| 9 | A survey of visualization tools for biological network analysis | BioData Mining | 2008 | 173 |
| 10 | DA DA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization | BioData Mining | 2011 | 148 |
| 11 | Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions | BioData Mining | 2009 | 129 |
| 12 | LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data | BioData Mining | 2013 | 112 |
| 13 | Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends | BioData Mining | 2014 | 102 |
| 14 | ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity | BioData Mining | 2019 | 99 |
| 15 | An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use | BioData Mining | 2014 | 93 |
| 16 | Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking | BioData Mining | 2021 | 92 |
| 17 | Meta-analytic support vector machine for integrating multiple omics data | BioData Mining | 2017 | 90 |
| 18 | Fast Gene Ontology based clustering for microarray experiments | BioData Mining | 2008 | 89 |
| 19 | A reference guide for tree analysis and visualization | BioData Mining | 2010 | 81 |
| 20 | Representing and querying disease networks using graph databases | BioData Mining | 2016 | 75 |
| 21 | Using Bayesian networks to discover relations between genes, environment, and disease | BioData Mining | 2013 | 71 |
| 22 | Supervised DNA Barcodes species classification: analysis, comparisons and results | BioData Mining | 2014 | 71 |
| 23 | Mining SOM expression portraits: feature selection and integrating concepts of molecular function | BioData Mining | 2012 | 70 |
| 24 | Accurate prediction of protein relative solvent accessibility using a balanced model | BioData Mining | 2017 | 70 |
| 25 | Comprehensive analysis of human microRNA target networks | BioData Mining | 2011 | 69 |
| 26 | EFS: an ensemble feature selection tool implemented as R-package and web-application | BioData Mining | 2017 | 68 |
| 27 | Gene set analysis methods: a systematic comparison | BioData Mining | 2018 | 68 |
| 28 | Encodings and models for antimicrobial peptide classification for multi-resistant pathogens | BioData Mining | 2019 | 68 |
| 29 | Predicting opioid dependence from electronic health records with machine learning | BioData Mining | 2019 | 65 |
| 30 | ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network | BioData Mining | 2013 | 64 |
| 31 | nRC: non-coding RNA Classifier based on structural features | BioData Mining | 2017 | 64 |
| 32 | A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data | BioData Mining | 2009 | 63 |
| 33 | A review of estimation of distribution algorithms in bioinformatics | BioData Mining | 2008 | 61 |
| 34 | CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits | BioData Mining | 2014 | 54 |
| 35 | ChatGPT and large language models in academia: opportunities and challenges | BioData Mining | 2023 | 54 |
| 36 | r2VIM: A new variable selection method for random forests in genome-wide association studies | BioData Mining | 2016 | 53 |
| 37 | Deep learning methods improve linear B-cell epitope prediction | BioData Mining | 2020 | 52 |
| 38 | Detecting gene-gene interactions using a permutation-based random forest method | BioData Mining | 2016 | 51 |
| 39 | epiACO - a method for identifying epistasis based on ant Colony optimization algorithm | BioData Mining | 2017 | 51 |
| 40 | Investigating the parameter space of evolutionary algorithms | BioData Mining | 2018 | 51 |
| 41 | Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development | BioData Mining | 2013 | 50 |
| 42 | Biclustering of Gene Expression Data by Correlation-Based Scatter Search | BioData Mining | 2011 | 48 |
| 43 | Caipirini: using gene sets to rank literature | BioData Mining | 2012 | 47 |
| 44 | Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure | BioData Mining | 2011 | 46 |
| 45 | ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci | BioData Mining | 2010 | 43 |
| 46 | Unraveling genomic variation from next generation sequencing data | BioData Mining | 2013 | 43 |
| 47 | Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis | BioData Mining | 2010 | 42 |
| 48 | Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View | BioData Mining | 2012 | 42 |
| 49 | A comparison of machine learning techniques for survival prediction in breast cancer | BioData Mining | 2011 | 41 |
| 50 | Connecting genetics and gene expression data for target prioritisation and drug repositioning | BioData Mining | 2018 | 41 |
