Citation Impact 2023
Journal Impact Factor: 4.0
5-year Journal Impact Factor: 3.7
Source Normalized Impact per Paper (SNIP): 1.413
SCImago Journal Rank (SJR): 0.958
Speed 2023
Submission to first editorial decision (median days): 15
Submission to acceptance (median days): 171
Usage 2023
Downloads: 400,374
Altmetric mentions: 146
Articles
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Citation: BioData Mining 2023 16:9
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Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms
Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy b...
Citation: BioData Mining 2023 16:8 -
Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma
Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One o...
Citation: BioData Mining 2023 16:7 -
Ten simple rules for providing bioinformatics support within a hospital
Bioinformatics has become a key aspect of the biomedical research programmes of many hospitals’ scientific centres, and the establishment of bioinformatics facilities within hospitals has become a common pract...
Citation: BioData Mining 2023 16:6 -
iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation
In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and ski...
Citation: BioData Mining 2023 16:5 -
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard ...
Citation: BioData Mining 2023 16:4 -
LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants and genes
Loss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. The association of LoF variants with compl...
Citation: BioData Mining 2023 16:3 -
Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms
Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cell...
Citation: BioData Mining 2023 16:2 -
Bacteria spatial tracking in Urban Park soils with MALDI-TOF Mass Spectrometry and Specific PCR
Urban parks constitute one of the main leisure areas, especially for the most vulnerable people in our society, children, and the elderly. Contact with soils can pose a health risk. Microbiological testing is ...
Citation: BioData Mining 2023 16:1 -
Robust and rigorous identification of tissue-specific genes by statistically extending tau score
In this study, we aimed to identify tissue-specific genes for various human tissues/organs more robustly and rigorously by extending the tau score algorithm.
Citation: BioData Mining 2022 15:31 -
Classification of breast cancer recurrence based on imputed data: a simulation study
Several studies have been conducted to classify various real life events but few are in medical fields; particularly about breast recurrence under statistical techniques. To our knowledge, there is no reported...
Citation: BioData Mining 2022 15:30 -
Detecting diseases in medical prescriptions using data mining methods
Every year, the health of millions of people around the world is compromised by misdiagnosis, which sometimes could even lead to death. In addition, it entails huge financial costs for patients, insurance comp...
Citation: BioData Mining 2022 15:29 -
Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning
Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand ...
Citation: BioData Mining 2022 15:28 -
An unsupervised image segmentation algorithm for coronary angiography
Computer visual systems can rapidly obtain a large amount of data and automatically process them with ease. These characteristics constitute advantages for the application of such systems in the automatic anal...
Citation: BioData Mining 2022 15:27 -
Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts
Knowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These dat...
Citation: BioData Mining 2022 15:26 -
EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers
The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing datab...
Citation: BioData Mining 2022 15:25 -
Effective hybrid feature selection using different bootstrap enhances cancers classification performance
Machine learning can be used to predict the different onset of human cancers. Highly dimensional data have enormous, complicated problems. One of these is an excessive number of genes plus over-fitting, fittin...
Citation: BioData Mining 2022 15:24 -
Polygenic risk modeling of tumor stage and survival in bladder cancer
Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve ...
Citation: BioData Mining 2022 15:23 -
A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature
Annotating scientific literature with ontology concepts is a critical task in biology and several other domains for knowledge discovery. Ontology based annotations can power large-scale comparative analyses in...
Citation: BioData Mining 2022 15:22 -
Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Clinical decision of extubation is a challenge in the treatment of patient with invasive mechanical ventilation (IMV), since existing extubation protocols are not capable of precisely predicting extubation fai...
Citation: BioData Mining 2022 15:21 -
Machine Learning Algorithms for understanding the determinants of under-five Mortality
Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five m...
Citation: BioData Mining 2022 15:20 -
ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data
The three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater...
Citation: BioData Mining 2022 15:19 -
Learning and visualizing chronic latent representations using electronic health records
Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial ...
Citation: BioData Mining 2022 15:18 -
Analysis of risk factors progression of preterm delivery using electronic health records
Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction o...
Citation: BioData Mining 2022 15:17 -
Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data
Cardiopulmonary exercise testing (CPET) provides a reliable and reproducible approach to measuring fitness in patients and diagnosing their health problems. However, the data from CPET consist of multiple time...
Citation: BioData Mining 2022 15:16 -
Benchmarking AutoML frameworks for disease prediction using medical claims
Ascertain and compare the performances of Automated Machine Learning (AutoML) tools on large, highly imbalanced healthcare datasets.
Citation: BioData Mining 2022 15:15 -
Novel digital approaches to the assessment of problematic opioid use
The opioid epidemic continues to contribute to loss of life through overdose and significant social and economic burdens. Many individuals who develop problematic opioid use (POU) do so after being exposed to ...
Citation: BioData Mining 2022 15:14 -
Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data
Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types i...
Citation: BioData Mining 2022 15:13 -
Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning
Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes...
Citation: BioData Mining 2022 15:12 -
Correction: Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies
Citation: BioData Mining 2022 15:11 -
DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets
Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, ...
Citation: BioData Mining 2022 15:10 -
A new challenge for data analytics: transposons
Citation: BioData Mining 2022 15:9 -
mSRFR: a machine learning model using microalgal signature features for ncRNA classification
This work presents mSRFR (microalgae SMOTE Random Forest Relief model), a classification tool for noncoding RNAs (ncRNAs) in microalgae, including green algae, diatoms, golden algae, and cyanobacteria. First, ...
Citation: BioData Mining 2022 15:8 -
Predicting molecular initiating events using chemical target annotations and gene expression
The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data ...
Citation: BioData Mining 2022 15:7 -
PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predict...
Citation: BioData Mining 2022 15:6 -
Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection
Symptom-based machine learning models for disease detection are a way to reduce the workload of doctors when they have too many patients. Currently, there are many research studies on machine learning or deep ...
Citation: BioData Mining 2022 15:5 -
Gene-Interaction-Sensitive enrichment analysis in congenital heart disease
Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to inco...
Citation: BioData Mining 2022 15:4 -
iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier
Lysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular ...
Citation: BioData Mining 2022 15:3 -
Polymorphisms in the mTOR-PI3K-Akt pathway, energy balance-related exposures and colorectal cancer risk in the Netherlands Cohort Study
The mTOR-PI3K-Akt pathway influences cell metabolism and (malignant) cell growth. We generated sex-specific polygenic risk scores capturing natural variation in 7 out of 10 top-ranked genes in this pathway. We...
Citation: BioData Mining 2022 15:2 -
Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applicatio...
Citation: BioData Mining 2022 15:1 -
Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus
Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study’s purpose was to construct mac...
Citation: BioData Mining 2021 14:52 -
Identification of natural selection in genomic data with deep convolutional neural network
With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies su...
Citation: BioData Mining 2021 14:51 -
LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification
Long noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experime...
Citation: BioData Mining 2021 14:50 -
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, an...
Citation: BioData Mining 2021 14:49 -
Development of glaucoma predictive model and risk factors assessment based on supervised models
To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors.
Citation: BioData Mining 2021 14:48 -
Correction to: iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes
Citation: BioData Mining 2021 14:47 -
Prediction of synergistic drug combinations using PCA-initialized deep learning
Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acqui...
Citation: BioData Mining 2021 14:46 -
Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation
Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automat...
Citation: BioData Mining 2021 14:45 -
Evaluation of different approaches for missing data imputation on features associated to genomic data
Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis. The data collection process can lead...
Citation: BioData Mining 2021 14:44 -
Taxonomy-based data representation for data mining: an example of the magnitude of risk associated with H. pylori infection
The amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of...
Citation: BioData Mining 2021 14:43