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Biological research is generating high volumes of data distributed across various sources. The inconsistent naming of proteins and their encoding genes brings great challenges to protein data integration: prot...
The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify dr...
Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approac...
Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization o...
Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experiment...
Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of...
Hepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system i...
To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomo...
Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life...
The study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs ...
Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by th...
Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes...
The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data i...
Here we present scSNPdemux, a sample demultiplexing pipeline for single-cell RNA sequencing data using natural genetic variations in humans. The pipeline requires alignment files from Cell Ranger (10× Genomics...
There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to sc...
Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical researc...
Prediction of drug–target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods addre...
The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used f...
The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our gl...
Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measu...
Hashimoto’s thyroiditis is an autoimmune disorder characterized by the destruction of thyroid cells through immune-mediated mechanisms involving cells and antibodies. The condition can trigger disturbances in ...
Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background acros...
Transposable elements (TEs) are short, mobile DNA elements that are known to play important roles in the genomes of many eukaryotic species. The identification and categorization of these elements is a critica...
Biologists are faced with an ever-changing array of complex software tools with steep learning curves, often run on High Performance Computing platforms. To resolve the tradeoff between analytical sophisticati...
Two types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtain...
Existing methods for generating synthetic genotype data are ill-suited for replicating the effects of assortative mating (AM). We propose rb_dplr, a novel and computationally efficient algorithm for generating hi...
Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bact...
Accurate case report data are essential to understand arbovirus dynamics, including spread and evolution of arboviruses such as Zika, dengue and chikungunya viruses. Giving the multi-country nature of arboviru...
Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information.
Visualizing genome coverage is of vital importance to inspect and interpret various next-generation sequencing (NGS) data. Besides genome coverage, genome annotations are also crucial in the visualization. Whi...
With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms ...
Proteins often assemble into higher-order complexes to perform their biological functions. Such protein–protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a we...
Since the introduction of next-generation sequencing (NGS) techniques, whole-exome sequencing (WES) and whole-genome sequencing (WGS) have not only revolutionized research, but also diagnostics. The gradual sw...
Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a networ...
The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relativ...
Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. V...
The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs a...
Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as ...
An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to m...
Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expressi...
Protein engineering aims to improve the functional properties of existing proteins to meet people’s needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features co...
Statistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal r...
To understand genome evolution in a group of microbes, we need to know the timing of events such as duplications, deletions and horizontal transfers. A common approach is to perform a gene-tree / species-tree ...
Identifying variants associated with diseases is a challenging task in medical genetics research. Current studies that prioritize variants within individual genomes generally rely on known variants, evidence f...
Accurate prediction of molecular property holds significance in contemporary drug discovery and medical research. Recent advances in AI-driven molecular property prediction have shown promising results. Due to...
The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging ...
The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the grow...
Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is diff...
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Citation Impact 2023
Journal Impact Factor: 2.9
5-year Journal Impact Factor: 3.6
Source Normalized Impact per Paper (SNIP): 0.821
SCImago Journal Rank (SJR): 1.005
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