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Neglected tropical diseases affect millions of individuals and cause loss of productivity worldwide. They are common in developing countries without the financial resources for research and drug development. W...
Gene regulatory networks (GRNs) arise from the intricate interactions between transcription factors (TFs) and their target genes during the growth and development of organisms. The inference of GRNs can unveil...
The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to pr...
In this paper we propose PIICM, a probabilistic framework for dose–response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionali...
Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing info...
Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible t...
Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manua...
Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, opht...
The original article was published in BMC Bioinformatics 2023 24:109
The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association b...
Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring ...
Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are...
The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool ret...
Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invarianc...
Gene expression profiling is a widely adopted method in areas like drug development or functional gene analysis. Microarray data of gene expression experiments is still commonly used and widely available for r...
It has been observed that high levels of enhancer of zeste homolog 2 (EZH2) expression are associated with unsatisfactory prognoses and can be found in a wide range of malignancies. However, the effects of EZH...
Concurrent existence of lncRNA and circular RNA at both nucleus and cytosol within a cell at different proportions is well reported. Previous studies showed that circular RNAs are synthesized in nucleus follow...
Gastric cancer (GC) is one of the most common causes of cancer-related fatalities worldwide, and its progression is associated with RNA modifications. Here, using RNA modification-related genes (RNAMRGs), we a...
The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction.
Compositional heterogeneity—when the proportions of nucleotides and amino acids are not broadly similar across the dataset—is a cause of a great number of phylogenetic artefacts. Whilst a variety of methods ca...
Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases...
Modeling the whole cardiac function involves the solution of several complex multi-physics and multi-scale models that are highly computationally demanding, which call for simpler yet accurate, high-performanc...
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is highly phenotypically and genetically heterogeneous. With the accumulation of biological sequencing data, more and more studies s...
Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic ...
Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also...
Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is “large p and small n” in that data contain a small number of subjects but a large number of ge...
For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach i...
Vesicle transport proteins not only play an important role in the transmembrane transport of molecules, but also have a place in the field of biomedicine, so the identification of vesicle transport proteins is...
Bistable systems, i.e., systems that exhibit two stable steady states, are of particular interest in biology. They can implement binary cellular decision making, e.g., in pathways for cellular differentiation ...
Population structure and cryptic relatedness between individuals (samples) are two major factors affecting false positives in genome-wide association studies (GWAS). In addition, population stratification and ...
Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques ...
RNA-seq followed by de novo transcriptome assembly has been a transformative technique in biological research of non-model organisms, but the computational processing of RNA-seq data entails many different sof...
In protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid seque...
EML4-ALK gene fusions are oncogenic drivers in non-small cell lung cancer (NSCLC), and liquid biopsies containing EML4-ALK fragments can be used to study tumor dynamics using next-generation sequencing (NGS). How...
In the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection tech...
Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for iden...
Concentrations of the pathogenic microorganisms’ DNA in biological samples are typically low. Therefore, DNA diagnostics of common infections are costly, rarely accurate, and challenging. Limited by failing to...
Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions th...
The human microbiome plays a critical role in maintaining human health. Due to the recent advances in high-throughput sequencing technologies, the microbiome profiles present in the human body have become publ...
The Correction to this article has been published in BMC Bioinformatics 2023 24:307
Cluster analysis is utilized frequently in scientific theory and applications to separate data into groups. A key assumption in many clustering algorithms is that the data was generated from a population consi...
Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for furt...
The original article was published in BMC Bioinformatics 2023 24:5
As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary pr...
In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of ge...
High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, an...
Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting m...
It has been discovered that tumor-infiltrating lymphocytes (TILs) are essential for the emergence of bladder cancer (BCa). This study aimed to research TIL-related genes (TILRGs) and create a gene model to pre...
Biomedical researchers use alignments produced by BLAST (Basic Local Alignment Search Tool) to categorize their query sequences. Producing such alignments is an essential bioinformatics task that is well suite...
Gene co-expression networks represent modules of genes with shared biological function, and have been widely used to model biological pathways in gene expression data. Co-expression networks associated with a ...
This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different data...
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