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De novo genome assembly poses challenges when dealing with highly degraded DNA samples or ultrashort sequencing reads. Probabilistic approaches have been offered to enhance the algorithms, though existing methods...
The original article was published in BMC Bioinformatics 2024 25:142
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a widely used technique for mapping protein-DNA interactions and histone modifications across the genome. Despite its utility, current analysi...
Understanding tumor heterogeneity is essential for advancing cancer treatment. Clonal reconstruction methods play a pivotal role in deciphering this heterogeneity. Our goal is to develop a clonal reconstructio...
Cophylogeny reconciliation is a powerful method for analyzing host-symbiont coevolution. The cophylogeny problem consists of mapping the phylogenetic tree of the symbionts into the one of the hosts, including ...
The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy of...
This article is part of a Supplement: Volume 24 Supplement 1
Automatic relationship extraction (RE) from biomedical literature is critical for managing the vast amount of scientific knowledge produced each year. In recent years, utilizing pre-trained language models (PL...
Cancer is a complex disease influenced by numerous concurrent genetic factors that result in diverse tumor microenvironments (TMEs) across different cancer types. Large-scale genomic projects, such as The Cancer ...
The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes ("feature selection") for downstream analysis (e.g., unsupervised cell clustering). T...
Prior results for tRNA and 5S rRNA demonstrated that secondary structure prediction accuracy can be significantly improved by modifying the parameters in the multibranch loop entropic penalty function. However...
Small RNAs, such as microRNAs (miRNAs), are candidates for mediating communication between the host and its microbiota, regulating bacterial gene expression and influencing microbiome functions and dynamics. H...
The high error rate associated with Oxford Nanopore sequencing technology adversely affects demultiplexing. To improve demultiplexing and reduce unclassified reads from nanopore sequencing data, we developed Myst...
Peptides have emerged as promising therapeutic agents for drug development against cancer, immune disorders, hypertension, and microbial infections. Peptide drugs have the advantage of high selectivity, low pr...
The progression of cancer is driven by the accumulation of mutations in driver genes. Many researches promote to identify cancer driver genes. However, most of them ignore the high-order features in the network.
Longitudinal studies often require flexible methodologies for predicting response trajectories based on time-dependent and time-independent covariates. To address the complexities of longitudinal data, this st...
The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the fu...
The advancement of technology and continuous glucose monitoring (CGM) systems has introduced several computational and technical challenges for clinicians and researchers. The growing volume of CGM data necess...
Copy number variation (CNV) analyses—often inferred from DNA-methylation data—depict alterations of DNA quantities across chromosomes and have improved tumour diagnostics and classification. For the analyses o...
Bivariate monotonic classifiers (BMCs) are based on pairs of input features. Like many other models used for machine learning, they can capture nonlinear patterns in high-dimensional data. At the same time, th...
The increasing amount of available genome sequence data enables large-scale comparative studies. A common task is the inference of phylogenies– a challenging task if close reference sequences are not available...
Gene set analysis aims to identify gene sets containing differentially expressed genes between two different experimental conditions. A representative example of gene sets is a gene regulatory network where mu...
Knowledge discovery in scientific literature is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential ...
Antimicrobial resistance (AMR) is one of the most concerning modern threats as it places a greater burden on health systems than HIV and malaria combined. Current surveillance strategies for tracking antimicro...
Identification of microorganisms in a biological sample is a crucial step in diagnostics, pathogen screening, biomedical research, evolutionary studies, agriculture, and biological threat assessment. While pro...
With the development of sequencing technologies, chromosome-level genome assemblies have become increasingly common across various organisms, including non-model species. BLAST + is one of the most widely used...
Recent technological advances have enabled the simultaneous collection of multi-omics data, i.e., different types or modalities of molecular data. Integrative predictive modeling of such data is particularly c...
Analyzing calcium imaging data to understand complex functional networks can be challenging, often requiring multiple tools, custom scripts, and some coding expertise. To address these challenges, we present C...
Gene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comp...
In the genome analysis workflow, Genome Analysis Toolkit (GATK) HaplotypeCaller is a widely used variant calling tool designed to accurately identify single nucleotide polymorphisms (SNPs) and insertions/delet...
Accurate identification of translation initiation sites is essential for the proper translation of mRNA into functional proteins. In eukaryotes, the choice of the translation initiation site is influenced by m...
Human Leukocyte Antigens (HLA) play central roles in histocompatibility and immune system functions, including antigen presentation. Accurate typing of Class I and II HLA genes is crucial for transplant tissue...
In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data so...
Genetic association studies play a pivotal role in identifying disease-associated variants, but researchers face challenges in performing essential calculations like Hardy–Weinberg equilibrium testing, odds ra...
Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as re...
The rapid expansion of next-generation sequencing (NGS) technologies has generated vast amounts of genomic data, creating a growing demand for secure, scalable, and accessible tools to support variant interpre...
Understanding and comparing three-dimensional (3D) structures of proteins can advance bioinformatics, molecular biology, and drug discovery. While 3D models offer detailed insights, comparing multiple structur...
Keywords: Cell mapping, Deep Learning, Kolmogorov-Arnold network, Single-cell RNA-seq, Spatial transcriptomics.
As a typical type of neurodegenerative disorders, Parkinson’s disease(PD) is characterized by significant clinical and progression heterogeneity. Based on gene expression data, reliable detection of PACE subty...
When a single gene exhibits an insignificant association with complex disease, applying gene–gene interactions as biomarkers may achieve striking findings. However, it is still a barrier to measuring the stren...
As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researche...
Cellular development and differentiation in Eukaryotes depends upon sequential gene regulatory decisions that allow a single genome to encode many hundreds of distinct cellular phenotypes. Decisions are stored...
DNA data storage is an emerging technology that caught the attention of many researchers and engineers. This technology uses DNA molecules as a storage medium and thus presents an extremely dense and durable s...
Drug repurposing offers a promising strategy for drug discovery. Drug repurposing involves identifying new therapeutic indications for existing, marketed drugs, thereby reducing the risks, costs, and time typi...
Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.
The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. Thes...
The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. Most routine analyses of scRNAseq data focus on indi...
Bees can be colonized by a large diversity of microbes, including beneficial gut symbionts and detrimental pathogens, with implications for bee health. Over the last few years, researchers around the world hav...
The advent of next-generation and long-read sequencing technologies has provided an ever-increasing wealth of phylogenetic data that require specially designed algorithms to decipher the underlying evolutionar...
A key challenge in differential abundance analysis (DAA) of microbial sequencing data is that the counts for each sample are compositional, resulting in potentially biased comparisons of the absolute abundance...
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