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<h4>Motivation</h4>We introduce a statistical approach for pattern recognition in multivariate spatial transcriptomics data.<h4>Results</h4>Our algorithm constructs a projection of the data onto a low-dimensional feature space which is optimal in maximizing Moran's I, a measure of …
Read PDFEarly and accurate diagnosis remains a major challenge in cervical cancer management. This study aimed to identify reliable diagnostic biomarkers for cervical cancer by integrating bioinformatics and machine learning approaches and to further validate their …
Read PDF<h4>Introduction</h4>Acute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy with poor prognosis, despite therapeutic advances. Gene expression analysis has emerged as a powerful tool for identifying novel biological markers that can aid in predicting patient …
Read PDF<h4>Background</h4>Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus-associated malignancy. Tumor-associated macrophages play a pivotal role in NPC development, but molecular mechanisms remain unclear. This study aimed to identify M1 macrophage-associated hub genes and investigate their biological …
Read PDF<h4>Summary</h4>Cell-cell communication dynamically changes across time while involving diverse cell populations and ligand types such as proteins and metabolites. Single-cell transcriptomics enables its inference, but existing tools typically analyze ligand types separately and overlook their …
Read PDF<h4>Summary</h4>Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an …
Read PDFOvarian cancer remains a major clinical challenge, largely because most patients present with advanced-stage disease. Early detection is essential for improving outcomes, yet current biomarkers such as CA-125 and HE4 have limited sensitivity in early-stage …
Read PDFThe identification of cell types is a basic step of pipelines for Single-Cell RNA sequencing (scRNA-seq) data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: high dimensionality, sparseness of the expression …
Read PDF<h4>Motivation</h4>Foundation models trained on large-scale single-cell transcriptomes can capture rich molecular representations of cellular states, yet their potential for cancer survival prediction from bulk RNA-seq data remains largely unexplored.<h4>Results</h4>We applied the single-cell foundation model scFoundation …
Read PDF<h4>Motivation</h4>Identifying meaningful patterns in complex biological data necessitates correlation coefficients capable of capturing diverse relationship types beyond simple linearity. Furthermore, efficient computational tools are crucial for handling the ever-increasing scale of biological datasets.<h4>Results</h4>We introduce CCC-GPU, …
Read PDF<h4>Motivation</h4>DNA damage response (DDR) is essential for maintaining genome stability and preventing tumorigenesis. While protein-coding DDR genes have been extensively investigated, long non-coding RNAs (lncRNAs) remain relatively understudied despite the growing evidence of their involvement …
Read PDF<h4>Motivation</h4>B-cell receptors (BCRs) and gene expression profiles are two distinct yet complementary modalities of B cells. However, most analyses treat them independently. Here, we present CoMBCR, a B-cell embedding tool that co-learns BCRs and gene …
Read PDFGene expression analysis has evolved substantially over the past 25 years, from early transcript surveys using expressed sequence tags and microarrays to RNA sequencing, and more recently to single-cell and spatial transcriptomics. These successive waves …
Read PDFModern genomics produces billions of sequencing records per run, which are typically stored as gzip-compressed FASTQ files. While this format is widely used, it is not optimal for high-throughput processing due to its reliance on …
Read PDF<h4>Summary</h4>Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead …
Read PDF<h4>Objectives</h4>Soft tissue sarcomas (STSs) are rare, heterogeneous cancers with over 70 subtypes, often diagnosed late due to diagnostic complexity, leading to poor outcomes. We aimed to identify and validate novel transcriptomic biomarkers for the diagnosis …
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