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Full text 2026

singIST: An integrative method for comparative single-cell transcriptomics between disease models and humans

Moruno-Cuenca A, Picart-Armada S, Bogle R, et al.

<h4>Motivation</h4>Disease models are fundamental tools in drug discovery and early-stage drug development, but they only approximate human disease, and selecting a suitable model is challenging. Quantitative computational methods exist to assess molecular resemblance to human …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

SPP1 as a Critical Regulator of Cardiac Cell Reprogramming Following Myocardial Infarction Through Single-Cell Transcriptomic Analysis

Wang R, Zhang M, Liu X, et al.

<h4>Background</h4>Cardiovascular mortality remains predominantly driven by acute myocardial infarction (AMI), necessitating comprehensive elucidation of mechanisms governing cardiomyocyte reprogramming for therapeutic advancement. Characterizing molecular dynamics throughout cardiac repair processes presents substantial methodological challenges.<h4>Methods</h4>We employed a comprehensive …

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Bioinformatics Transcriptomics
Full text 2026

Integrated Single-Cell Virtual Knockout and Machine Learning Analyses Reveal a Protective Role of CKAP2 in Gastric Cancer

Yang J, Qiu Z, Song W, et al.

<b>Objective:</b> To elucidate the role of cytoskeleton-associated protein 2 (CKAP2) in gastric cancer (GC) progression and evaluate its prognostic and potential protective significance. <b>Methods:</b> The candidate genes for GC were identified using differential expression and …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

Single-cell transcriptomic comparison of tubular segment maturation in advanced human &lt;i&gt;in vitro&lt;/i&gt; kidney models

Pou Casellas C, Yousef Yengej FA, Ammerlaan CME, et al.

<i>In vitro</i> kidney models play a crucial role in the study of renal development, (patho)physiology, drug development, and potential applications in bioartificial kidneys. Human <i>in vitro</i> kidney models have recently advanced significantly with the development …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

Exploration of the roles of SSR2 in hepatocellular carcinogenesis based on single-cell transcriptomics and spatial transcriptomics

Liu S, Wang X, Cheng D, et al.

<h4>Background</h4>Hepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pathogenesis and limitations of therapeutic approaches.<h4>Method</h4>To improve the prognosis of HCC patients, we analyzed …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

Interpretable learning of temporal cellular dynamics from single-cell data

Kouadri Boudjelthia I, Milite S, El Kazwini N, et al.

Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful, but interpreting their results to learn new biology remains difficult, and their predictive power is limited. …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

Benchmarking LLM-based agents for single-cell omics analysis

Liu Y, Zhou L, Du X, et al.

<h4>Background</h4>The surge in single-cell omics data exposes limitations in traditional, manually defined analysis workflows. AI agents offer a paradigm shift, enabling adaptive planning, executable code generation, traceable decisions, and real-time knowledge fusion. However, the lack …

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Bioinformatics
Full text 2026

Protocol for single-cell optimization objective and trade-off inference

Lin DW, Teixeira C, Chung C, et al.

Single-cell optimization objective and trade-off inference (SCOOTI) is a computational framework that integrates bulk and single-cell omics data with genome-scale metabolic modeling to infer metabolic objectives and trade-offs in biological systems. Here, we present a …

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Bioinformatics Single-Cell Transcriptomics
Full text 2026

Integrated Genomic and Single-Cell Analysis Reveals Heterogeneity, Prognosis, and Treatment Vulnerability in Urothelial Carcinoma

Tang C, Liu Y, Wang TL, et al.

At the transcriptomic level, several molecular subtyping schemes have been established to elucidate the intrinsic heterogeneity of urothelial carcinoma and to inform prognostic assessment and therapeutic guidance. However, a unified molecular classification scheme characterizing genomic …

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Bioinformatics Single-Cell Transcriptomics