Ask about this productRelated genes to: DAPP1 Blocking Peptide
- Gene:
- DAPP1 NIH gene
- Name:
- dual adaptor of phosphotyrosine and 3-phosphoinositides 1
- Previous symbol:
- -
- Synonyms:
- BAM32
- Chromosome:
- 4q23
- Locus Type:
- gene with protein product
- Date approved:
- 2001-09-07
- Date modifiied:
- 2016-05-27
Related products to: DAPP1 Blocking Peptide
Related articles to: DAPP1 Blocking Peptide
- Single-cell multi-omics technologies capture cellular heterogeneity at unprecedented resolution, yet dimensionality reduction methods face a fundamental local-global trade-off: approaches optimized for local neighborhood preservation distort global topology, while those emphasizing global coherence obscure fine-grained cell states. - Source: PubMed
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Fu ZeyuFu JiaweiChen ChunlinZhang KeyangWang Song - It is urgent to explore the potential biomarkers for pancreatic cancer (PC) prognosis and treatment to improve patients' outcomes. - Source: PubMed
Publication date: 2025/10/07
Cui ZhongyuanLei XiaGou YaniWu ZhixianHuang Xiaojun - Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular differentiation, yet many existing methods have difficulty modeling its continuous, coupled, and noise-prone dynamics. We present CODEVAE (Correlated Ordinary Differential Equation Variational Autoencoder), a deep generative framework that integrates ordinary differential equation constraints with correlation-aware latent representations to preserve geometric continuity and biologically coupled variation. Building on a baseline variational autoencoder, CODEVAE incrementally incorporates low-β regularization, an information bottleneck reconstruction pathway, ODE-based continuity, and correlated latent components. Across an evaluation suite of 18 metrics and 55 independent runs, CODEVAE achieves consistently higher performance than advanced variational models, single-cell specific methods, graph/contrastive approaches, and traditional dimensionality reduction techniques. In multi-batch settings, CODEVAE maintains smooth manifolds and attains improved integration quality. In biological applications, CODEVAE reconstructs a continuous megakaryocyte differentiation trajectory and delineates stage-specific effects of Dapp1 perturbation. These findings position CODEVAE as a robust, principled approach for modeling continuous cellular dynamics and extracting mechanistic insights across diverse single-cell contexts. - Source: PubMed
Publication date: 2025/10/04
Fu ZeyuChen Chunlin - Response to trastuzumab-based neoadjuvant therapy in human epidermal growth factor receptor type 2 (HER2)-positive breast cancer is affected by multiple features of the tumor. Few studies have investigated epigenetic features in these patients. This study investigates whether changes in deoxyribonucleic acid (DNA) methylation patterns are linked to response to neoadjuvant therapy in HER2-positive breast cancer and aims to identify epigenetic markers of treatment resistance. - Source: PubMed
Publication date: 2025/08/06
Guo ZibaiWei JinhongGui AnpingLiang XuanhuaYu PengliBai JingCui LiangXia XuefengMa Shihui - Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor-critic framework that recasts differentiation as a sequential decision process on an interpretable latent manifold derived with Latent Dirichlet Allocation. The critic learns state-value functions that quantify fate intensity for each cell, while the actor traces optimal developmental routes across the manifold. Benchmarks on hematopoiesis, mouse endocrinogenesis, acute myeloid leukemia, and gene-knockout and irradiation datasets show that scRL surpasses fifteen state-of-the-art methods in five independent evaluation dimensions, recovering early decision states that precede overt lineage commitment and revealing regulators such as . Beyond fate decisions, the same framework produces competitive measures of lineage-contribution intensity without requiring ground-truth probabilities, providing a unified and extensible approach for decoding developmental logic from single-cell data. - Source: PubMed
Publication date: 2025/06/11
Fu ZeyuChen ChunlinWang SongWang JunpingChen Shilei