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gene    音标拼音: [dʒ'in]
n. 因子,基因

因数,基因

gene
n 1: (genetics) a segment of DNA that is involved in producing a
polypeptide chain; it can include regions preceding and
following the coding DNA as well as introns between the
exons; it is considered a unit of heredity; "genes were
formerly called factors" [synonym: {gene}, {cistron}, {factor}]

60 Moby Thesaurus words for "gene":
Altmann theory, DNA, DNA double helix, De Vries theory,
Galtonian theory, Mendelianism, Mendelism, RNA, Verworn theory,
Weismann theory, Weismannism, Wiesner theory, allele, allelomorph,
anticodon, biotype, birth, character, chromatid, chromatin,
chromosome, codon, deoxyribonucleic acid, determinant, determiner,
diathesis, endowment, eugenics, factor, gene complex, gene flow,
gene pool, genesiology, genetic code, genetic drift, genetics,
genotype, hereditability, hereditary character, heredity,
heritability, heritage, inborn capacity, inheritability,
inheritance, mRNA, matrocliny, messenger RNA, nucleotide,
operator gene, operon, patrocliny, pharmacogenetics,
recessive character, regulator gene, replication, ribosomal RNA,
structural gene, tRNA, transfer RNA


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  • Vol. 39 No. 15: AAAI-25 Technical Tracks 15
    SpotDiff: Spatial Gene Expression Imputation Diffusion with Single-Cell RNA Sequencing Data Integration Tianyi Chen, Yunfei Zhang, Lianxin Xie, Wenjun Shen, Si Wu, Hau-San Wong 15848-15856 PDF Video Poster Slides
  • Refinement Contrastive Learning of Cell–Gene Associations for . . .
    Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships
  • Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with . . .
    To address this, we propose Cradle-VAE, a causal generative framework tailored for single-cell gene perturbation modeling, enhanced with counterfactual reasoning-based artifact disentanglement Throughout training, Cradle-VAE models the underlying latent distribution of technical artifacts and perturbation effects present in single-cell datasets
  • Vol. 40 No. 2: AAAI-26 Technical Tracks 2
    Dizhan Xue, Jing Cui, Shengsheng Qian, Chuanrui Hu, Changsheng Xu 1391-1399 PDF Poster GenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences Jingquan Yan, Yuwei Miao, Lei Yu, Yuzhi Guo, Xue Xiao, Lin Xu, Junzhou Huang 1400-1408 PDF Video Poster
  • Dual-Path Knowledge-Augmented Contrastive Alignment Network for . . .
    To address these challenges, we propose DKAN, a novel Dual-path Knowledge-Augmented contrastive alignment Network that predicts spatially resolved gene expression by integrating histopathological images and gene expression profiles through a biologically informed approach
  • GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression . . .
    This study proposes a novel deep generative model called GxVAEs to generate hit-like molecules from gene expression profiles by leveraging two joint variational autoencoders (VAEs) The first VAE, ProfileVAE, extracts latent features from gene expression profiles
  • SpotDiff: Spatial Gene Expression Imputation Diffusion with Single-Cell . . .
    Further, SpotDiff integrates single-cell RNA sequencing data to impute gene expression at each spot The proposed approach is able to reduce the uncertainty in the imputation process, since the aggregation of multiple single-cell measurements yield a stable representation of the corresponding spot expression profile
  • Learning to Cluster Rare Cell Types: Implicit Semantic Data . . .
    Spatial multi-modal omics technologies have transformed biological research by enabling the simultaneous profiling of gene expression, protein abundance, and chromatin accessibility within their native spatial contexts Despite these advances, accurately clustering rare cell types remains a major challenge due to data sparsity, high dimensionality, and limited annotated samples While Graph
  • Vol. 38 No. 1: AAAI-24 Technical Tracks 1
    Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport Jixiang Yu, Nanjun Chen, Ming Gao, Xiangtao Li, Ka-Chun Wong 356-364 PDF Video Poster





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