Journal: Journal of Translational Medicine

Article Title: Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems

doi: 10.1186/s12967-025-07091-0

Figure Lengend Snippet: Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration. This enables accurate predictions for gene regulatory networks (GRN) and drug response analysis . b LocalCLiP for Spatial Transcriptomics: LocalCLiP utilizes a local transformer model to integrate spatial transcriptomics data, using KNN for image patch analysis and gene expression prediction, providing insights into tissue-specific molecular patterns . c BioTask Executor for Task-Specific Analysis: The BioTask Executor handles various biological tasks, from zero-shot learning to GRN inference and drug response prediction, by preprocessing data, initializing pretrained models (e.g., SCGPT, Geneformer), and fine-tuning them for task-specific applications . d Human-8CATAC-CorpuS for Multi-Tissue Analysis: The Human-8CATAC-CorpuS dataset, with 5 million cells from 31 tissues, is used to train models for gene expression prediction and cCRE signal reconstruction, enabling comprehensive analysis of tissue-specific regulatory elements . The schematics were adapted from [ , , ] and

Article Snippet: The schematic were adapted from [ ] and [ ] with permission Fig. 3 Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration.

Techniques: Biomarker Discovery, Gene Expression

Journal: Polymers

Article Title: Generation of Randomly Inclined Fibers in the Representative Volume Element for Predicting the Elastic Modulus of Fiber-Reinforced Polymer Composites

doi: 10.3390/polym17172300

Figure Lengend Snippet: Inclined fibers in the representative volume element (RVE); ( a ) FRP profile; ( b ) the RVE; ( c ) single fiber.

Article Snippet: The pre-processed RVE model is imported into the ABAQUS 2021 to impose PBCs [ , , ].

Techniques: