AIMC Topic: Gene Expression Profiling

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Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer ti...

Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative Colitis Bioinformatics and Machine Learning.

Endocrine, metabolic & immune disorders drug targets
OBJECTIVE: To investigate and validate ferroptosis genes (FRGs) in ulcerative colitis (UC) for diagnostic, subtype, and biological agent reactivity, with the goal of providing a foundation for the identification of novel therapeutic targets and the r...

Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.

Combinatorial chemistry & high throughput screening
BACKGROUND: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as...

Emerging technologies in adipose tissue research.

Adipocyte
Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel tech...

Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis.

Acta biochimica et biophysica Sinica
Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinica...

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

Briefings in bioinformatics
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option,...

Explainable artificial intelligence for omics data: a systematic mapping study.

Briefings in bioinformatics
Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in ...

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Nucleic acids research
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to deter...

A multi-scale expression and regulation knowledge base for Escherichia coli.

Nucleic acids research
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-...

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq)...