AIMC Topic: Transcriptome
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.
PeerJ
Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating...
Applying AI/ML for Analyzing Gene Expression Patterns.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomics is not matching the levels others have achieved. Challenges include but are not limited to the ha...
Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.
Schizophrenia bulletin
Dec 20, 2024
BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (...
Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.
The Journal of clinical endocrinology and metabolism
Dec 18, 2024
CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.
AI techniques have facilitated the understanding of epitranscriptome distribution.
Cell genomics
Dec 11, 2024
N-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al. designed a novel Transformer-BiGRU framew...
Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.
Briefings in functional genomics
Dec 6, 2024
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing...
Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression.
Briefings in bioinformatics
Nov 22, 2024
Deciphering the cellular abundance in spatial transcriptomics (ST) is crucial for revealing the spatial architecture of cellular heterogeneity within tissues. However, some of the current spatial sequencing technologies are in low resolutions, leadin...
Predicting transcriptional changes induced by molecules with MiTCP.
Briefings in bioinformatics
Nov 22, 2024
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug di...
Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.
Briefings in bioinformatics
Nov 22, 2024
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microe...
Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.
Briefings in bioinformatics
Nov 22, 2024
Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant chall...
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