AIMC Topic: Transcriptome
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Deep learning in integrating spatial transcriptomics with other modalities.
Briefings in bioinformatics
Nov 22, 2024
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histolog...
Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.
Briefings in bioinformatics
Nov 22, 2024
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchi...
A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.
Briefings in bioinformatics
Nov 22, 2024
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particu...
Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.
Briefings in bioinformatics
Nov 22, 2024
Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model-based chatbots offer promising solutions by le...
Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.
Journal of cellular and molecular medicine
Nov 1, 2024
The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS...
Unveiling the glycolysis in sepsis: Integrated bioinformatics and machine learning analysis identifies crucial roles for IER3, DSC2, and PPARG in disease pathogenesis.
Medicine
Sep 27, 2024
Sepsis, a multifaceted syndrome driven by an imbalanced host response to infection, remains a significant medical challenge. At its core lies the pivotal role of glycolysis, orchestrating immune responses especially in severe sepsis. The intertwined ...
scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.
Briefings in bioinformatics
Sep 23, 2024
The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, ...
Deep contrastive learning for predicting cancer prognosis using gene expression values.
Briefings in bioinformatics
Sep 23, 2024
Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor trans...
m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.
Briefings in bioinformatics
Sep 23, 2024
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing ...
Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.
Journal of cellular and molecular medicine
Sep 1, 2024
This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing a...
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