The GoSPGF-GoPGF-GhWRKYs transcriptional cascade positively regulates pigment gland formation in the stem of upland cotton.

The pigment gland in cotton (Gossypium hirsutum) is a specialized structure that serves as a reservoir for gossypol and its derivatives. The morphogenesis of the pigment gland is complex and involves sophisticated but poorly understood interactions among genes that regulate cell differentiation and development. In this study, we identified a distinct gland/gossypol cluster which includes a key regulator of cotton pigment gland formation, GoPGF, and elucidated the developmental trajectory of gland cells through spatial transcriptome and single-cell RNA integrative analysis.

iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states.

iGTP: Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states.

METTL14 is a chromatin regulator independent of its RNA N6-methyladenosine methyltransferase activity.

METTL3 and METTL14 are two components that form the core heterodimer of the main RNA m6A methyltransferase complex (MTC) that installs m6A. Surprisingly, depletion of METTL3 or METTL14 displayed distinct effects on stemness maintenance of mouse embryonic stem cell (mESC). While comparable global hypo-methylation in RNA m6A was observed in Mettl3 or Mettl14 knockout mESCs, respectively.

Pancancer analysis of a potential gene mutation model in the prediction of immunotherapy outcomes.

Immune checkpoint blockade (ICB) represents a promising treatment for cancer, but predictive biomarkers are needed. We aimed to develop a cost-effective signature to predict immunotherapy benefits across cancers. We proposed a study framework to construct the signature.

Deep graph convolutional network for US birth data harmonization.

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more information were more similar to each other.

A deep learning solution to recommend laboratory reduction strategies in ICU.

Objective: To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations.

Materials And Methods: We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care database (MIMIC III), which includes 4,570,709 observations of 12 standard laboratory tests, among 38,773 critical care patients.

Developing an automated mechanism to identify medical articles from wikipedia for knowledge extraction.

Wikipedia contains rich biomedical information that can support medical informatics studies and applications. Identifying the subset of medical articles of Wikipedia has many benefits, such as facilitating medical knowledge extraction, serving as a corpus for language modeling, or simply making the size of data easy to work with. However, due to the extremely low prevalence of medical articles in the entire Wikipedia, articles identified by generic text classifiers would be bloated by irrelevant pages.

Predict or draw blood: An integrated method to reduce lab tests.

Serial laboratory testing is common, especially in Intensive Care Units (ICU). Such repeated testing is expensive and may even harm patients. However, identifying specific tests that can be omitted is challenging.