ribaoo Research Daily

RISK / BOUNDARY

PathMED: An R toolkit for single-sample molecular scoring and machine learning with omics data.

Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article. Supplementary data are available at Bioinformatics online.

Evidence boundary: 工具论文展示的是可复现分析框架；其临床预测价值仍取决于具体数据、验证设计和部署环境。

Source: PMID:42496598

DAILY LEAD

ELF3 links kidney function GWAS loci to maladaptive epithelial inflammation.

Weekend observation (25 July 2026, Asia/Shanghai): no usable routine new-paper batch was visible. This issue only summarizes the latest visible PubMed Date-Publication records from 2026-07-24 (24 July 2026); their source date is not a new-publication claim for the observation date.

ribaoo research radar

Genome-wide association studies (GWAS) have uncovered over a thousand loci associated with kidney function, but the effector genes and mechanisms remain largely unknown. Here, we demonstrate that ELF3 is the effector gene at a kidney function GWAS locus and acts as an epithelial proinflammatory amplifier of the reprogramming of injured proximal tubules (iPTs). E74-like factor 3 (ELF3) expression is induced in iPTs in both mouse models and human chronic kidney disease (CKD), where it defines transitional epithelial states enriched for inflammatory gene programs and surrounded by leukocyte infiltrates. Conditional deletion of Elf3 in mice after injury reduced iPT accumulation, cytokine production, and immune cell recruitment. Mechanistically, ELF3 directly bound and activated components of the noncanonical nuclear factor κB (NF-κB) pathway (Nfκb2, Map3k14, and Il6r) and was required for NFκB2 nuclear translocation and cytokine induction. Spatial transcriptomics and immunofluorescence of human CKD kidneys confirmed that ELF3 expression correlates with epithelial inflammation, disease progression, and loss of kidney function. Together, these findings establish ELF3 as a genetically validated effector gene that drives inflammatory reprogramming in iPTs, nominating it as a therapeutic target to blunt maladaptive inflammation in CKD.

Evidence boundary: 小鼠干预与人组织相关性共同支持机制假说，但ELF3仍是候选治疗靶点，不是现成临床疗法。

Source: PMID:42497247

Observation

2026-07-25

Asia/Shanghai · Saturday

Routine batch

not visible

PubMed E-utilities server 500; no old record relabelled

Latest visible

2026-07-24 PubMed Date-Publication

Selected

latest visible primary sources

METHOD / NEXT

Recurrent evolution of cryptic triploids in cultivated enset increases yield.

The high incidence of polyploidy in crops could be explained if domestication involved the selection of polyploids (cytotypes with more than two sets of chromosomes). Ancestral horticulture could have targeted desirable traits including yield, robustness to stress or disease, rather than requiring knowledge of polyploidy itself. We find evidence for this process underway in enset (Ensete ventricosum, Musaceae), a highly resilient crop constituting the main staple for over 20 million people in Ethiopia, which is clonally propagated and cultivated for its starch-rich corm and pseudostem. Prior to this study, enset was thought to be exclusively diploid (2n = 2x = 18). Using a newly-assembled chromosome-scale reference genome, and sequence data from 723 wild and domesticated enset individuals from southwestern Ethiopia, we demonstrate that around 20% of cultivated enset clones are triploid. We show that triploidy has arisen multiple times independently, that the triploid lineages have been given distinct landrace names, and planted disproportionately frequently. Enset triploid clones also have a higher pseudostem volume than diploids on average. As well as providing evidence for enduring selection on triploids, our results reveal valuable genetic diversity captured by these triploid lines. They represent key resources for scientifically directed breeding of this major crop in the Ethiopian agrosystem, and could improve food security in Sub-Saharan Africa.

Evidence boundary: 产量相关观察并不自动证明每个三倍体都更优；育种与粮食安全价值仍需田间与遗传验证。

Source: PMID:42497221