Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

PRPS-ST: A protocol-agnostic self-training method for gene expression-based classification of blood cancers.

ABSTRACT: Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing data sets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression data sets. In comparison to gold standards, we demonstrate favorable performance of PRPS-ST in gene expression-based classification of DLBCL and B-ALL using a diverse variety of gene expression data types and pre-processing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.

SUBMITTER: Jiang A

PROVIDER: S-EPMC7774870 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Tumour-agnostic drugs in paediatric cancers.

Project description:The recognition that new cancer drugs can be truly tumour-agnostic based on mechanism-of-action is important for paediatric cancers, where access to novel targeted therapies developed for adult indications has sometimes been problematic. The recently approved drug larotrectinib is an excellent case study of the development of a tumour-agnostic drug relevant to children.

| S-EPMC7217925 | biostudies-literature

The Papaver self-incompatibility pollen S-determinant, PrpS, functions in Arabidopsis thaliana.

Project description:Many angiosperms use specific interactions between pollen and pistil proteins as "self" recognition and/or rejection mechanisms to prevent self-fertilization. Self-incompatibility (SI) is encoded by a multiallelic S locus, comprising pollen and pistil S-determinants. In Papaver rhoeas, cognate pistil and pollen S-determinants, PrpS, a pollen-expressed transmembrane protein, and PrsS, a pistil-expressed secreted protein, interact to trigger a Ca(2+)-dependent signaling network, resulting in inhibition of pollen tube growth, cytoskeletal alterations, and programmed cell death (PCD) in incompatible pollen. We introduced the PrpS gene into Arabidopsis thaliana, a self-compatible model plant. Exposing transgenic A. thaliana pollen to recombinant Papaver PrsS protein triggered remarkably similar responses to those observed in incompatible Papaver pollen: S-specific inhibition and hallmark features of Papaver SI. Our findings demonstrate that Papaver PrpS is functional in a species with no SI system that diverged ~140 million years ago. This suggests that the Papaver SI system uses cellular targets that are, perhaps, common to all eudicots and that endogenous signaling components can be recruited to elicit a response that most likely never operated in this species. This will be of interest to biologists interested in the evolution of signaling networks in higher plants.

| S-EPMC3695568 | biostudies-literature

Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos.

Project description:The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall.

| S-EPMC7766150 | biostudies-literature

Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma.

Project description:Osteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, a convolutional neural network was adversarially trained to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. Adversarial training improved domain adaption of a histologic subtype classifier from canines to humans, achieving an average multiclass F1 score of 0.77 (95% CI, 0.74-0.79) and 0.80 (95% CI, 0.78-0.81) when compared with the ground truth in canines and humans, respectively. Finally, this trained model, when used to characterize the histologic landscape of 306 canine OSs, uncovered distinct clusters with markedly different clinical responses to standard-of-care therapy.

| S-EPMC9798510 | biostudies-literature

A self-supervised deep learning method for data-efficient training in genomics.

Project description:Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models.

| S-EPMC10495322 | biostudies-literature

Filamentation modulates allosteric regulation of PRPS.

Project description:Phosphoribosyl pyrophosphate (PRPP) is a key intermediate in the biosynthesis of purine and pyrimidine nucleotides, histidine, tryptophan, and cofactors NAD and NADP. Abnormal regulation of PRPP synthase (PRPS) is associated with human disorders, including Arts syndrome, retinal dystrophy, and gouty arthritis. Recent studies have demonstrated that PRPS can form filamentous cytoophidia in eukaryotes. Here, we show that PRPS forms cytoophidia in prokaryotes both in vitro and in vivo. Moreover, we solve two distinct filament structures of E. coli PRPS at near-atomic resolution using Cryo-EM. The formation of the two types of filaments is controlled by the binding of different ligands. One filament type is resistant to allosteric inhibition. The structural comparison reveals conformational changes of a regulatory flexible loop, which may regulate the binding of the allosteric inhibitor and the substrate ATP. A noncanonical allosteric AMP/ADP binding site is identified to stabilize the conformation of the regulatory flexible loop. Our findings not only explore a new mechanism of PRPS regulation with structural basis, but also propose an additional layer of cell metabolism through PRPS filamentation.

| S-EPMC9232217 | biostudies-literature

CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method.

Project description:With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

| S-EPMC8705865 | biostudies-literature

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations.

Project description:Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

| S-EPMC7446154 | biostudies-literature

PRPS polymerization influences lens fiber organization in zebrafish.

Project description:BackgroundThe self-assembly of metabolic enzymes into filaments or foci highlights an intriguing mechanism for the regulation of metabolic activity. Recently, we identified the conserved polymerization of phosphoribosyl pyrophosphate synthetase (PRPS), which catalyzes the first step in purine nucleotide synthesis, in yeast and cultured mammalian cells. While previous work has revealed that loss of PRPS activity regulates retinal development in zebrafish, the extent to which PRPS filament formation affects tissue development remains unknown.ResultsBy generating novel alleles in the zebrafish PRPS paralogs, prps1a and prps1b, we gained new insight into the role of PRPS filaments during eye development. We found that mutations in prps1a alone are sufficient to generate abnormally small eyes along with defects in head size, pigmentation, and swim bladder inflation. Furthermore, a loss-of-function mutation that truncates the Prps1a protein resulted in the failure of PRPS filament assembly. Lastly, in mutants that fail to assemble PRPS filaments, we observed disorganization of the actin network in the lens fibers.ConclusionsThe truncation of Prps1a blocked PRPS filament formation and resulted in a disorganized lens fiber actin network. Altogether, these findings highlight a potential role for PRPS filaments during lens fiber organization in zebrafish.

| S-EPMC8370018 | biostudies-literature

<i>RET</i>-Altered Cancers-A Tumor-Agnostic Review of Biology, Diagnosis and Targeted Therapy Activity.

Project description:RET alterations, such as fusions or mutations, drive the growth of multiple tumor types. These alterations are found in canonical (lung and thyroid) and non-canonical (e.g., gastrointestinal, breast, gynecological, genitourinary, histiocytic) cancers. RET alterations are best identified via comprehensive next-generation sequencing, preferably with DNA and RNA interrogation for fusions. Targeted therapies for RET-dependent cancers have evolved from older multikinase inhibitors to selective inhibitors of RET such as selpercatinib and pralsetinib. Prospective basket trials and retrospective reports have demonstrated the activity of these drugs in a wide variety of RET-altered cancers, notably those with RET fusions. This paved the way for the first tumor-agnostic selective RET inhibitor US FDA approval in 2022. Acquired resistance to RET kinase inhibitors can take the form of acquired resistance mutations (e.g., RET G810X) or bypass alterations.

| S-EPMC10452615 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data