Global transcriptional profiles of hiPSC-derived hypothalamus-pituitary organoids predicted to fail or succeed by machine learning model
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE301692 | GEO | 2025/07/03
REPOSITORIES: GEO
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Project description:Global transcriptional profiles of hiPSC-derived hypothalamus-pituitary organoids predicted to fail or succeed by machine learning model
| PRJNA1285990 | ENA
Project description:Interventions: Gold Standard:Pathological diagnosis of resected intact sample.;Index test:Artificial intelligent convolution neural network
Primary outcome(s): Colonoscopic images
Study Design: Cross-sectional
| 80163 | ecrin-mdr-crc
Project description:ObjectiveNo accurate predictive models were identified for hormonal prognosis in non-functioning pituitary adenoma (NFPA). This study aimed to develop machine learning (ML) models to facilitate the prognostic assessment of pituitary hormonal outcomes after surgery.MethodsA total of 215 male patients with NFPA, who underwent surgery in four medical centers from 2015 to 2021, were retrospectively reviewed. The data were pooled after heterogeneity assessment, and they were randomly divided into training and testing sets (172:43). Six ML models and logistic regression models were developed using six anterior pituitary hormones.ResultsOnly thyroid-stimulating hormone (p < 0.001), follicle-stimulating hormone (p < 0.001), and prolactin (PRL; p < 0.001) decreased significantly following surgery, whereas growth hormone (GH) (p < 0.001) increased significantly. The postoperative GH (p = 0.07) levels were slightly higher in patients with gross total resection, but the PRL (p = 0.03) level was significantly lower than that in patients with subtotal resection. The optimal model achieved area-under-the-receiver-operating-characteristic-curve values of 0.82, 0.74, and 0.85 in predicting hormonal hypofunction, new deficiency, and hormonal recovery following surgery, respectively. According to feature importance analyses, the preoperative levels of the same type and other hormones were all important in predicting postoperative individual hormonal hypofunction.ConclusionFluctuation in anterior pituitary hormones varies with increases and decreases because of transsphenoidal surgery. The ML models could accurately predict postoperative pituitary outcomes based on preoperative anterior pituitary hormones in NFPA.
| S-EPMC8529112 | biostudies-literature
Project description:Humor research is supposed to predict whether something is funny. According to its theories and observations, amusement should be predictable based on a wide variety of variables. We test the practical value of humor appreciation research in terms of prediction accuracy. We find that machine learning methods (boosted decision trees) can indeed predict humor appreciation with an accuracy close to its theoretical ceiling. However, individual demographic and psychological variables, while replicating previous statistical findings, offer only negligible gains in accuracy. Successful predictions require previous ratings by the same rater, unless highly specific interactions between rater and joke content can be assessed. We discuss implications for humor research, and offer advice for practitioners designing content recommendations engines or entertainment platforms, as well as other research fields aiming to review their practical usefulness.
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Project description:The size of composition space means even coarse grid-based searches for interesting alloys are infeasible unless heavily constrained, which requires prior knowledge and reduces the possibility of making novel discoveries. Genetic algorithms provide a practical alternative to brute-force searching, by rapidly homing in on fruitful regions and discarding others. Here, we apply the genetic operators of competition, recombination, and mutation to a population of trial alloy compositions, with the goal of evolving towards candidates with excellent glass-forming ability, as predicted by an ensemble neural-network model. Optimization focuses on the maximum casting diameter of a fully glassy rod, D max, the width of the supercooled region, ΔT x, and the price-per-kilogramme, to identify commercially viable novel glass-formers. The genetic algorithm is also applied with specific constraints, to identify novel aluminium-based and copper-zirconium-based glass-forming alloys, and to optimize existing zirconium-based alloys.
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Project description:Background: Barrett’s esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic haematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n=15), LGD non-progressive (n=14), LGD progressive (n=14), and HGD (n=14). MSI experiments were conducted at 50x50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches.
2021-11-15 | PXD028949 | Pride
Project description:We developed ML-based methods to optimize PE design
2021-07-26 | GSE175955 | GEO
Project description:Machine Learning-based Mascot Rescoring to Reduce False Positives in Tumor Immunopeptidomics using Mass Spectrometry
2024-08-25 | PXD044852 | JPOST Repository
Project description:Selective inhibitors are essential for targeted therapeutics and for probing enzyme functions in various biological systems. The two main challenges in identifying such inhibitors lie in the extensive experimental effort required, including the generation of large libraries, and in tailoring the selectivity of inhibitors to enzymes with homologous structures. To address these challenges, machine learning (ML) is being used to improve protein design by training on targeted libraries and identifying key interface mutations that enhance affinity and specificity. However, such ML-based methods are limited by inaccurate energy calculations and difficulties in predicting the structural impacts of multiple mutations. Here, we present an ML-based method that leverages HTS data to streamline the design of selective inhibitors. To demonstrate its utility, we applied our new method to finding inhibitors of matrix metalloproteinases (MMPs), a family of homologous enzymes involved in both physiological and pathological processes. By training ML models on binding data for three MMPs (MMP-1, MMP-3, and MMP-9), we successfully designed a novel N-TIMP2 variant with a differential specificity profile, namely, high affinity for MMP-9, moderate affinity for MMP-3, and low affinity for MMP-1. Our experimental validation showed that this novel variant exhibited a significant specificity shift and enhanced selectivity compared to wild-type N-TIMP2. Through molecular modeling and energy minimization, we obtained structural insights into the variant’s enhanced selectivity. Our findings highlight the power of ML-based methods to reduce experimental workloads, facilitate the rational design of selective inhibitors, and advance the understanding of specific inhibitor-enzyme interactions in homologous enzyme systems.
2025-03-10 | GSE290918 | GEO
Project description:Purpose: To detect the presence of antipituitary (APA) and antihypothalamus antibodies (AHA) in subjects treated for brain cancers, and to evaluate their potential association with pituitary dysfunction. Methods: We evaluated 63 patients with craniopharyngioma, glioma, and germinoma treated with surgery and/or radiotherapy and/or chemotherapy at a median age of 13 years. Forty-one had multiple pituitary hormone deficiencies (MPHD), six had a single pituitary defect. GH was the most common defect (65.1%), followed by AVP (61.9%), TSH (57.1%), ACTH (49.2%), and gonadotropin (38.1%). APA and AHA were evaluated by simple indirect immunofluorescence method indirect immunofluorescence in patients and in 50 healthy controls. Results: Circulating APA and/or AHA were found in 31 subjects (49.2%) and in none of the healthy controls. In particular, 25 subjects out of 31 were APA (80.6%), 26 were AHA (83.90%), and 20 were both APA and AHA (64.5%). Nine patients APA and/or AHA have craniopharyngioma (29%), seven (22.6%) have glioma, and 15 (48.4%) have germinoma. Patients with craniopharyngioma were positive for at least one antibody in 39.1% compared to 33.3% of patients with glioma and to 78.9% of those with germinoma with an analogous distribution for APA and AHA between the three tumors. The presence of APA or AHA and of both APA and AHA was significantly increased in patients with germinoma. The presence of APA (P = 0.001) and their titers (P = 0.001) was significantly associated with the type of tumor in the following order: germinomas, craniopharyngiomas, and gliomas; an analogous distribution was observed for the presence of AHA (P = 0.002) and their titers (P = 0.012). In addition, we found a significant association between radiotherapy and APA (P = 0.03). Conclusions: Brain tumors especially germinoma are associated with the development of hypothalamic-pituitary antibodies and pituitary defects. The correct interpretation of APA/AHA antibodies is essential to avoid a misdiagnosis of an autoimmune infundibulo-neurohypophysitis or pituitary hypophysitis in patients with germinoma.
| S-EPMC7040196 | biostudies-literature