The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/ and can be utilized for all organisms.
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http://dx.doi.org/10.21203/rs.3.rs-5776937/v1 | DOI Listing |
Asian Pac J Cancer Prev
January 2025
D1 S.P. Botkin City Clinical Hospital, Moscow, Russia.
Objectives: To study the predictive role of tumor-associated neutrophils in early luminal HER2-negative breast cancer.
Materials And Methods: This is a retrospective study conducted on 60 women cases aged from 31 to 79 years underwent surgery for luminal HER2-negative ductal breast cancer in tertiary care cancer centre. We first estimated basic morphological signs: tumor size, tumor grade (by Nottingham Histologic Score), tumor infiltrating lymphocytes (TILs), Lymphovascular invasion, hormonal receptors status, proliferative index, and regional lymph nodes metastasis.
Trop Anim Health Prod
January 2025
Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, 38541, Republic of Korea.
To improve the quality and yield of the Korean beef industry, selection criteria often focus on estimated breeding values for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study estimated genetic parameters and assessed the accuracy of genomic estimated breeding values (GEBVs) using SNP weighting methods. We compared the accuracy of these methods with the genomic best linear unbiased prediction (GBLUP) and various Bayesian approaches (BayesA, BayesB, BayesC, and BayesCPi) for the specified traits.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Department of Laboratory, the Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Background: Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.
Methods: This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.
Vet Dermatol
January 2025
IVC Evidensia Referral Hospital, EDZ Arnhem, Arnhem, the Netherlands.
Background: Alopecia X (AX) is a common noninflammatory alopecic condition of Pomeranian dogs with an unknown cause. While a genetic aetiology is suspected, no current tests can predict the development of this disorder or confirm the aetiology. Therefore, identifying potential risk indicators for the development of AX would be of value in this breed.
View Article and Find Full Text PDFLife Med
April 2024
Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China.
Synthetic organ models such as organoids and organ-on-a-chip have been receiving recognition from administrative agencies. Despite the proven success of organoids in predicting drug efficacy on laboratory scales, their translational advances have not fully satisfied the expectations for both clinical implementation and commercial applications. The transition from laboratory settings to clinical applications continues to encounter challenges.
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