Impact of Treatment Strategies on Survival and Within Multivariate Predictive Model for Renal Cell Carcinoma Based on the SEER Database: A Retrospective Cohort Study.

Background: This project aims to shed light on how various treatment approaches affect RCC patients' chances of survival and create a prediction model for them.

Methods: Data from the Surveillance, Epidemiology, and End Results database were used in this investigation. OS and RCSS after radiation, chemotherapy, and surgery were investigated using the Kaplan-Meier approach.

A deep learning drug screening framework for integrating local-global characteristics: A novel attempt for limited data.

At the beginning of the "Disease X" outbreak, drug discovery and development are often challenged by insufficient and unbalanced data. To address this problem and maximize the information value of limited data, we propose a drug screening model, LGCNN, based on convolutional neural network (CNN), which enables rapid drug screening by integrating features of drug molecular structures and drug-target interactions at both local and global (LG) levels. Experimental results show that LGCNN exhibits better performance compared to other state-of-the-art classification methods under limited data.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma.

Background: Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes.

Background: The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers.

Methods: We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature.

Predicting hepatocellular carcinoma through cross-talk genes identified by risk pathways.

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers.

Prediction on the risk population of idiosyncratic adverse reactions based on molecular docking with mutant proteins.

Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process.