MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased...
The bias-variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias-variance ...
BACKGROUND: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predict...
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2023
BACKGROUND: To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring...
Zhonghua wai ke za zhi [Chinese journal of surgery]
Jan 1, 2023
As a severe malignant tumor of the digestive system,the highly invasive pancreatic cancer lacks typical preliminary symptoms. Rapid metastatic dissemination and difficulty in early-stage diagnosis preclude the chance of radical curative resection,hen...
Since the introduction of artificial intelligence (AI) in 1956 by John McCarthy, the field has propelled medicine, optimized efficiency, and led to technological breakthroughs in clinical care. As an important frontier in healthcare, AI has implicati...
MOTIVATION: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. ...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural network th...
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