AIMC Topic: Machine Learning

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Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective To build a prostate cancer (PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence (AI) technology under heal...

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical featur...

Guided interactive image segmentation using machine learning and color-based image set clustering.

Bioinformatics (Oxford, England)
MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understandi...

Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data.

Bioinformatics (Oxford, England)
MOTIVATION: In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary coll...

The Future of Causal Inference.

American journal of epidemiology
The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high...

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Briefings in bioinformatics
Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. How...

A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction.

Briefings in bioinformatics
Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have bee...

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, di...

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...