Frontiers in bioscience (Landmark edition)
Mar 16, 2022
This study reviews the recent progress of machine learning for the early diagnosis of thyroid disease. Based on the results of this review, different machine learning methods would be appropriate for different types of data for the early diagnosis of...
BACKGROUND AND AIMS: There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using ro...
BACKGROUND: With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a con...
New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within th...
Although sifting functional genes has been discussed for years, traditional selection methods tend to be ineffective in capturing potential specific genes. First, typical methods focus on finding features (genes) relevant to class while irrelevant to...
Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a ...
Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to pred...
Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins th...
Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical ex...
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