Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognos...
Essential genes are vitally important to the survival and reproduction of organisms. Many machine learning methods have been widely employed to predict essential genes and have obtained satisfactory results. However, most of these methods are supervi...
Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-o...
In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the num...
There has been a lot of research on supervised learning in spiking neural network (SNN) for a couple of decades to improve computational efficiency. However, evolutionary algorithm based supervised learning for SNN has not been investigated thoroughl...
PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on...
This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM...
We present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) a...
Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discov...
Neural networks : the official journal of the International Neural Network Society
Jul 18, 2020
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), howeve...