AIMC Topic: Algorithms

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Variable structure controller design for Boolean networks.

Neural networks : the official journal of the International Neural Network Society
The paper investigates the variable structure control for stabilization of Boolean networks (BNs). The design of variable structure control consists of two steps: determine a switching condition and determine a control law. We first provide a method ...

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

International journal of neural systems
One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated m...

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompati...

Modular representation of layered neural networks.

Neural networks : the official journal of the International Neural Network Society
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge f...

A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

Scientific reports
Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignor...

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

BMC bioinformatics
BACKGROUND: The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this c...

Noninvasive Evaluation of Portal Hypertension Using a Supervised Learning Technique.

Journal of healthcare engineering
Portal hypertension (PHT) is a key event in the evolution of different chronic liver diseases and leads to the morbidity and mortality of patients. The traditional reliable PHT evaluation method is a hepatic venous pressure gradient (HVPG) measuremen...

The trade-off between morphology and control in the co-optimized design of robots.

PloS one
Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can robotic...

Autonomous dynamic obstacle avoidance for bacteria-powered microrobots (BPMs) with modified vector field histogram.

PloS one
In order to broaden the use of microrobots in practical fields, autonomous control algorithms such as obstacle avoidance must be further developed. However, most previous studies of microrobots used manual motion control to navigate past tight spaces...

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

Magnetic resonance in medicine
PURPOSE: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors.