AIMC Topic: Algorithms

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Forecasting nanoparticle toxicity using nonlinear predictive regressor learning systems.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nanoparticle (NP) toxicity is determined by a vast number of topological, sterical, physico-chemical as well as biological properties, rendering a priori evaluation of the effect of NP on biological tissue as arduous as it is necessary and urgent. We...

Difficulty understanding speech in noise by the hearing impaired: underlying causes and technological solutions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A primary complaint of hearing-impaired individuals involves poor speech understanding when background noise is present. Hearing aids and cochlear implants often allow good speech understanding in quiet backgrounds. But hearing-impaired individuals a...

Intelligent hearing aids: the next revolution.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The first revolution in hearing aids came from nonlinear amplification, which allows better compensation for both soft and loud sounds. The second revolution stemmed from the introduction of digital signal processing, which allows better programmabil...

Robotic path-finding in inverse treatment planning for stereotactic radiosurgery with continuous dose delivery.

Medical physics
PURPOSE: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an i...

Artificial Neural Network Approach in Laboratory Test Reporting:  Learning Algorithms.

American journal of clinical pathology
OBJECTIVES: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would e...

MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine.

Molecular bioSystems
Knowledge of the subcellular location (SCL) of viral proteins in the host cell is important for understanding their function in depth. Therefore, we have developed "MSLVP", a two-tier prediction algorithm for predicting multiple SCLs of viral protein...

A fuzzy logic controller based approach to model the switching mechanism of the mammalian central carbon metabolic pathway in normal and cancer cells.

Molecular bioSystems
Dynamics of large nonlinear complex systems, like metabolic networks, depend on several parameters. A metabolic pathway may switch to another pathway in accordance with the current state of parameters in both normal and cancer cells. Here, most of th...

Classifying and segmenting microscopy images with deep multiple instance learning.

Bioinformatics (Oxford, England)
MOTIVATION: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on...

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Bioinformatics (Oxford, England)
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...

Fast metabolite identification with Input Output Kernel Regression.

Bioinformatics (Oxford, England)
MOTIVATION: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching t...