AIMC Topic: Artificial Intelligence

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The application of machine learning to the modelling of percutaneous absorption: an overview and guide.

SAR and QSAR in environmental research
Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specific...

Role of moving average analysis for development of multi-target (Q)SAR models.

Mini reviews in medicinal chemistry
In modern drug discovery era, multi target- quantitative structure activity relationship [mt- (Q)SAR] approaches have emerged as novel and powerful alternatives in the field of in-silico drug design so as to facilitate the discovery of new chemical e...

An integrated machine-learning model to predict prokaryotic essential genes.

Methods in molecular biology (Clifton, N.J.)
Essential genes are indispensable for the target organism's survival. Large-scale identification and characterization of essential genes has shown to be beneficial in both fundamental biology and medicine fields. Current existing genome-scale experim...

Computer-based prediction of mitochondria-targeting peptides.

Methods in molecular biology (Clifton, N.J.)
Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where t...

Multiple hypotheses image segmentation and classification with application to dietary assessment.

IEEE journal of biomedical and health informatics
We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned...

Feature selection using a neural framework with controlled redundancy.

IEEE transactions on neural networks and learning systems
We first present a feature selection method based on a multilayer perceptron (MLP) neural network, called feature selection MLP (FSMLP). We explain how FSMLP can select essential features and discard derogatory and indifferent features. Such a method...

Definition of loss functions for learning from imbalanced data to minimize evaluation metrics.

Methods in molecular biology (Clifton, N.J.)
Most learning algorithms for classification use objective functions based on regularized and/or continuous versions of the 0-1 loss function. Moreover, the performance of the classification models is usually measured by means of the empirical error o...

Epistasis analysis using artificial intelligence.

Methods in molecular biology (Clifton, N.J.)
Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of exper...

Conditional density estimation with dimensionality reduction via squared-loss conditional entropy minimization.

Neural computation
Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case, estimating the conditional density itse...

Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis.

Journal of computational biology : a journal of computational molecular cell biology
Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocellular carcinoma (HCC) is the main type of liver cancer. We applied a machine learning approach with maximum-relevance-minimum-redundancy (mRMR) algorithm followed by in...