AIMC Topic: Artificial Intelligence

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Improved feature-based prediction of SNPs in human cytochrome P450 enzymes.

Interdisciplinary sciences, computational life sciences
Single nucleotide polymorphisms (SNPs) make up the most common form of mutations in human cytochrome P450 enzymes family, and have the potential to bring with different drug responses or specific diseases in individual patients. Here, based on machin...

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.

Environmental monitoring and assessment
Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The pres...

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Computational biology and chemistry
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene...

Sentiment analysis using common-sense and context information.

Computational intelligence and neuroscience
Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the r...

Kernel temporal differences for neural decoding.

Computational intelligence and neuroscience
We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. T...

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BMC psychiatry
BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivor...

Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification.

IEEE transactions on neural networks and learning systems
In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as...

Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum.

Nucleic acids research
Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-...

A fast clustering algorithm for data with a few labeled instances.

Computational intelligence and neuroscience
The diameter of a cluster is the maximum intracluster distance between pairs of instances within the same cluster, and the split of a cluster is the minimum distance between instances within the cluster and instances outside the cluster. Given a few ...