AIMC Topic: Algorithms

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Some remarks on prediction of protein-protein interaction with machine learning.

Medicinal chemistry (Shariqah (United Arab Emirates))
Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of...

Existence and uniform stability analysis of fractional-order complex-valued neural networks with time delays.

IEEE transactions on neural networks and learning systems
This paper deals with the problem of existence and uniform stability analysis of fractional-order complex-valued neural networks with constant time delays. Complex-valued recurrent neural networks is an extension of real-valued recurrent neural netwo...

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

Methods in molecular biology (Clifton, N.J.)
Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion tha...

Jump neural network for real-time prediction of glucose concentration.

Methods in molecular biology (Clifton, N.J.)
Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T...

Developing a multimodal biometric authentication system using soft computing methods.

Methods in molecular biology (Clifton, N.J.)
Robust personal authentication is becoming ever more important in computer-based applications. Among a variety of methods, biometric offers several advantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard ...

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.)
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...

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...

Towards an expert system for accurate diagnosis and progress monitoring of Parkinson's disease.

Advances in experimental medicine and biology
While Parkinson's disease is a chronic and progressive movement disorder, no one can predict which symptoms will affect an individual patient. At the present time there is no cure for Parkinson's disease but instead a variety of alternative treatment...

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities.

Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images.

Neural computation
Deep learning has traditionally been computationally expensive, and advances in training methods have been the prerequisite for improving its efficiency in order to expand its application to a variety of image classification problems. In this letter,...