Optimization of indirect wastewater characterization using led spectrophotometry: a comparative analysis of regression, scaling, and dimensionality reduction methods.

LED spectrophotometry is a robust technique for the indirect characterization of wastewater pollutant load through correlation modeling. To tackle this issue, a dataset with 1300 samples was collected, from both raw and treated wastewater from 45 wastewater treatment plants in Spain and Chile collected over 4 years. The type of regressor, scaling, and dimensionality reduction technique and nature of the data play crucial roles in the performance of the processing pipeline. Eighty-four pipelines were tested through exhaustive experimentation resulting from the combination of 7 regression techniques, 3 scaling methods, and 4 possible dimensional reductions. Those combinations were tested on the prediction of chemical oxygen demand (COD) and total suspended solids (TSS). Each pipeline underwent a tenfold cross-validation on 15 sub-datasets derived from the original dataset, accounting for variations in plants and wastewater types. The results point to the normalization of the data followed by a conversion through the PCA to finally apply a Random Forest Regressor as the combination which stood out These results highlight the importance of modeling strategies in wastewater management using techniques such as LED spectrophotometry.

Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models.

Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique-based on successive deformation, search, fitting, filtering, and interpolation stages-is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.

Evolutionary computation and QSAR research.

The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.

Biomedical data integration in computational drug design and bioinformatics.

In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.

Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks.

About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings can provide valuable information for understanding the mechanisms behind epileptic disorders. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Wavelet transform (WT) is an effective analysis tool for non-stationary signals, such as EEGs. The line length feature reflects the waveform dimensionality changes and is a measure sensitive to variation of the signal amplitude and frequency. This paper presents a novel method for automatic epileptic seizure detection, which uses line length features based on wavelet transform multiresolution decomposition and combines with an artificial neural network (ANN) to classify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A famous public dataset was used to evaluate the proposed method. The high accuracy obtained for three different classification problems testified the great success of the method.

Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia.

Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain) schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype-Disease Relationships) that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3-93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network.

Drug discovery and design for complex diseases through QSAR computational methods.

There is a need for a study of the complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors. This review presents the most recent studies on drug discovery and design using QSAR of several complex diseases in the fields of Neurology, Cardiology and Oncology.

Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

A new approach to the extraction of ANN rules and to their generalization capacity through GP.

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.