Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network

This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorectal cancer (CRC) by near-infrared (NIR) spectroscopy in conjunction with counter propagation artificial neural network (CP-ANN). The NIR spectral data from 104 paraffin-embedded CRC tissue samples consisting of an equal number of the BRAF V600E mutant and wild-type ones calibrated and validated the CP-ANN model. As a result, the CP-ANN model had the classification accuracy of calibration (CAC) 98.0%, cross-validation (CACV) 95.0% and validation (CAV) 94.4%. When used to detect the BRAF V600E mutation in CRC, the model showed a diagnostic sensitivity of 100.0%, a diagnostic specificity of 87.5%, and a diagnostic accuracy of 93.8%. Moreover, this method was proven to distinguish the BRAF V600E mutant from the wild type based on intrinsic differences by using a total of 312 CRC tissue samples paraffin-embedded, deparaffinized, and stained. The novel method can be used for the auxiliary diagnosis of the BRAF V600E mutation in CRC. This work can expand the application of NIR spectroscopy in the auxiliary diagnosis of gene mutation in human cancer.

Application of counter propagation artificial neural network for classification and genetic diversity assessment of somePseudomonasspecies

Members of the genus Pseudomonas bacterium are of great interest because of their importance in plant disease. In this study, DNA fingerprints of 60 strains of Pseudomonas bacteria including three species of Pseudomonas syringae (Pseudomonas syringae pv. syringae (Pss) and Pseudomonas syringae pv. Lachrymans (Psl)), Pseudomonas savastanoi (Psa) and Pseudomonas tolaasii (Pt) were used for developing a robust predictive classification model. The DNA fingerprints were obtained by repetitive polymerase chain reaction (Rep-PCR) using enterobacterial repetitive intergenic consensus (ERIC), repetitive extragenic palindromes (REP), and BOXAIR primers. The classification results of counter propagation artificial neural network (CP-ANN) modeling indicated that a combination of Rep-PCR fingerprinting and chemometrics analysis can be used as an effective and powerful methodology to differentiate species of Pseudomonas and pathovars of P. syringae strains based on a predictive model.

Estudio de la Relación Cuantitativa Estructura-Actividad de pesticidas mediante técnicas de clasificación

El objetivo de este trabajo fue la comparación entre los métodos de clasificación del vecino más cercano (κ-NN) y las redes neuronales artificiales de contrapropagación (CP-ANN) para modelar la toxicidad de un conjunto de 192 pesticidas organoclorados, organofosforados, carbamatos y piretroides, medidos como Concentración Efectiva (EC50) y que fueron divididos en tres clases, es decir, baja, intermedia y alta toxicidad. Se calcularon 4885 descriptores moleculares usando el programa DRAGON, los que fueron simultáneamente analizados mediante el método κ-NN acoplado con la técnica de selección de variables de los Algoritmos Genéticos (GA-VSS). Los modelos fueron apropiadamente validados mediante un subconjunto de predicción. Los resultados claramente sugieren que los descriptores 3D no ofrecen información relevante para modelar las clases. Por otro lado, κ-NN muestra mejores resultados que CP-ANN.