Fuzzy virtual ligands for virtual screening

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers

Conditional advancement of machine learning algorithm via fuzzy neural network

Improving overall performance is the ultimate goal of any machine learning (ML) algorithm. While it is a trivial task to explore multiple individual validation measurements, evaluating and monitoring overall performance can be complicated due to the highly nonlinear nature of the functions describing the relationships among different validation metrics, such as the Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Therefore, it is naturally desirable to have a reliable validation algorithm or model that can integrate all existing validation metrics into a single value. This consolidated metric would enable straightforward assessment of an ML algorithm’s performance and identify areas for improvement. To deal with such a complex nonlinear problem, this study suggests a novel parameterized model named Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which takes any set of input–output precise-imprecise data and uses a neuro-adaptive learning strategy to tune the parameters of the pre-defined membership functions. Our method can be accepted as an elegant and the state-of-the-art method for the nonlinear function approximation, which could be added directly to any convolutional neural networks (CNN) loss functions as the regularization term to generate a constrained-CNN-FUZZY model optimization. To demonstrate the ability of the purposed method and provide a practical explanation of the capability of ANFIS, we use deep CNN as a testing platform to consider the fact that one of the biggest challenges CNN-developers faced today is to reduce the mismatching between the provided input data and the predicted results monitored by different validation metrics. We first create a toy dataset using MNIST and investigate the properties of the proposed model. We then use a medical dataset to demonstrate our method’s efficacy on brain lesion segmentation. In both datasets, our method shows reliable validation results to guide researchers towards choosing performance metrics in a problem-aware manner, especially when the results of different validation metrics are too similar among models to determine the best one

Conditional advancement of machine learning algorithm via fuzzy neural network

Improving overall performance is the ultimate goal of any machine learning (ML) algorithm. While it is a trivial task to explore multiple individual validation measurements, evaluating and monitoring overall performance can be complicated due to the highly nonlinear nature of the functions describing the relationships among different validation metrics, such as the Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Therefore, it is naturally desirable to have a reliable validation algorithm or model that can integrate all existing validation metrics into a single value. This consolidated metric would enable straightforward assessment of an ML algorithm’s performance and identify areas for improvement. To deal with such a complex nonlinear problem, this study suggests a novel parameterized model named Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which takes any set of input–output precise-imprecise data and uses a neuro-adaptive learning strategy to tune the parameters of the pre-defined membership functions. Our method can be accepted as an elegant and the state-of-the-art method for the nonlinear function approximation, which could be added directly to any convolutional neural networks (CNN) loss functions as the regularization term to generate a constrained-CNN-FUZZY model optimization. To demonstrate the ability of the purposed method and provide a practical explanation of the capability of ANFIS, we use deep CNN as a testing platform to consider the fact that one of the biggest challenges CNN-developers faced today is to reduce the mismatching between the provided input data and the predicted results monitored by different validation metrics. We first create a toy dataset using MNIST and investigate the properties of the proposed model. We then use a medical dataset to demonstrate our method’s efficacy on brain lesion segmentation. In both datasets, our method shows reliable validation results to guide researchers towards choosing performance metrics in a problem-aware manner, especially when the results of different validation metrics are too similar among models to determine the best one

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Gating Artificial Neural Network Based Soft Sensor

This work proposes a novel approach to Soft Sensor modelling, where the Soft Sensor is built by a set of experts which are artificial neural networks with randomly generated topology. For each of the experts a meta neural network is trained, the gating Artificial Neural Network. The role of the gating network is to learn the performance of the experts in dependency on the input data samples. The final prediction of the Soft Sensor is a weighted sum of the individual experts predictions. The proposed meta-learning method is evaluated on two different process industry data sets

Laparoscopy Pneumoperitoneum Fuzzy Modeling

Abstract: Gas volume to intra-peritoneal pressure fuzzy modeling for evaluating pneumoperitoneum in videolaparoscopic surgery is proposed in this paper. The proposed approach innovates in using fuzzy logic and fuzzy set theory for evaluating the accuracy of the prognosis value in order to minimize or avoid iatrogenic injuries due to the blind needle puncture. In so doing, it demonstrates the feasibility of fuzzy analysis to contribute to medicine and health care. Fuzzy systems is employed here in synergy with artificial neural network based on backpropaga tion, multilayer perceptron architecture for building up numerical functions. Experimental data employed for analysis were collected in the accomplishment of the pneumoperitoneum in a random population of patients submitted to videolaparoscopic surgeries. Numerical results indicate that the proposed fuzzy mapping for describing the relation from the intra peritoneal pressure measures as function injected gas volumes succeeded in determinining a fuzzy model for this nonlinear system when compared to the statistical model