Sparse Fingerprint Research Articles

Deep learning-based classification model for GPR151 activator activity prediction

BackgroundGPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening.ResultsWe propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction.ConclusionThe results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening.

Accurate Indoor Visible Light Positioning Using a Modified Pathloss Model With Sparse Fingerprints

Visible light communications (VLC) based indoor positioning is a promising approach to serve an increasing need for location-aware services. Received signal strength (RSS) fingerprints based visible light positioning (VLP) was shown to achieve highly accurate, yet simple, VLP systems. It needs, however, large training datasets; whose collection is labor intensive. In this work, an artificial dataset generation methodology based on a modified pathloss model is proposed, where the pathloss exponent is assumed to be variable. Four interpolation techniques were investigated for the estimation of the variable pathloss exponent, where bicubic interpolation showed the best results in the simulation using a uniform sparse grid. The method is specially conceived to address the site surveying demand while attaining high positioning accuracy under non-line-of-sight (NLOS) conditions with sparse offline measurements. The technique is not limited to visible light applications since it depends only on the RSS and the pathloss model. Moreover, by using the weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighbor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) machine learning technique, this work designs an accurate VLP system based on sparse fingerprints. Simulation results show an average positioning error of 2.48 cm using weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN (W <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) with only 53 offline measurements in a 25 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> area. Furthermore, experimental results show an average positioning error of 3.04 cm with only 18 offline measurements in an area of approximately 9 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> , and an average positioning error of 1.92 cm using only 16 offline measurements in an area of 6 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> .

A Magnetic-Based Indoor Positioning Method on Fingerprint and Confidence Evaluation

A high accuracy in positioning and navigation is difficult in indoor and underground conditions due to obstacles. To deal with the issues of inaccurate positioning in indoor and underground environments, a novel Magnetic Based Positioning System (MBPS) based on fingerprint and confidence evaluation is presented in this article. The proposed method promises good penetrability and negligible interference from multipath error, along with solving the sparse fingerprint problem constrained by the a priori condition. Furthermore, to address the problem of variable signal quality, an optimal information selection method based on confidence evaluation is proposed. This method suggests evaluating and selecting the optimal information source to improve the robustness and accuracy of the MBPS. The approach is supported by experiments, where magnetic beacons are detected behind a concrete wall of 0.3 m thickness. According to the experimental results, the positioning accuracy can reach up to 0.040 m. This work provides an impressive positioning solution for indoor, underground, and other scenarios where high-frequency wireless signals cannot be used due to the obstacles.

Cost-efficient BLE fingerprint database construction approach via multi-quadric RBF interpolation

The demand for indoor localization is becoming urgent, but the traditional location fingerprint approach takes a lot of manpower and time to construct a fine-grained location fingerprint database. To address this problem, we propose to use the approach of combining dynamic collection of fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse fingerprint database is constructed from a small number of fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse fingerprint database. Then, the RBF interpolation approach is applied to enrich the sparse fingerprint database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of fingerprint samples.

Face Recognition Using Sparse Fingerprint Classification Algorithm

Unconstrained face recognition is still an open problem as the state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called sparse fingerprint classification algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the data set is small or medium ( e.g. , the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.

Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

Using Inverted Indices for Accelerating LINGO Calculations

The ever growing size of chemical databases calls for the development of novel methods for representing and comparing molecules. One such method called LINGO is based on fragmenting the SMILES string representation of molecules. Comparison of molecules can then be performed by calculating the Tanimoto coefficient, which is called LINGOsim when used on LINGO multisets. This paper introduces a verbose representation for storing LINGO multisets, which makes it possible to transform them into sparse fingerprints such that fingerprint data structures and algorithms can be used to accelerate queries. The previous best method for rapidly calculating the LINGOsim similarity matrix required specialized hardware to yield a significant speedup over existing methods. By representing LINGO multisets in the verbose representation and using inverted indices, it is possible to calculate LINGOsim similarity matrices roughly 2.6 times faster than existing methods without relying on specialized hardware.