Signature Descriptor Research Articles

Attention-based approach to predict drug-target interactions across seven target superfamilies.

Drug-target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets. This study introduces an attention-based method (called as MMAtt-DTA) to predict drug-target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185676 drug-target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications. https://github.com/AronSchulman/MMAtt-DTA.

3D shape descriptor design based on HKS and persistent homology with stability analysis

In recent years, with the rapid development of the computer aided design and computer graphics, a large number of 3D models have emerged, making it a challenge to quickly find models of interest. As a concise and informative representation of 3D models, shape descriptors are a key factor in achieving effective retrieval. In this paper, we propose a novel global descriptor for 3D models that incorporates both geometric and topological information. We refer to this descriptor as the persistent heat kernel signature descriptor (PHKS). Constructed by concatenating our isometry-invariant geometric descriptor with topological descriptor, PHKS possesses high recognition ability, while remaining insensitive to noise and can be efficiently calculated. Retrieval experiments of 3D models on the benchmark datasets show considerable performance gains of the proposed method compared to other descriptors based on HKS and advanced topological descriptors.

Machine learnt patterns in rhodium-catalysed asymmetric Michael addition using chiral diene ligands

Interpretable featurisation allows Quantitative Structure-Property Relationships (QSPR) between chiral diene ligand structures and product stereoselectivities in rhodium-catalysed asymmetric Michael additions (RhCASA). The machine learning approach developed herein is simple to implement yet provides instructive insights and useful predictions (q2/R2 0.7–0.8). These simple interpretable features are found to give comparable predictive ability to both commercial (Dragon) and molecular signature descriptor library approaches. Strong insights into the specific areas of the chiral diene ligand that engender improved RhCASA process enantioselectivity are achieved. These were more interpretable, to synthetic chemists, than existing ML approaches, greatly facilitating design of new chiral diene-ligands.

Design of fragrance molecules using computer-aided molecular design with machine learning

The robust growth of the flavour and fragrances market necessitates the need for a model-based method for the identification of fragrance molecules with favourable product attributes. In this work, a computer-aided molecular design (CAMD) based approach was developed to design fragrance molecules. Hyperbox classifiers are implemented for predicting fragrance properties due to their ability to generate transparent results. The resulting models can be interpreted as disjunctive decision support rules that establish the quantitative relationship between the structural parameters of molecules to their odour characteristics. In addition, other relevant properties are modelled using group contribution (GC) models into the CAMD framework. In this work, molecular signature descriptors were used to formulate a CAMD model that connects different prediction models and machine learning algorithm in a common framework. A case study was conducted to illustrate the application of the proposed methodology in the design of fragrance additives used in body lotions.

Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework

Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework

Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

Analysis of calcareous sand particle shape using fourier descriptor analysis

Soil particle shape analysis was conducted on two calcareous sands from Dissa and Matmata in Tunisia and one silica sand from Jumunjin in Korea using an image analysis method. This technique uses complex Fourier shape descriptors and image analysis of sand grains SEM photographs to provide accurate quantification of particle morphology and texture. The Fourier descriptors, denoted “Signature Descriptors”, provide measures of Elongation, Triangularity, Squareness, and Asymmetry and identify the overall shape of soil particles. A summary of higher-order descriptors provides textural information linked up to local roughness and texture characteristics. After analyzing the Fourier descriptors, elongation was found to be the major characteristics of the three sands, mainly for the Dissa and Matmata carbonate sands. In addition, the elongation signature descriptors for the Dissa and Matmata sands have very high values compared to those of Ras Alkhair (Saudi Arabia) and Dog’s bay (Ireland) sands. Therefore, the Dissa and Matmata carbonate sands have higher angles of friction and higher critical state angle of friction than Ras Alkhair and Dog’s bay sands. Furthermore, the Dissa and Matmata carbonate sands show higher roughness than Jumunjin’s silica sand.

Proteolysis and Process-Induced Modifications in Synbiotic Yogurt Investigated by Peptidomics and Phosphopeptidomics.

Probiotic and synbiotic yogurt preparations were manufactured at the semi-industrial pilot scale with Lactobacillus acidophilus and Bifidobacteria strains without inulin or fortified with 1 and 3% (w/w) inulin. The pathway of casein breakdown was determined in probiotic, synbiotic, conventional yogurt, and nonstarted milk base using HPLC-ESI-MS/MS-based peptidomics and phosphopeptidomics; in the latter case, casein phosphorylated peptides (CPPs) were previously enriched by hydroxyapatite chromatography. Compared with traditional yogurt, casein proteolysis increased in probiotic and even more in synbiotic yogurt with 1% inulin. Fortification with 3% inulin greatly modified the proteolytic pattern, indicating a characteristic contribution of probiotics to proteolysis. The enhanced proteolysis in synbiotic yogurt exposed the neo-formed peptides to progressively increase enzymatic or chemical modifications, such as dephosphorylation of CPPs, methionine oxidation, and formation of N-terminal pyroglutamic acids. These modifications might constitute molecular signature descriptors of metabolic processes mediated by complex bacterial communities, with technological, nutritional, and sensorial significance.

Genetic programming-based learning of texture classification descriptors from Local Edge Signature

Describing texture is a very challenging problem for many image-based expert and intelligent systems (e.g. defective product detection, people re-identification, abnormality investigation in medical imaging and remote sensing applications…) since the process of texture classification relies on the quality of the extracted features. Indeed, detecting and extracting features is a hard and time-consuming task that requires the intervention of an expert, notably when dealing with challenging textures. Thus, machine learning-based descriptors have emerged as another alternative to deal with the difficulty of feature extracting. In this work, we propose a new operator, which we named Local Edge Signature (LES) descriptor, to locally represent texture. The proposed texture descriptor is based on statistical information on edge pixels’ arrangement and orientation in a specific local region, and it is insensitive to rotation and scale changes. A genetic programming-based approach is then fitted to automatically learn a global texture descriptor that we called Genetic Texture Signature (GTS). In fact, a tree representation of individuals is used to generate global texture features by applying elementary operations on LES elements at a set of keypoints, and a fitness function evaluates the descriptors considering intra-class homogeneity and inter-class discrimination properties of their generated features. The obtained results, on six challenging texture datasets (Brodatz, Outex_TC_00000, Outex_TC_00013, KTH-TIPS, KTH-TIPS2b and UIUCTex), show that the proposed classification method, which is fully automated, achieves state-of-the-art performance, especially when the number of available training samples is limited.

On-Line Signature Partitioning Using a Population Based Algorithm

Abstract The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.

Facial expression recognition using digital signature feature descriptor

Facial feature extraction is the most crucial part for efficient representation of facial images. For facial expression recognition, efficiency of recognition mainly depends on discriminative nature of features that describe optical changes of facial expressions. Facial expressions are very dynamic in nature. The dynamics of facial muscle changes is required to be captured and encoded accurately. Facial expressions are contractions and expansions of facial muscles which results in high-frequency edges on different part of facial image. A novel feature extraction framework called digital signature based on high-frequency edges in combination with LBP histogram features is developed, and the proposed methodology is used for facial expression recognition (FER). Digital signature descriptor of facial dynamic is obtained by projecting edge pixels vertically and horizontally. Digital signature uniquely and completely describes the facial expressions. Support vector method classifier is used to classify six basic expressions based on one-against-all strategy of classification. The validity of proposed algorithm is tested on standard widely used Cohn–Kanade Facial Expression Database (CKFED), Taiwanese Facial Expression Database (TFED) and Japanese Female Facial Expression databases (JFED). Experimental results show that the efficiency of expression recognition of proposed novel technique is 96.25% on CKFED which is higher than other existing methods.

Toward Interpretable Machine Learning Models for Materials Discovery

Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials. Although these methods make good predictions of the properties of new materials, current modeling methods use efficient but rather arcane (difficult‐to‐interpret) mathematical features (descriptors) to characterize materials. Data‐driven ML models are considerably more useful if more chemically interpretable descriptors are used to train them, as long as these models also accurately recapitulate the properties of materials in training and test sets used to generate and validate the models. Herein, how a particular type of molecular fragment descriptor, the signature descriptor, achieves these joint aims of accuracy and interpretability is described. Seven different types of materials properties are modeled, and the performance of models generated from signature descriptors is compared with those generated by widely used Dragon descriptors. The key descriptors in the model represent functionalities that make chemical sense. Mapping these fragments back on to exemplar materials provides a useful guide to chemists wishing to modify promising lead materials to improve their properties. This is one of the first applications of signature descriptors to the modeling of complex materials properties.

Gradient Direction Accumulation-based Heat Kernel Signature Descriptor for Nonrigid 3D Model Retrieval

This paper presents an effective 3D local feature descriptor, which is called Gradient Direction Accumulation-based Heat Kernel Signature (GDA-HKS) descriptor, and its application in nonrigid 3D model retrieval. The GDA-HKS descriptor is based on the heat kernel signature, and it is scale invariant and robust to the nonrigid deformation of the 3D model. Compared with the SI-HKS descriptor, the GDA-HKS descriptor is constructed directly in the time domain, and it can effectively avoid the loss of high frequency information. The absolute gradient difference is used to encode the GDA-HKS descriptor, which can describe the changing trend of the one-dimensional signal more effectively. Extensive experimental results have validated the effectiveness of the designed GDA-HKS descriptor.

Dempster–Shafer evidence theory‐based multi‐feature learning and fusion method for non‐rigid 3D model retrieval

This study introduces a novel multi‐feature‐based non‐rigid three‐dimensional (3D) model retrieval method. First, for each 3D model, compute the scale‐invariant heat kernel signature (SI‐HKS) descriptor and the wave kernel signature (WKS) descriptor of each vertex. Then, the normalised weighted bags of phrases feature is obtained and they are fed to the convolutional neural networks. The trust degree of each kind of descriptor is computed, and the total trust degree can be obtained. Finally, the fusion network is trained and the retrieval results can be obtained according to the ranking of the total trust degrees. For the training phase and the testing phase, the authors define different computation methods of the trust degrees and the total trust degrees. The Dempster–Shafer (DS) evidence‐based total trust degrees are used not only in the feature layer but also in the decision layer. The final decision results of the total trust degrees are used in the process of the network learning. So the proposed method can make full use of the complementary information of the SI‐HKS descriptor and the WKS descriptor. Extensive experiments have shown that the proposed multi‐feature fusion method has better performance than a single feature‐based method, and also outperforms other existing state‐of‐the‐art methods.

A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification

In this paper, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation (SR) in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS, and UTSIG. Beyond the accuracy improvements, several major parameters associated with SR; such as selected configuration, dictionary size, sparsity level, and positivity priors are investigated. Besides, it is evinced that second-order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of SR is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the binary robust invariant scalable key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication toward the benefits of learned features, even in writer dependent scenarios with a unique model for each writer and only a few available reference samples of him/her.

Development of Modified Spherical Signature Descriptor Using 3D Point Cloud Data and Application to Convolutional Neural Network for Urban Structure Classification

Development of Modified Spherical Signature Descriptor Using 3D Point Cloud Data and Application to Convolutional Neural Network for Urban Structure Classification

Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks

Non-rigid 3-D model retrieval is a challenging problem in 3-D shape analysis. Recently, deep learning-based 3-D feature extraction methods have been studied and have achieved better performance than the previous state-of-the-art methods. Inspired by the quadruplet neural networks proposed for learning local image feature descriptors, we propose a novel non-rigid 3-D model retrieval method based on quadruplet convolutional neural networks. For training the proposed networks, the quadruplet samples are first selected using the online sampling method. For each 3-D model, the wave kernel signature descriptor of each vertex is computed, and its corresponding multi-energy shape distribution matrix is constructed as the input of the network. Then, the quadruplet convolutional neural networks are trained using our improved quadruplet loss function, which not only preserves the advantages of existing quadruplet loss functions but also decreases the risk of underfitting. For the query sample, the 3-D shape features are computed using one branch of the trained quadruplet networks. Finally, the retrieval results are obtained by the L2 distance measure. Extensive experimental results have validated the effectiveness of the proposed method.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets.

Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure–property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.

NMR spectroscopy of filtered serum of prostate cancer: A new frontier in metabolomics.

To address the shortcomings of digital rectal examinations (DRE), serum prostate-specific antigen (PSA), and trans-rectal ultrasound (TRUS) for precise determination of prostate cancer (PC) and differentiation from benign prostatic hyperplasia (BPH), we applied (1) H-nuclear magnetic resonance (NMR) spectroscopy as a surrogate tactic for probing and prediction of PC and BPH. The study comprises 210 filtered sera from suspected PC, BPH, and a healthy subjects' cohort (HC). The filtered serum approach delineates to identify and quantify 52 metabolites using (1) H NMR spectroscopy. All subjects had undergone clinical evaluations (DRE, PSA, and TRUS) followed by biopsy for Gleason score, if needed. NMR-measured metabolites and clinical evaluation data were examined separately using linear multivariate discriminant function analysis (DFA) to probe the signature descriptors for each cohort. DFA indicated that glycine, sarcosine, alanine, creatine, xanthine, and hypoxanthine were able to determine abnormal prostate (BPH + PC). DFA-based classification presented high precision (86.2% by NMR and 68.1% by clinical laboratory method) in discriminating HC from BPH + PC. DFA reveals that alanine, sarcosine, creatinine, glycine, and citrate were able to discriminate PC from BPH. DFA-based categorization exhibited high accuracy (88.3% by NMR and 75.2% by clinical laboratory method) to differentiate PC from BPH. (1) H NMR-based metabolic profiling of filtered-serum sample appears to be assuring, swift, and least-invasive for probing and prediction of PC and BPH with its signature metabolic profile. This novel technique is not only on a par with histopathological evaluation of PC determination but is also comparable to liquid chromatography-based mass spectrometry to identify the metabolites. Prostate 76:1106-1119, 2016. © 2016 Wiley Periodicals, Inc.