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Depending on the relative numbers and spatial arrangement of Tryptophan (Trp; W) and Tyrosine (Tyr; Y) residues, different proteins produce distinct autofluorescence (AF) spectral shapes when excited at ∼280 nm. Yet, considering the vast number and heterogeneous forms in nature, visual analysis and precise identification of proteins based on their AF spectra is challenging and further compounded in cases when different proteins produce substantially similar AF spectral shapes. There is, thus, a serious need to develop a methodology to address this problem. The current study proposes a practical technology to quickly identify proteins using machine learning (ML) algorithms based on their AF spectra. Specifically, AF spectra of fifteen different standard proteins of varying origin with distinct structural and Trp/Tyr compositions were recorded; based on the spectral features selected by the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm, a multiclass Support Vector Machine (SVM) learning model with Radial Basis Function (RBF), Polynomial, and Linear kernels classified the proteins with high accuracy of 99.06%, 99.03%, and 98.29% respectively. Since protein identification is the key to understand biological functions and disease diagnosis, the proposed methodology could offer a viable alternative to and improve the existing protein identification techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2023. Published by Elsevier B.V. All rights reserved.)

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5 th , 10 th , 15 th & 20 th , were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology.

Epidermal growth factor receptor (EGFR) promotes tumorigenic characteristics and activates cancer-associated signaling pathways such as Wnt/-catenin, transforming growth factor (TGF-β), and phosphoinositide-3-kinase (PI3K). Several inhibitors have been reported to suppress the activity of EGFR and are being used in cancer treatment. However, patients in the malignant stage of cancer show resistance to those inhibitors, opening a wide space for research to discover novel inhibitors. Therefore, we carried out machine learning and virtual screening to discover novel inhibitors with high affinity against EGFR-TK. Initially, a library of 2640 chalcones were screened out using a machine-learning model developed based on the random forest algorithm, exhibiting high sensitivity and a Receiver Operating Characteristic curve (ROC area) of 0.99. Furthermore, out of the initial 2640 screened compounds, 412 compounds exhibiting potential activity are subjected to evaluation for drug-likeness properties through different filters: Blood–brain barrier penetration, Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter, alongside Cell Line Cytotoxicity Prediction. A total of 30 compounds that successfully pass through all these filters are selected for molecular docking. Of these, 6 compounds display substantial binding affinity and closer interaction with the conserved catalytic residues of the target EGFR-TK compared to the reference molecule (erlotinib). Furthermore, molecular dynamics simulation studies were conducted on four compounds (CID-375861, CID-375862, CID-23636403, and CID-259166) to confirm the stability of the docked complexes over a 100 ns simulation trajectory. Additionally, the binding free energy calculations by MMPBSA reveal that these four chalcone compounds exhibit strong affinity towards the EGFR-TK enzyme, with binding free energies of − 65.421 kJ/mol, − 94.266 kJ/mol, − 80.044 kJ/mol, and − 79.734 kJ/mol, respectively. The findings from this investigation highlight a set of promising chalcone compounds that have the potential to be developed into effective drugs for the treatment of various cancers. Further research and development on these compounds could pave the way for novel therapeutic interventions. [ABSTRACT FROM AUTHOR]

Online signature verification is most popular in the field of biometrics and forensics. Due to its popularity and recent demand, the major challenges are to improve its performance and complexity. This paper presents a novel approach for online signature validation based on local weight learning. The different features like, x coordinate, y coordinates time stamp, pen up and down, azimuth, height, pressure, displacement, velocity and acceleration are extracted from online signature. The extracted features are then passed to locally weighted learning classifier algorithms. Our experimentation is performed on local weight learning classifier of a machine learning method. Locally weighted learning classifier is experimentally found to be effective having False acceptance rate and False rejection rate as 1.18 and 0.02, respectively. The proposed methods gives better performance when compared with other well-known existing models for online signature verification. This result demonstrates that the method is suitable for a real-time system.The popular SVC2004 dataset is used in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower false positive and false negative rate. [ABSTRACT FROM AUTHOR]

This paper presents a new and mathematical method for online signature validation based on machine learning. In this way, the average values of the factors are taken into account to ensure validity. Here, seven different types of features used are x coordinates, y coordinates, time stamp, pen up and down, azimuth, height and pressure. Three new features are extracted from it, i.e., (displacement, velocity and acceleration) using the correlated extraction process to obtain dynamic feature of signature. These features are extracted from the popular dataset SVC2004. The extracted feature is then passed to various classifiers named as Naive Bayes, random forest, J48, MLP, logistic regression and PART. The result of genuine and forge signatures is obtained in terms of precision, true positive rate, false positive rate, F-score, etc. The obtained result is then compared with the existing method with respect to false acceptance rate and false rejection rate. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for dynamic signature authentication based on the machine learning approach. In the proposed method, average values of features are taken into consideration for the verification. Here, seven different types (x and y coordinates, time stamp, pen ups and downs, azimuth, altitude and pressure) of features are used. The obtained extracted feature is learned into different classifiers. Different classifiers have been taken into consideration like random tree, Naive Bayes, random forest, J48, etc. These features are extracted from well-known SVC2004 dataset. [ABSTRACT FROM AUTHOR]

In this paper we propose an effective method for static signature recognition from spontaneous handwritten text images. Our method relies on different aspects of writing: the presence of redundant patterns in the writing and its features. Signatures are analyzed at small fragments in which we seek to extract the patterns that an individual employs frequently as he writes. We exploit different features of writing like orientation, centroid and contour by computing a set of features from writing samples at different levels of observations. Orientation like intersecting point, edge point and gradient change of signature achieve great success in feature description. These features are extracted from the standard signature database and extracted features are trained and tested by machine learning (ML) approach. The machine learning approaches like Bagging, Random subspace (RS) and REP tree are used for classification purpose. Bagging with 10 iterations and base learner achieved efficiency upto 88 %. RS randomly selects features from feature set and creates new feature set. It uses decision tree as base classifier with different tree size. RS achieved efficiency same as Bagging but has more statistical errors. However, in case of REP tree we have achieved efficiency upto 75 %. The experimental results show that the Bagging and RS achieves promising results on publicly available data set. [ABSTRACT FROM AUTHOR]

Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (−)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer. [ABSTRACT FROM AUTHOR]

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC50 values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2. [ABSTRACT FROM AUTHOR]