@article { ISI:000248860300003,
	title = {Using pseudo amino acid composition to predict protein subnuclear localization: approached with PSSM},
	journal = {Pattern Recognition Letters},
	volume = {28},
	number = {13},
	year = {2007},
	month = {OCT},
	pages = {1610-1615},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {<p>Identification of Nuclear protein localization assumes significance as it can provide in depth insight for genome regulation and function annotation of novel proteins. A multiclass SVM classifier with various input features was employed for nuclear protein compartment identification. The input features include factor solution scores and evolutionary information (position specific scoring matrix (PSSM) score) apart from conventional dipeptide composition and pseudo amino acid composition. All the SVM classifiers with different sets of input features performed better than the previously available prediction classifiers. The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou [Shen, H.B., Chou, K.C., 2005, Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337, 752-756] is 71.23\%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods. (c) 2007 Elsevier B.V. All rights reserved.</p>},
	keywords = {factor solution score, multiclass SVM, nuclear protein, PSSM, subnuclear localization},
	issn = {0167-8655},
	doi = {10.1016/j.patrec.2007.04.001},
	author = {Mundra, Piyushkumar and Kumar, Madhan and Kumar, K. Krishna and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.}
}
@article { ISI:000371799000029,
	title = {Utilization of banana peel for the removal of benzoic and salicylic acid from aqueous solutions and its potential reuse},
	journal = {Desalination and Water Treatment},
	volume = {57},
	number = {27},
	year = {2016},
	month = {JUN},
	pages = {12717-12729},
	publisher = {TAYLOR \& FRANCIS INC},
	type = {Article},
	address = {530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA},
	abstract = {<p>We report on the adsorptive removal of benzoic acid (BA) and salicylic acid (SA) using banana peel (BP), an abundantly available agricultural waste material, for the first time. BP was characterized by proximate analysis, FT-IR, scanning electron microscopy, BET surface area, and XRF. The number of basic sites on BP (4.9mmolg(-1)) is relatively more than acidic sites (0.75mmolg(-1)). The Langmuir uptake capacity values obtained are 6.62mgg(-1) for BA and 9.80mgg(-1) for SA. A mechanism for binding acid molecule to the BP surface is proposed.</p>},
	keywords = {Adsorption, Banana peel, Benzoic acids, Salicylic acid, Wastewater treatment},
	issn = {1944-3994},
	doi = {10.1080/19443994.2015.1051589},
	author = {Pathak, Pranav D. and Mandavgane, Sachin A. and Kulkarni, Bhaskar D.}
}
@article {47713,
	title = {On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities},
	journal = {Chemical Biology \& Drug Design},
	volume = {98},
	year = {2021},
	month = {AUG},
	pages = {258-269},
	type = {Article},
	abstract = {For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC(50) predictions can screen molecules that have desirable activity before assessment by docking studies.},
	keywords = {drug discovery, electron affinity, Electronegativity, molecular field descriptors, partial least squares, QSAR},
	issn = {1747-0277},
	doi = {10.1111/cbdd.13895},
	author = {Kunde, Pushkar D. and Ramkumar, Sudha and Kamble, Sanjay P. and RaviKumar, Ameeta and Kulkarni, Bhaskar D. and Kumar, V. Ravi}
}
