A
Anil Kumar Pandey
Researcher at All India Institute of Medical Sciences
Publications - 95
Citations - 1405
Anil Kumar Pandey is an academic researcher from All India Institute of Medical Sciences. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 14, co-authored 67 publications receiving 1060 citations. Previous affiliations of Anil Kumar Pandey include University of British Columbia & Mohanlal Sukhadia University.
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Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Matthias Rupp,Wolfram Teetz,Stefan Brandmaier,Ahmed Abdelaziz,Volodymyr V. Prokopenko,Vsevolod Yu. Tanchuk,Roberto Todeschini,Alexandre Varnek,Gilles Marcou,Peter Ertl,Vladimir Potemkin,Maria Grishina,Johann Gasteiger,Christof H. Schwab,Igor I. Baskin,Vladimir A. Palyulin,Eugene V. Radchenko,William J. Welsh,Vladyslav Kholodovych,Dmitriy Chekmarev,Artem Cherkasov,João Aires-de-Sousa,Qingyou Zhang,Andreas Bender,Florian Nigsch,Luc Patiny,Antony J. Williams,Valery Tkachenko,Igor V. Tetko +32 more
TL;DR: The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling and to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community.
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Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Artem Cherkasov,Jiazhong Li,Paola Gramatica,Katja Hansen,Timon Schroeter,Klaus-Robert Müller,Lili Xi,Huanxiang Liu,Xiaojun Yao,Tomas Öberg,Farhad Hormozdiari,Phuong Dao,Cenk Sahinalp,Roberto Todeschini,Pavel G. Polishchuk,A. Artemenko,Victor E. Kuz’min,Todd M. Martin,Douglas M. Young,Denis Fourches,Eugene N. Muratov,Alexander Tropsha,Igor I. Baskin,Dragos Horvath,Gilles Marcou,Christophe Muller,A. Varnek,Volodymyr V. Prokopenko,Igor V. Tetko +32 more
TL;DR: This work demonstrates that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs and can be used to halve the cost of experimental measurements by providing a similar prediction accuracy.
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Applicability domain for in silico models to achieve accuracy of experimental measurements
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Vasily V. Kovalishyn,Volodymyr V. Prokopenko,Igor V. Tetko +6 more
TL;DR: It is shown that applicability domain (AD) approaches can differentiate reliable and non‐reliable predictions and identify those with experimental accuracy for both regression and classification models.
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Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients.
TL;DR: It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model.
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Imbalanced Breast Cancer Classification Using Transfer Learning
TL;DR: This paper proposes a framework based on the notion of transfer learning to address the issue of accurate breast cancer detection using automated algorithms and focuses its efforts on histopathological and imbalanced image classification.