Rakesh Kanji

Jaypee University of Information Technology

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Ganesh Bagler

Indraprastha Institute of Information Technology Delhi

Shubham Dokania

Daimler (United Kingdom)

Apuroop Sethupathy

Indian Institute of Technology Delhi

Anupam Jain

Central Drug Research Institute

Aishwarya Gupta

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Shivani Rana

Jaypee University of Information Technology

Shruti Jain

Jaypee University of Information Technology

Neelansh Garg

Indraprastha Institute of Information Technology Delhi

Rakhi Nk

Indraprastha Institute of Information Technology Delhi

Rahul Badhwar

Lovely Professional University

Cooperative Institutions

Indraprastha Institute of Information Technology Delhi

Indian Institute of Technology Delhi

Indian Institute of Technology Jodhpur

University of Delhi

Chandigarh University

Lovely Professional University

Netaji Subhas University of Technology

Indian Institute of Technology Hyderabad

Jaypee University of Information Technology

Guru Gobind Singh Indraprastha University

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Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

Molecular BioSystems (2015)

Rakesh Kanji Abhinav Sharma Ganesh Bagler

Knowing the importance of identification of drug features that are critical for specifying their adverse effects, we propose a generalized ordinary canonical correlation analysis model that integrates the target profiles and chemical profiles of drugs.

Phenotypic screening

10.1039/c5mb00312a

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Citations (9)

Invisible Image Watermarking Using Z Transforms (IIWZT)

Advances in intelligent systems and computing (2012)

Jyotsna Kumar Mandal Rakesh Kanji

Transformation matrix

10.1007/978-3-642-31552-7_81

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Citations (1)

Comprehensive Analysis of Oversampling Techniques for Addressing Class Imbalance Employing Machine Learning Models

Recent Advances in Computer Science and Communications (2024)

Shivani Rana Rakesh Kanji Shruti Jain

Background: Unbalanced datasets present a significant challenge in machine learning, often leading to biased models that favor the majority class. Recent oversampling techniques like SMOTE, Borderline SMOTE, and ADASYN attempt to mitigate these issues. This study investigates these techniques in conjunction with machine learning models like SVM, Decision Tree, and Logistic Regression. The results reveal critical challenges such as noise amplification and overfitting, which we address by refining the oversampling approaches to improve model performance and generalization. Aim: In order to address this challenge of unbalanced datasets, the minority class is oversampled to accommodate the majority class. Oversampling techniques such SMOTE (Synthetic Minority Oversampling Technique), Borderline SMOTE and ADASYN (Adaptive Synthetic Sampling) are used in this work. Objective: To perform the comprehensive analysis of various oversampling methods for taking acre of class imbalance issue using ML methods. Method: The proposed methodology uses BERT technique which removes the pre-processing step. Various proposed oversampling techniques in the literature are used for balancing the data, followed by feature extraction followed by text classification using ML algorithms. Experiments are performed using ML classification algorithms like Decision tree (DT), Logistic regression (LR), Support vector machine (SVM) and Random forest (RF) for categorizing the data. Result: The results show improvement corresponding SVM using Borderline SMOTE, resulting in an accuracy of 71.9% and MCC value of 0.53. Conclusion: The suggested method assists in the evolution of fairer and more effective ML models by addressing this basic issue of class imbalance.

Oversampling

Overfitting

Tree (set theory)

10.2174/0126662558347788241127051934

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A hierarchical anatomical classification schema for prediction of phenotypic side effects

PLoS ONE (2018)

Somin Wadhwa Aishwarya Gupta Shubham Dokania Rakesh Kanji Ganesh Bagler

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

Schema (genetic algorithms)

Identification

10.1371/journal.pone.0193959

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Citations (9)

A Generalized Partial Canonical Correlation Model to Measure Contribution of Individual Drug Features Toward Side Effects Prediction

Lecture notes on data engineering and communications technologies (2020)

Rakesh Kanji Ganesh Bagler

Canonical correlation

Identification

Molecular descriptor

10.1007/978-981-15-0978-0_15

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FlavorDB: a database of flavor molecules

Nucleic Acids Research (2017)

Neelansh Garg Apuroop Sethupathy Rudraksh Tuwani Rakhi Nk Shubham Dokania

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

Ingredient

10.1093/nar/gkx957

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Citations (130)