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12th International Conference on computational Advances in Bio and Medical Sciences, ICCABS 2023

作者： Pagolu, Sri Lakshmi Bhavani Suba, S. Parekh, Nita Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Telangana Hyderabad500032 India

ISBN: (纸本)9783031827679

Aberrant genome-wide DNA methylation patterns is common in cancers. Understanding how these affect the transcriptome can provide insights into subtype specific development and progression of tumorigenesis. In this study we carried out genome-wide analysis of DNA methylation and gene expression profiles in TCGA-BRCA breast cancer samples to propose a novel set of 35 methylation-based prognostic markers that may provide insights to molecular subtype specific disease stratification. Gene-set enrichment and pathway analysis of the predicted markers using MSigDB and DAVID revealed their role in mammary gland development pathway, various signaling pathways (ERBB2, NOTCH, etc.), and other cancer pathways, and show clear association with genes affected by hormone receptor status. We further show the discriminative power of the proposed DNA methylation signature in classifying breast cancer samples into three molecular subtypes, viz., Luminal, HER2-enriched and Triple Negative. An accuracy of 94.12% and MCC of 0.87 is obtained in stratified 5-fold cross-validation for the three-class classification using SVM-RBF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gene expression

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A new fuzzy support vector machine with pinball loss

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Discover Artificial Intelligence 2023年第1期3卷 14页

作者： Verma, Ram Nayan Deo, Rahul Srivastava, Rakesh Subbarao, Naidu Singh, Gajendra Pratap School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi110067 India Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India

The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem;second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time. © The Author(s) 2023.

关键词： Support vector machines

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computational Studies of Phytochemicals from Allium Sativum with H7N9 Subtype in Avian Influenza 3rd

Computational Studies of Phytochemicals from Allium Sativum ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Mandal, Brishti Singh, Avineet Dhingra, Cheena Bansal, Hina Santoshi, Seneha Center for Computational Biology and Bioinformatics Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819986453

Allium sativum or garlic is a medicinal plant that is reported for its applications in many biological activities, such as its antiviral, anti-diabetic, anti-inflammatory properties etc. In this paper, the focus has been the identification of potential phytochemicals that can be extracted from Allium sativum that can act as a candidate which possesses the capability to inhibit the H7N9 Avian Influenza, a type of highly pathogenic and zoonotic strain that presents a significant threat to the public healthcare systems. Using computational tools and methods, screening of the 145 phytochemicals from Allium sativum was done against a major H7N9 protein Neuraminidase (NA). The molecular interaction study with the help of computational molecular docking indicated that many compounds have stable interaction with high binding affinity towards Neuraminidase (NA). These results suggest that phytochemicals from Allium sativum can be promising candidates for development of therapeutic agents against H7N9 virus for management of Avian Influenza. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Molecular modeling

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KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2 6

KCLab at Chemotimelines 2024: End-to-end system for chemothe...

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6th Workshop on Clinical Natural Language Processing, ClinicalNLP 2024, held at NAACL 2024

作者： Tan, Yukun Dede, Merve Chen, Ken Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center HoustonTx United States

ISBN: (纸本)9798891761094

This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database;second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach. © 2024 Association for computational Linguistics.

关键词： Chemotherapy

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In Silico Evaluation of Antifungal Compounds from Nigella Sativa Against Black Fungus 10

In Silico Evaluation of Antifungal Compounds from Nigella Sa...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Rai, Manshi Kaur, Amanpreet Sharma, Priya Kruthi, M Nithya Santoshi, Seneha Bansal, Hina Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida201313 India

ISBN: (纸本)9789380544472

Nigella sativa, often known as Black seed, has been used as a natural cure for a variety of illnesses for ages across various cultures. It comprises numerous active substances which includes carvacrol, nigellimine, nigellicine, and nigellidine which are only few of the many active substances. Depending on the underlying health of the patients, Mucormycosis also known as Black fungus infection, has a death rate of 46-96%. Uncommon opportunistic infections like fungus typically strike patients with weakened immune systems. In this study, compounds showing antifungal properties from Nigella Sativa were screened using molecular docking against Mucormycosis or black fungus infection to identify potential inhibitors. In this study the already established therapeutic target of Mucormycosis exo-l,3-beta-glucan synthase have been explored for interaction study. In order to deepen our understanding of the free energy binding studies, we have performed molecular docking analysis and additional MM-GBSA calculations. The computational study showed that the interaction scores of the chosen drugs with the chosen target were encouraging. The outcomes of this study pave the path for additional research into chemicals with potential profiles against the extremely rare but fatal disease known as Mucormycosis. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Free energy

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A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features 50

A Machine Learning Approach for Outcome Prediction in Postan...

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50th Computing in Cardiology, CinC 2023

作者： Venkataramani, Vijay Vignesh Garg, Akshit Maity, Maitreya Priyakumar, U Deva Iiit Center for Computational Natural Sciences and Bioinformatics Hyderabad India Iiit IHub Data Hyderabad India

ISBN: (纸本)9798350382525

In this work, we describe the creation of our machine-learning-based solution for coma prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the 'Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023'. Our team, 'ComaToast', had its best submission ranked 28 out of 36 teams selected worldwide, with a challenge score of 0.381 on the official leaderboard for the hidden test set. We use a combination of age and signal features from EEG and ECG recordings. Frequency domain features, specifically mean power spectral density from 4 different bands of frequencies (Delta, Theta, Alpha and Beta) and mean Burst Suppression Ratio, were extracted from pre-processed EEG recordings from the first and last available recording for a given patient. Features like mean and standard deviations were extracted along channels for ECG recordings. After imputing missing values, these features are fed to an XGBoost classifier for the final binary classification of the outcome prediction task. The features are fed to a random forest regressor to predict the CPC outcome for every patient. A solution like ours, which uses a simple model and training technique, may be more viable than deep-learning solutions in general use cases. In our final model, our approach achieved a 5-fold cross-validation score of 0.34 on the public train set. © 2023 CinC.

关键词： Forecasting

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Analyzing epidemiological trends in second and third waves of COVID-19 variants in India

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The European Physical Journal Special Topics 2024年 1-12页

作者： Agarwal, Kushagra Ghosh, Subrata Parekh, Nita Hens, Chittaranjan Center for Computational Natural Science and Bioinformatics International Institute of Informational Technology Hyderabad India

The COVID-19 pandemic wrought havoc across India, particularly during its devastating second and third waves. This study undertakes a crucial epidemiological analysis of these waves, leveraging actual variant count data. Given limited sequencing efforts, variant information is sparse, prompting a novel approach to scaling up with actual case data. Employing a multi-strain variant-level SEIRS model tailored to each Indian state, we modeled the disease’s propagation. We report the estimated parameters of the SEIRS models for the two waves separately. Notably, the transmission coefficients ( $$\beta $$ ) were estimated to be 0.12 for Kappa, 0.35 for Delta, and 0.38 for Omicron. These coefficients signify the contagiousness of each variant, offering critical information for understanding and managing the pandemic’s dynamics. The findings hold significant implications for public health strategies, emphasizing the urgency of comprehensive variant tracking and proactive measures to mitigate the impact of evolving viral strains.

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An Analytical Approach to Predict the Cardio Vascular Disorder 17

An Analytical Approach to Predict the Cardio Vascular Disord...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Chauhan, Ritu Gola, Nidhi Yafi, Eiad Amity University Center for Computational Biology and Bioinformatics UP India University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9781665453486

In recent times, heart disease has been recognized as the world's leading cause of death. However, it is also regarded as the disease that is most easily controlled and prevented. Recently, World Health Organization (WHO) claims that heart disease's progression and associated treatment expenses can both be significantly halted with the help of an early and prompt diagnosis. Therefore, researchers have employed various data mining approaches to diagnose heart disease in consideration of the rising number of deaths caused by the disease. This research study applied data mining classification modeling techniques, specifically discriminant analysis on the heart disease dataset for the prediction of chances of heart disease based on various attributes and assess the contribution of each attribute towards the heart disease. Lastly, the range and the accuracy of the classification are assessed. This dataset has an accuracy of 85.3% in predicting that whether individual has heart disease or not and the specificity of individual possess heart disease is 84.8% while normal individuals acquire specificity of 85.9%. © 2023 IEEE.

关键词： Data mining

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Sarve:synthetic data and local differential privacy for private frequency estimation

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Cybersecurity 2022年第4期5卷 97-116页

作者： Gatha Varmal Ritu Chauhan Dhananjay Singh Amity Institute of Information Technology Amity UniversityNoidaIndia Center for Computational Biology and Bioinformatics Amity UniversityNoidaIndia Department of Electronics Engineering HUFSSeoulKorea

The collection of user attributes by service providers is a double-edged *** are instrumental in driving statistical analysis to train more accurate predictive models like *** analysis of the collected user data includes frequency estimation for categorical ***,the users deserve privacy guarantees against inadvertent identity *** algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their *** propose Sarve,a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response(RAPPOR)and Hadamard Response(HR)for randomization in combination with fake *** design of a service-oriented architecture must consider two types of complexities,namely computational and *** functions of such systems aim to minimize the two complexities and therefore,the choice of privacy-enhancing methods must be a calculated *** variant of RAPPOR we had used was realized through bloom flters.A bloom filter is a memory-efficient data structure that offers time complexity of O(1).On the other hand,HR has been proven to give the best communication costs of the order of log(b)for b-bits ***,Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original *** also implemented an adaptive solution enhanced from the work of Arcolezi et *** use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacyregimes.

关键词： Synthetic data Differential privacy Frequency estimation Frequency oracle Privacy

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Machine Learning: An Analytical Approach for Pattern Detection in Diabetes 1

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Chauhan, Ritu Goel, Anika Kaur, Harleen Alankar, Bhavya Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

In current times, the amount of data is increasing exponentially but generated data is not transformed into knowledge data which eventually serves no purpose. This issue can be resolved by applying machine learning approaches which prove to play a promising role in the field of prediction. Similarly, in healthcare industries, there is a supply of large amount of data for which it is necessary to extract information for prognosis, diagnosis, and medication development. In this paper, we have focused on diagnosis of diabetes which is a chronic disease with fastest global growth rate. The current study is carried out in two phases where in first phase data is pre-processed to remove the unwanted and missing values and in second phase the data is analyzed by model building using machine learning algorithm. Additionally, we have attempted machine learning techniques in our study such as decision tree classifier and logistic regression which can be used for diagnosis of diabetes, and we attain the accuracy of 75.9% for decision tree classifier and 74.6% for logistic regression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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