arXiv subjects

Srilekha Mamidala

Publications and source records attributed to Srilekha Mamidala.

3 recordsLinked to original sources

arXiv2024

Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches

The prevalence of dementia has increased over time as global life expectancy improves and populations age. An individual's risk of developing dementia is influenced by various genetic, lifestyle, and environmental factors, among others. Predicting dementia risk may enable individuals to employ mitigation strategies or lifestyle changes to delay dementia onset. Current computational approaches to dementia prediction only return risk upon narrow categories of variables and do not account for interactions between different risk variables. The proposed framework utilizes a novel holistic approach to dementia risk prediction and is the first to incorporate various sources of tabular environmental pollution and lifestyle factor data with network systems biology-based genetic data. LightGBM gradient boosting was employed to ensure validity of included factors. This approach successfully models interactions between variables through an original weighted integration method coined Sysable. Multiple machine learning models trained the algorithm to reduce reliance on a single model. The developed approach surpassed all existing dementia risk prediction approaches, with a sensitivity of 85%, specificity of 99%, geometric accuracy of 92%, and AUROC of 91.7%. A transfer learning model was implemented as well. De-biasing algorithms were run on the model via the AI Fairness 360 Library. Effects of demographic disparities on dementia prevalence were analyzed to potentially highlight areas in need and promote equitable and accessible care. The resulting model was additionally integrated into a user-friendly app providing holistic predictions and personalized risk mitigation strategies. The developed model successfully employs holistic computational dementia risk prediction for clinical use.

q-bio.NC↗

arXiv2023

The SLED (Shelf Life Expiration Date) Tracking System: Using Machine Learning Algorithms to Combat Food Waste and Food Borne Illnesses

The issue of food waste is a major problem contributing to the emission of greenhouse gases into the environment in addition to causing illness in humans. This research aimed to develop a correlation between the amount of time until a food spoiled and dates on food labels in conjunction with sensory observations. Sensory observations are more accurate as they are immediate observations that are specific to the food. This experiment observed bananas, bread, milk, eggs, and leafy greens over a period of time using characteristics specific to the food to quantify food spoilage. It was shown that the actual time until spoilage for all foods was longer than that of the best by date and that sensory observations proved to be a more accurate factor in determining spoilage. From this data, a machine learning algorithm was trained to predict if food was spoiled or not, in addition to the number of days until spoilage. This was presented to the consumer as an app, where the user can track foods and are reminded to check on them to prevent wastage. In addition, the experimental procedures were incorporated into a test kit for the consumer to take instructed observations to assess the spoilage of their food, which are then entered into the app to improve the algorithm. This paper discusses the individual effects of sensorial observations on each food and examines the shifting of consumer habits through an app and test kit to combat environmental consequences of food waste.

q-bio.OT↗

arXiv2023

NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach

Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repurposing has been performed in silico, in which databases of drugs and chemical information are used to determine interactions between target proteins and drug molecules to identify potential drug candidates. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition characterized by seizures, periods of time with bursts of uncontrolled electrical activity in brain cells. Existing drugs for epilepsy can be ineffective and expensive, revealing a need for new antiepileptic drugs. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials. The algorithm has the potential to determine possible drug combinations to prescribe a patient based on a patient's prior medical history. This project examines NeuroCADR, a novel approach to computational drug repurposing capable of revealing potential drug candidates in neurological diseases such as epilepsy.

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