Developing A Tool For Interactive In Silico Analysis of Medicinal Plant Extracts From In House Medicinal Plant Database

Bioinformatics plays key role in creating useful information from raw biological data. So in this project a bioinformatics tool has been designed and linked to the in house developed database for analysis of medicinal plant extracts. Various small molecules i.e. alkaloids, flavonoid, glycosides are the main extracts from plants which are widely used as established therapeutics for an array of human diseases. The current work has focused mainly on those molecules. So we have designed an application which is capable of finding similarity among two small molecules based on their structure. This similarity tool combined with other tools such as molecular format conversion tool are designed to make the research process easy for end user. Finally we have created a tool for automated docking of selected similar molecules to a protein of interest. This process would identify new drug molecules. In addition, the target protein of interest can be sent for homology modeling directly from the application to get proteins with similar 3D structure and folding. Various permutations and combinations can be applied between ligand (small molecules) and the whole range of proteins. In nut a shell, the application utilizes a versatile algorithm for discovering newer ligands as well as newer target proteins to intervene various pathways leading to disease. This application would definitely help the researchers to a great extent in finding new small molecules since the need of similar molecule finding is of great importance in drug discovery process

DEEP LEARNING METHODS FOR MULTI-MODAL HEALTHCARE DATA

Abstract: Today, enormous transformations are happening in health care research and applications. In the past few years, there has been exponential growth in the amount of healthcare data generated from multiple sources. This growth in data has led to many new possibilities and opportunities for researchers to build different models and analytics for improving healthcare for patients. While there has been an increase in research and successful application of prediction and classification tasks, there are many other challenges in improving overall healthcare. Some of these challenges include optimizing physician performance, reducing healthcare costs, and discovering new treatments for diseases. - Often, doctors have to perform many time-consuming tasks, which leads to fatigue and misdiagnosis. Many of these tasks could be automated to save time and release doctors from menial tasks enabling them to spend more time improving the quality of care. - Health dataset contains multiple modalities such as structured sequence, unstructured text, images, ECG, and EEG signals. Successful application of machine learning requires methods to utilize these diverse data sources. - Finally, current healthcare is limited by the treatments available on the market. Often, many treatments do not make it beyond clinical trials, which leads to a lot of lost opportunities. It is possible to improve the outcome of clinical trials and ultimately improve the quality of treatment for the patients with machine learning models for different clinical trial-related tasks. In this dissertation, we address these challenges by - Predictive Models: Building deep learning models for sleep clinics to save time and effort needed by doctors for sleep staging, apnea, limb movement detection - Generative Models: Developing multimodal deep learning systems that can produce text reports and augment doctors in clinical practice. - Interpretable Representation Models: Applying multimodal models to help in clinical trial recruitment and counterfactual explanations for clinical trial outcome predictions to improve clinical trial success.Ph.D

Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on {\it clinical trial recruitment}, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose doctor2vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, doctor2vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, doctor2vec demonstrated improved performance over the best baseline by up to $8.7\%$ in PR-AUC. We also demonstrated that the doctor2vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with $13.7\%$ improvement or for rare diseases with $8.1\%$ improvement in PR-AUC.Comment: Accepted by AAAI 202

CONAN: Complementary Pattern Augmentation for Rare Disease Detection

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved .96 precision recall area under the curve (PR-AUC) and 50.1% relative improvement over best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22 PR-AUC with 41.3% relative improvement over the best baseline

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST.Comment: Accepted to WWW 202