Medical Data Analysis Method For Epilepsy

Applying data mining techniques on medical databases which contain un-structured and semi-structured data is a challenging task. It is not only due to the complexity of such databases but also due to the characteristics of the medical domain. This thesis describes how multiple layers of data mining techniques have been applied to a Human Brain Image Database system. It starts with data preparation which paves the way for conventional data analysis techniques to be applied to the data. A similarity based patient retrieval tool has been designed and developed to assist in treatment planning and outcome estimation for epileptic patients. Finally connected scatter-plot visualization tool has been designed and implemented in order to assist the medical experts to see the relationship between attributes and visually compare a new patient\u27s similarity scores against patients that have been previously operated on in the hospital

Anomalous QBO influence in the long period Kelvin waves in the low latitude mesosphere and lower thermosphere region over Kolhapur (16.7N, 74.2E)

15th MST Radar WorkshopSession M6: Middle atmosphere dynamics and structureMay 31 (Wed), NIPR Auditoriu

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Computational Methods for Optimization of Biological Organisms

Computational methods play an irreplaceable role for optimization of biological organisms in the era of high-resolution omics, genetic engineering, and high-performance computing. A general overview of computational methods for optimization of biological organisms is presented in Chapter 1 with a focus on three main challenges relating to data scarcity and heterogeneity, model interpretability, and the large number of factors that can affect an organisms’ phenotype. Recent advances are discussed in Chapter 2 with a forward-looking view on the application of computational methods for microbiome-based diet and health optimization. In Chapter 3, existing computational methods are applied for microbiome-based diet optimization in irritable bowel syndrome (IBS). The integrated data analysis results argue that there are two types of patients distinguishable by their fecal samples, those with high colonic methane and SCFA production, who will respond well on a low-FODMAP diet, and all others, who would benefit a dietary supplementation containing butyrate and propionate, as well as probiotics with SCFA-producing bacteria, such as lactobacillus. In Chapter 4, a novel artificial neural network (ANN) architecture called genetic neural network (GNN) is presented that captures the dependencies and non-linear dynamics that exist in gene networks into the GNN architecture. The results argue for 40% more accuracy of GNNs compared to several common ANNs in predicting genome-wide gene expression given gene knockouts and master regulator perturbations in bacterium E. coli. In Chapter 5, a novel group testing method called algorithmic lifestyle optimization (ALO) is presented for rapid identification of effective lifestyle interventions in individuals. ALO is robust to noise, data size and data heterogeneity, is between 58.9% and 68.4% more efficient compared to standard elimination diet for identification of food items that exacerbate IBS symptoms and allergic reactions, and better than alternative state of the art group testing method for this application. The conclusions and future directions are discussed at the end of each chapter and summarized in the final chapter. Chapters 2, 3 and 4 are published (1–3)

Algorithmic lifestyle optimization.

OBJECTIVE: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual. MATERIALS AND METHODS: We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group. RESULTS: Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individuals food intolerances and allergies to 19-56 foods. DISCUSSION: ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individuals subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints. CONCLUSION: ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED

Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening

The objective of this study is to validate reduced graphene oxide (RGO)-based volatile organic compounds (VOC) sensors, assembled by simple and low-cost manufacturing, for the detection of disease-related VOCs in human breath using machine learning (ML) algorithms. RGO films were functionalized by four different metalloporphryins to assemble cross-sensitive chemiresistive sensors with different sensing properties. This work demonstrated how different ML algorithms affect the discrimination capabilities of RGO–based VOC sensors. In addition, an ML-based disease classifier was derived to discriminate healthy vs. unhealthy individuals based on breath sample data. The results show that our ML models could predict the presence of disease-related VOC compounds of interest with a minimum accuracy and F1-score of 91.7% and 83.3%, respectively, and discriminate chronic kidney disease breath with a high accuracy, 91.7%

DeepPep overview.

<p>DeepPep takes as an input a set of strings for sequences of all the protein matches to an observed peptide. (A) To train the model for a specific peptide, each protein sequence string is converted to binary with ones where the peptide sequence matches that of the protein sequence, and zero everywhere else. (B) A CNN is then trained to predict the peptide probability. A peptide probability is the probability that the peptide that is identified through a database search from the mass spectra is the correct one. (C) The effect of a protein removal to a peptide probability is then calculated for all proteins and all peptides. (D) Finally, we score proteins based on differential change of each protein in CNN when it is present/absent.</p