Bisakha Ray

An informatics research agenda to support precision medicine: seven key areas

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM’s vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Information content and analysis methods for multi-modal high-throughput biomedical data.

The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect “multi-modal” data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.

Network inference from multimodal data: A review of approaches from infectious disease transmission

Abstract Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.

Design of the first neuronal connectomics challenge: From imaging to connectivity

We are organizing a challenge to reverse engineer the structure of neuronal networks from patterns of activity recorded with calcium fluorescence imaging. Unraveling the brain structure at the neuronal level at a large scale is an important step in brain science, with many ramifications in the comprehension of animal and human intelligence and learning capabilities, as well as understanding and curing neuronal diseases and injuries. However, uncovering the anatomy of the brain by disentangling the neural wiring with its very fine and intertwined dendrites and axons, making both local and far reaching synapses, is a very arduous task: traditional methods of axonal tracing are tedious, difficult, and time consuming. This challenge proposes to approach the problem from a different angle, by reconstructing the effective connectivity of a neuronal network from observations of neuronal activity of thousands of neurons, which can be obtained with state-of-the-art fluorescence calcium imaging. To evaluate the effectiveness of proposed algorithms, we will use data obtained with a realistic simulator of real neurons for which we have ground truth of the neuronal connections. We produced simulated calcium imaging data, taking into account a model of fluorescence and light scattering. The task of the participants is to reconstruct a network of 1000 neurons from time series of neuronal activities obtained with this model. This challenge is part of the official selection of the WCCI 2014 competition program.

Text classification for automatic detection of alcohol use-related tweets: A feasibility study

We present a feasibility study using text classification to classify tweets about alcohol use. Alcohol use is the most widely used substance in the US and is the leading risk factor for premature morbidity and mortality globally. Understanding use patterns and locations is an important step toward prevention, moderation, and control of alcohol outlets. Social media may provide an alternate way to measure alcohol use in real time. This feasibility study explores text classification methodologies for identifying alcohol use tweets. We labeled 34,563 geo-located New York City tweets collected in a 24 hour period over New Years Day 2012. We preprocessed the tweets into stem/ not stemmed and unigram/ bigram representations. We then applied multinomial naïve Bayes, a linear SVM, Bayesian logistic regression, and random forests to the classification task. Using 10 fold cross-validation, the algorithms performed with area under the receiver operating curve of 0.66, 0.91, 0.93, and 0.94 respectively. We also compare to a human constructed Boolean search for the same tweets and the text classification method is competitive with this hand crafted search. In conclusion, we show that the task of automatically identifying alcohol related tweets is highly feasible and paves the way for future research to improve these classifiers.

IEEE ICHI Healthcare Data Analytics Challenge

There are several publicly accessible patient forums where patients can post questions related to their health conditions. The objective of this study was to develop a query-retrieval system that can mine such forums and identify existing questions most similar to the provided question. This pilot study based on a bag-of-words model with latent semantic analysis and cosine similarity suggests that text similarity-based mining holds promise for identification of diabetes-related questions from patient forums and informing self-care management. Further studies involving advance natural language processing tools can be used to reduce false positives and uncover semantically related questions.

Text Classification-Based Automatic Recruitment of Patients for Clinical Trials: A Silver Standards-Based Case Study

A lack of recruitment of appropriate subjects plagues most clinical research trials. One barrier is an efficient way to identify eligible subjects. Researchers worked to harness computing power to improve automated identification of potential subjects for clinical trials with modest success. We use text classification to automatically identify patients for a hypothetical Acute Coronary Syndrome clinical research study from intensive care unit discharge summaries. We apply several state of the art classification methods including Bayesian Logistic Regression, AdaBoost, Support Vector Machines, and Random Forests to build models from administrative manually assigned ICD-9 codes. We then apply these models to discharge summaries labeled by a board certified cardiologist for patients eligible for the hypothetical research study. The best models perform with 0.95 area under the ROC curve for identifying eligible patients. This pilot study suggests that text-based classification holds promise for identification of potential clinical trial subjects. Our methods require further validation in studies involving multiple inclusion and exclusion criteria.