IU Indianapolis ScholarWorks :: Browsing by Author "Palakal, Mathew J."

Browsing by Author "Palakal, Mathew J."

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Advanced natural language processing and temporal mining for clinical discovery
(2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. Max
There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
Advancing Toxicology-Based Cancer Risk Assessment with Informatics
(2010-05-03T19:38:33Z) Bercu, Joel P.; Mahoui, Malika; Romero, Pedro R.; Stevens, James L.; Jones, Josette F.; Palakal, Mathew J.
Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.
Aural Mapping of STEM Concepts Using Literature Mining
(2013-03-06) Bharadwaj, Venkatesh; Palakal, Mathew J.; Raje, Rajeev; Xia, Yuni
Recent technological applications have made the life of people too much dependent on Science, Technology, Engineering, and Mathematics (STEM) and its applications. Understanding basic level science is a must in order to use and contribute to this technological revolution. Science education in middle and high school levels however depends heavily on visual representations such as models, diagrams, figures, animations and presentations etc. This leaves visually impaired students with very few options to learn science and secure a career in STEM related areas. Recent experiments have shown that small aural clues called Audemes are helpful in understanding and memorization of science concepts among visually impaired students. Audemes are non-verbal sound translations of a science concept. In order to facilitate science concepts as Audemes, for visually impaired students, this thesis presents an automatic system for audeme generation from STEM textbooks. This thesis describes the systematic application of multiple Natural Language Processing tools and techniques, such as dependency parser, POS tagger, Information Retrieval algorithm, Semantic mapping of aural words, machine learning etc., to transform the science concept into a combination of atomic-sounds, thus forming an audeme. We present a rule based classification method for all STEM related concepts. This work also presents a novel way of mapping and extracting most related sounds for the words being used in textbook. Additionally, machine learning methods are used in the system to guarantee the customization of output according to a user's perception. The system being presented is robust, scalable, fully automatic and dynamically adaptable for audeme generation.
An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data
(2011-10-19) Klann, Jeffrey G.; Schadow, Gunther; Downs, Stephen M.; Finnell, John T.; Palakal, Mathew J.; Szolovits, Peter
Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is available it is frequently incorrect. Therefore it is important to look beyond traditional guideline-based decision support to more readily available resources in order to bring this technology into widespread use. This study proposes that the wisdom of physicians within a practice is a rich, untapped knowledge source that can be harnessed for this purpose. I hypothesize and demonstrate that this wisdom is reflected by order entry data well enough to partially reconstruct the knowledge behind treatment decisions. Automated reconstruction of such knowledge is used to produce dynamic, situation-specific treatment suggestions, in a similar vein to Amazon.com shopping recommendations. This approach is appealing because: it is local (so it reflects local standards); it fits into workflow more readily than the traditional local-wisdom approach (viz. the curbside consult); and, it is free (the data are already being captured). This work develops several new machine-learning algorithms and novel applications of existing algorithms, focusing on an approach called Bayesian network structure learning. I develop: an approach to produce dynamic, rank-ordered situation-specific treatment menus from treatment data; statistical machinery to evaluate their accuracy using retrospective simulation; a novel algorithm which is an order of magnitude faster than existing algorithms; a principled approach to choosing smaller, more optimal, domain-specific subsystems; and a new method to discover temporal relationships in the data. The result is a comprehensive approach for extracting knowledge from order-entry data to produce situation-specific treatment menus, which is applied to order-entry data at Wishard Hospital in Indianapolis. Retrospective simulations find that, in a large variety of clinical situations, a short menu will contain the clinicians' desired next actions. A prospective survey additionally finds that such menus aid physicians in writing order sets (in completeness and speed). This study demonstrates that clinical knowledge can be successfully extracted from treatment data for decision support.
The Axolotl Fibula as a Model for the Induction of Regeneration across Large Segment Defects in Long Bones of the Extremities
(Public Library of Science, 2015) Chen, Xiaoping; Song, Fengyu; Jhamb, Deepali; Li, Jiliang; Bottino, Marco C.; Palakal, Mathew J.; Stocum, David L.; Department of Biology, School of Science
We tested the ability of the axolotl (Ambystoma mexicanum) fibula to regenerate across segment defects of different size in the absence of intervention or after implant of a unique 8-braid pig small intestine submucosa (SIS) scaffold, with or without incorporated growth factor combinations or tissue protein extract. Fractures and defects of 10% and 20% of the total limb length regenerated well without any intervention, but 40% and 50% defects failed to regenerate after either simple removal of bone or implanting SIS scaffold alone. By contrast, scaffold soaked in the growth factor combination BMP-4/HGF or in protein extract of intact limb tissue promoted partial or extensive induction of cartilage and bone across 50% segment defects in 30%-33% of cases. These results show that BMP-4/HGF and intact tissue protein extract can promote the events required to induce cartilage and bone formation across a segment defect larger than critical size and that the long bones of axolotl limbs are an inexpensive model to screen soluble factors and natural and synthetic scaffolds for their efficacy in stimulating this process.
CLIQUES FOR IDENTIFICATION OF GENE SIGNATURES FOR COLORECTAL CANCER ACROSS POPULATION
(Office of the Vice Chancellor for Research, 2012-04-13) Pradhan, Meeta P.; Nagulapalli, Kshithija; Palakal, Mathew J.
Introduction: Colorectal cancer (CRC) is one of the most common cancers diagnosed worldwide. Studies have correlated CRC with dietary habits and environmental conditions. We developed a novel network based approach where cliques and their connectivity profiles explained the variation and similarity in CRC across four populations- China, Germany, Saudi Arabia and USA. Methods: Networks generated after data preprocessing were analyzed individually based on topological and biological features. Using greedy algorithm, cliques of various sizes were identified in each network and size 7 cliques were further analyzed based on their clique connectivity profile (CCP). Our algorithm considered the interaction of cliques based on two parameters: (i) Identification of common (links) genes; (ii) CliqueStrength. The cliques were evaluated by two conditions (a) Maximum number of common genes across cliques and highest CliqueStrength and (b) Minimum number of common genes across cliques and highest CliqueStrength. Results: Large numbers of genes are found to be common between USA, China and Germany. Highly scored nodes based on topological parameters are TP53, SRC, ESR1, SMAD3, GRB2, CREBBP, EGFR, SMAD2, and CSN2KA1. Signal transduction, protein phosphorylation etc., were found to be important GO biological processes. Number of unique size 7 cliques identified in all the population is 650. 49 common cliques identified included genes- EGFR, GRB2, PIK3R1, PTPN6, BRCA1, SMAD2, TP53, CSN2 etc. We found 20 cliques that are uniquely identified for USA, 10 for Germany and one for China. Cliques include genes that are both well studied, less-studied in CRC; but are targets in other cancers. Conclusion: With CCP, we were able to identify commonality, uniqueness and divergence among the populations. Furthermore, comparing all cliques (their CCP) as gene-signatures across populations can help to identify efficient drug targets. Results were consistent with other studies and demonstrate the power of cliques to study CRC across populations.
Condition-specific differential subnetwork analysis for biological systems
(2015-04) Jhamb, Deepali; Liu, Xiaowen; Li, Lang; Liu, Yunlong; Palakal, Mathew J.; Stocum, David L.
Biological systems behave differently under different conditions. Advances in sequencing technology over the last decade have led to the generation of enormous amounts of condition-specific data. However, these measurements often fail to identify low abundance genes/proteins that can be biologically crucial. In this work, a novel text-mining system was first developed to extract condition-specific proteins from the biomedical literature. The literature-derived data was then combined with proteomics data to construct condition-specific protein interaction networks. Further, an innovative condition-specific differential analysis approach was designed to identify key differences, in the form of subnetworks, between any two given biological systems. The framework developed here was implemented to understand the differences between limb regeneration-competent Ambystoma mexicanum and –deficient Xenopus laevis. This study provides an exhaustive systems level analysis to compare regeneration competent and deficient subnetworks to show how different molecular entities inter-connect with each other and are rewired during the formation of an accumulation blastema in regenerating axolotl limbs. This study also demonstrates the importance of literature-derived knowledge, specific to limb regeneration, to augment the systems biology analysis. Our findings show that although the proteins might be common between the two given biological conditions, they can have a high dissimilarity based on their biological and topological properties in the subnetwork. The knowledge gained from the distinguishing features of limb regeneration in amphibians can be used in future to chemically induce regeneration in mammalian systems. The approach developed in this dissertation is scalable and adaptable to understand differential subnetworks between any two biological systems. This methodology will not only facilitate the understanding of biological processes and molecular functions which govern a given system but also provide novel intuitions about the pathophysiology of diseases/conditions.
Context specific text mining for annotating protein interactions with experimental evidence
(2014-01-03) Pandit, Yogesh; Palakal, Mathew J.; Liu, Yunlong; Liu, Xiaowen
Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents.
Data analysis and creation of epigenetics database
(2014-05-21) Desai, Akshay A.; Liu, Xiaowen; Wu, Huanmei; Palakal, Mathew J.
This thesis is aimed at creating a pipeline for analyzing DNA methylation epigenetics data and creating a data model structured well enough to store the analysis results of the pipeline. In addition to storing the results, the model is also designed to hold information which will help researchers to decipher a meaningful epigenetics sense from the results made available. Current major epigenetics resources such as PubMeth, MethyCancer, MethDB and NCBI’s Epigenomics database fail to provide holistic view of epigenetics. They provide datasets produced from different analysis techniques which raises an important issue of data integration. The resources also fail to include numerous factors defining the epigenetic nature of a gene. Some of the resources are also struggling to keep the data stored in their databases up-to-date. This has diminished their validity and coverage of epigenetics data. In this thesis we have tackled a major branch of epigenetics: DNA methylation. As a case study to prove the effectiveness of our pipeline, we have used stage-wise DNA methylation and expression raw data for Lung adenocarcinoma (LUAD) from TCGA data repository. The pipeline helped us to identify progressive methylation patterns across different stages of LUAD. It also identified some key targets which have a potential for being a drug target. Along with the results from methylation data analysis pipeline we combined data from various online data reserves such as KEGG database, GO database, UCSC database and BioGRID database which helped us to overcome the shortcomings of existing data collections and present a resource as complete solution for studying DNA methylation epigenetics data.
Decision Support System for Geriatric Care
Pandit, Yogesh; Palakal, Mathew J.; Jones, Josette; Xia, Yuni; Pecenka, Dave; Bandos, Jean; Tinsley, Eric; Geesaman, Jerry
Geriatrics is a branch in medicine that focuses on the healthcare of the elderly. It is a field that promotes health and aims towards preventing and treating diseases and disabilities in the older people. Geriatric interventions are published in many different articles and journals.

Browsing by Author "Palakal, Mathew J."

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