Abhishek Tripathi

Metabolic regulation in progression to autoimmune diabetes

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes.

Combined use of expression and CGH arrays pinpoints novel candidate genes in Ewing sarcoma family of tumors

BackgroundEwing sarcoma family of tumors (ESFT), characterized by t(11;22)(q24;q12), is one of the most common tumors of bone in children and young adults. In addition to EWS/FLI1 gene fusion, copy number changes are known to be significant for the underlying neoplastic development of ESFT and for patient outcome. Our genome-wide high-resolution analysis aspired to pinpoint genomic regions of highest interest and possible target genes in these areas.MethodsArray comparative genomic hybridization (CGH) and expression arrays were used to screen for copy number alterations and expression changes in ESFT patient samples. A total of 31 ESFT samples were analyzed by aCGH and in 16 patients DNA and RNA level data, created by expression arrays, was integrated. Time of the follow-up of these patients was 5–192 months. Clinical outcome was statistically evaluated by Kaplan-Meier/Logrank methods and RT-PCR was applied on 42 patient samples to study the gene of the highest interest.ResultsCopy number changes were detected in 87% of the cases. The most recurrent copy number changes were gains at 1q, 2, 8, and 12, and losses at 9p and 16q. Cumulative event free survival (ESFT) and overall survival (OS) were significantly better (P < 0.05) for primary tumors with three or less copy number changes than for tumors with higher number of copy number aberrations. In three samples copy number imbalances were detected in chromosomes 11 and 22 affecting the FLI1 and EWSR1 loci, suggesting that an unbalanced t(11;22) and subsequent duplication of the derivative chromosome harboring fusion gene is a common event in ESFT. Further, amplifications on chromosomes 20 and 22 seen in one patient sample suggest a novel translocation type between EWSR1 and an unidentified fusion partner at 20q. In total 20 novel ESFT associated putative oncogenes and tumor suppressor genes were found in the integration analysis of array CGH and expression data. Quantitative RT-PCR to study the expression levels of the most interesting gene, HDGF, confirmed that its expression was higher than in control samples. However, no association between HDGF expression and patient survival was observed.ConclusionWe conclude that array CGH and integration analysis proved to be effective methods to identify chromosome regions and novel target genes involved in the tumorigenesis of ESFT.

Matching samples of multiple views

Multi-view learning studies how several views, different feature representations, of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the absence of co-occurrence, when the views are observed separately and the identities of objects are not known. As a practical example, joint analysis of mRNA and protein concentrations requires mapping between genes and proteins. We introduce a data-driven approach for learning the correspondence of the observations in the different views, in order to enable joint analysis also in the absence of known co-occurrence. The method finds a matching that maximizes statistical dependency between the views, which is particularly suitable for multi-view methods such as canonical correlation analysis which has the same objective. We apply the method to translational metabolomics, to identify differences and commonalities in metabolic processes in different species or tissues. The metabolite identities and roles in the different species are not generally known, and it is necessary to search for a matching. In this paper we show, using different metabolomics measurement batches as the views so that the ground truth is known, that the metabolite identities can be reliably matched by a consensus of several matching solutions.

Bilingual sentence matching using Kernel CCA

The problem of matching samples between two data sets is a fundamental task in unsupervised learning. In this paper we propose an algorithm based on statistical dependency between the data sets to solve the matching problem in a general case when samples in both data sets have different feature representations. As a concrete example, we consider the task of sentence-level alignment of parallel corpus based on monolingual data. Multilingual text collections with sentence-level alignment are required by statistical machine translation methods. We show how statistical dependencies between feature representations of partially aligned (e.g., paragraph-level alignment) corpora can be used to learn sentence-level alignment in a data-driven way. Our novel matching algorithm based on Kernel Canonical Correlation Analysis (KCCA) outperforms an earlier algorithm using linear CCA.

Using dependencies to pair samples for multi-view learning

Several data analysis tools such as (kernel) canonical correlation analysis and various multi-view learning methods require paired observations in two data sets. We study the problem of inferring such pairing for data sets with no known one-to-one pairing. The pairing is found by an iterative algorithm that alternates between searching for feature representations that reveal statistical dependencies between the data sets, and finding the best pairs for the samples. The method is applied on pairing probe sets of two different microarray platforms.

RoadEye: A System for Personalized Retrieval of Dynamic Road Conditions

Awareness of dynamically changing road conditions is crucial for a safe and quality driving experience, as well as, in augmenting trip planning. This work addresses the problem of keeping users informed in a timely and personalized manner about road conditions arising from both scheduled and ad hoc events. We propose Road Eye, a system for personalized retrieval of dynamic road conditions. The key contribution of Road Eye is the psi R-tree, which is a novel R-tree-based index augmented with linked lists for facilitating quick and personalized retrieval of user-queried road conditions. Our performance study indicates that the psi R-tree is indeed effective in retrieving dynamic road conditions with reduced query response times and disk I/Os.

Personalized Messaging Engine: The Next Step in Employee Engagement

Employers today are struggling to engage positively with their employees to reduce attrition and improve productivity. There are solutions in the market which are trying to solve the problem but they suffer from two critical issues. Firstly, the scope of the existing solutions is too narrow to capture each and every interaction happening within the company. Secondly, their learning from the employee behaviour is either non-existent or minimal at best. Personalized Messaging Engine (PME) is an attempt to provide end-to-end system to organizations for effective employee engagement. PME uses SOA principles to connect to each and every system through which employees engage with their employers. It uses the data aggregated from multiple systems to provide a hyper-personalized and dynamic experience to each employee. With the help of APIs, multiple systems can push data to PME and it then processes the data to send relevant pre-configured messages to the employees in the domain of Health, Wealth and Career. Additionally, PME uses several factors to prioritize messages for each and every employee. It uses a state-of-the-art learning engine to combine Subject Matter Experts opinion, Client Strategy, User Experiences and behaviour to find the messages which are most effective for the employees.

A Team Hiring Solution Based on Graph-Based Modelling of Human Resource Entities

As modern organizations become more agile and support more complex business processes, acquiring the right set of talent is becoming crucial for their operations. One of the key talent acquisition problems is staffing a team that has requirement for multiple job descriptions, from a pool of external candidates. This team hiring problem may arise for (i) a new organization, (ii) a new group in an existing organization, or (iii) an existing group that faces high attrition level. This paper presents a Talent Acquisition Decision Support System (TADSS) that provides decision support for team hiring. The system first builds a weighted graph based model for the three types of Human Resource (HR) entities in the problem setup (jobs, employees and candidates), and the inter-relationship among them. Next, an algorithm based on spectral embedding of the HR Graph is used to select teams. The system then provides an interactive team selection and comparison interface based on the HR Graph. Simulation-based evaluations show the effectiveness of the proposed system in team formation.

Evaluating vector space models with canonical correlation analysis

Vector space models are used in language processing applications for calculating semantic similarities of words or documents. The vector spaces are generated with feature extraction methods for text data. However, evaluation of the feature extraction methods may be difficult. Indirect evaluation in an application is often time-consuming and the results may not generalize to other applications, whereas direct evaluations that measure the amount of captured semantic information usually require human evaluators or annotated data sets. We propose a novel direct evaluation method based on canonical correlation analysis (CCA), the classical method for finding linear relationship between two data sets. In our setting, the two sets are parallel text documents in two languages. A good feature extraction method should provide representations that reflect the semantic content of the documents. Assuming that the underlying semantic content is independent of the language, we can study feature extraction methods that capture the content best by measuring dependence between the representations of a document and its translation. In the case of CCA, the applied measure of dependence is correlation. The evaluation method is based on unsupervised learning, it is languageand domain-independent, and it does not require additional resources besides a parallel corpus. In this paper, we demonstrate the evaluation method on a sentence-aligned parallel corpus. The method is validated by showing that the obtained results with bag-of-words representations are intuitive and agree well with the previous findings. Moreover, we examine