Methods for Ordinal Peer Grading

MOOCs have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback, it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class -- with instructor and TA grades as a baseline -- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.Comment: Submitted to KDD 201

_M. tuberculosis_ interactome analysis unravels potential pathways to drug resistance

Drug resistance is a major problem for combating tuberculosis. Lack of understanding of how resistance emerges in bacteria upon drug treatment limits our ability to counter resistance. By analysis of the _Mycobacterium tuberculosis_ interactome network, along with drug-induced expression data from literature, we show possible pathways for the emergence of drug resistance. To a curated set of resistance related proteins, we have identified sets of high propensity paths from different drug targets. Many top paths were upregulated upon exposure to anti-tubercular drugs. Different targets appear to have different propensities for the four resistance mechanisms. Knowledge of important proteins in such pathways enables identification of appropriate _'co-targets'_, which when simultaneously inhibited with the intended target, is likely to help in combating drug resistance. RecA, Rv0823c, Rv0892 and DnaE1 were the best examples of co-targets for combating tuberculosis. This approach is also inherently generic, likely to significantly impact drug discovery

Doctor of Philosophy

dissertationGlioblastoma is the most common primary malignant brain tumor in adults. It is characterized by extensive invasion, an aberrant local blood brain barrier, and increased intercerebral pressure due to edema. Although there have been several advances in therapeutic strategies to treat gliomas, the current median survival for glioblastoma remains less than 2 years. A major impediment to the treatment of glioblastoma is the lack of drugs that can overcome the blood brain barrier, treat cancer cells, and not affect nearby glia and neurons. Additionally, most therapeutic strategies against cancers merely target cancer growth without affecting invasion, angiogenesis and metastasis. Glycosaminoglycans, particularly heparan sulfate and chondroitin sulfate, are responsible for regulating several pathological processes associated with the progression of glioblastoma. They interact with growth factors, chemokines, and other molecules in the extracellular matrix and within cells, to modulate aberrant cell signaling pathways that influence cancer invasion, metastasis, angiogenesis, and growth. In this dissertation, a therapeutic strategy based on glycosaminoglycan biology is designed and developed to treat gliomas and other cancers in vivo. The strategy is composed of two parts: glycosaminoglycan-based drugs (xylosides and glycosaminoglycan mimetics) and a glycosaminoglycan-based drug delivery vehicle conjugated to doxorubicin. Xylosides are small sugar monomers attached to aglycone moieties that cause cells to produce and release glycosaminoglycan chains without a coreprotein attached. It is shown that upon treatment of gliomas by xylosides, the released glycosaminoglycans dramatically reduce tumor-associated invasion and angiogenesis in vitro. As xylosides are nontoxic even at high dosages, they are an incredibly powerful means to curb tumor invasion and angiogenesis. In addition to xylosides, an optimized heparin-based drug delivery vehicle, composed of heparosan conjugated to aprotinin and doxorubicin, is developed to deliver toxic doses of doxorubicin across the blood brain barrier to gliomas. This conjugate is an exciting therapeutic not only because it can curb glioma growth, but also because it is biodegradable and easy to produce in large quantities. Based on exciting in vivo results in mice, it is expected that this strategy will show promise in future clinical studies in humans

Systems biology

Systems biology seeks to study biological systems as a whole, contrary to the reductionist approach that has dominated biology. Such a view of biological systems emanating from strong foundations of molecular level understanding of the individual components in terms of their form, function and interactions is promising to transform the level at which we understand biology. Systems are defined and abstracted at different levels, which are simulated and analysed using different types of mathematical and computational techniques. Insights obtained from systems level studies readily lend to their use in several applications in biotechnology and drug discovery, making it even more important to study systems as a whol

Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance

<p>Abstract</p> <p>Background</p> <p>Emergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.</p> <p>Results</p> <p>We derive a genome-scale protein-protein interaction network for <it>Mycobacterium tuberculosis </it>H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.</p> <p>Conclusion</p> <p>The study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.</p