Naren Ramakrishnan

Privacy risks in recommender systems

Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.

Photosynthetic Acclimation Is Reflected in Specific Patterns of Gene Expression in Drought-Stressed Loblolly Pine

Because the product of a single gene can influence many aspects of plant growth and development, it is necessary to understand how gene products act in concert and upon each other to effect adaptive changes to stressful conditions. We conducted experiments to improve our understanding of the responses of loblolly pine (Pinus taeda) to drought stress. Water was withheld from rooted plantlets of to a measured water potential of -1 MPa for mild stress and -1.5 MPa for severe stress. Net photosynthesis was measured for each level of stress. RNA was isolated from needles and used in hybridizations against a microarray consisting of 2,173 cDNA clones from five pine expressed sequence tag libraries. Gene expression was estimated using a two-stage mixed linear model. Subsequently, data mining via inductive logic programming identified rules (relationships) among gene expression, treatments, and functional categories. Changes in RNA transcript profiles of loblolly pine due to drought stress were correlated with physiological data reflecting photosynthetic acclimation to mild stress or photosynthetic failure during severe stress. Analysis of transcript profiles indicated that there are distinct patterns of expression related to the two levels of stress. Genes encoding heat shock proteins, late embryogenic-abundant proteins, enzymes from the aromatic acid and flavonoid biosynthetic pathways, and from carbon metabolism showed distinctive responses associated with acclimation. Five genes shown to have different transcript levels in response to either mild or severe stress were chosen for further analysis using real-time polymerase chain reaction. The real-time polymerase chain reaction results were in good agreement with those obtained on microarrays.

'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

Transcriptional Response of Saccharomyces cerevisiae to Desiccation and Rehydration

ABSTRACT A transcriptional analysis of the response of Saccharomyces cerevisiae strain BY4743 to controlled air-drying (desiccation) and subsequent rehydration under minimal glucose conditions was performed. Expression of genes involved in fatty acid oxidation and the glyoxylate cycle was observed to increase during drying and remained in this state during the rehydration phase. When the BY4743 expression profile for the dried sample was compared to that of a commercially prepared dry active yeast, strikingly similar expression changes were observed. The fact that these two samples, dried by different means, possessed very similar transcriptional profiles supports the hypothesis that the response to desiccation is a coordinated event independent of the particular conditions involved in water removal. Similarities between “stationary-phase-essential genes” and those upregulated during desiccation were also noted, suggesting commonalities in different routes to reduced metabolic states. Trends in extracellular and intracellular glucose and trehalose levels suggested that the cells were in a “holding pattern” during the rehydration phase, a concept that was reinforced by cell cycle analyses. Application of a “redescription mining” algorithm suggested that sulfur metabolism is important for cell survival during desiccation and rehydration.

Turning CARTwheels: an alternating algorithm for mining redescriptions

We present an unusual algorithm involving classification trees---CARTwheels---where two trees are grown in opposite directions so that they are joined at their leaves. This approach finds application in a new data mining task we formulate, called redescription mining. A redescription is a shift-of-vocabulary, or a different way of communicating information about a given subset of data; the goal of redescription mining is to find subsets of data that afford multiple descriptions. We highlight the importance of this problem in domains such as bioinformatics, which exhibit an underlying richness and diversity of data descriptors (e.g., genes can be studied in a variety of ways). CARTwheels exploits the duality between class partitions and path partitions in an induced classification tree to model and mine redescriptions. It helps integrate multiple forms of characterizing datasets, situates the knowledge gained from one dataset in the context of others, and harnesses high-level abstractions for uncovering cryptic and subtle features of data. Algorithm design decisions, implementation details, and experimental results are presented.

PYTHIA-II: a knowledge/database system for managing performance data and recommending scientific software

Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHIA-II—a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the users problem (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.

Epidemiological modeling of news and rumors on Twitter

Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.

Data mining: from serendipity to science

The idea of unsupervised learning from basic facts (axioms) or from data has fascinated researchers for decades. Knowledge discovery engines try to extract general inferences from facts or training data. Statistical methods take a more structured approach, attempting to quantify data by known and intuitively understood models. The problem of gleaning knowledge from existing data sources poses a significant paradigm shift from these traditional approaches. The size, noise, diversity, dimensionality, and distributed nature of typical data sets make even formal problem specification difficult. Moreover, you typically do not have control over data generation. This lack of control opens up a Pandoras box filled with issues such as overfitting, limited coverage, and missing/incorrect data with high dimensionality. Once specified, solution techniques must deal with complexity, scalability (to meaningful data sizes), and presentation. This entire process is where data mining makes its transition from serendipity to science.

Reasoning about sets using redescription mining

Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of association rule mining, from finding implications to equivalences; as a form of conceptual clustering, where the goal is to identify clusters that afford dual characterizations; and as a form of constructive induction, to build features based on given descriptors that mutually reinforce each other. In this paper, we present the use of redescription mining as an important tool to reason about a collection of sets, especially their overlaps, similarities, and differences. We outline algorithms to mine all minimal (non-redundant) redescriptions underlying a dataset using notions of minimal generators of closed itemsets. We also show the use of these algorithms in an interactive context, supporting constraint-based exploration and querying. Specifically, we showcase a bioinformatics application that empowers the biologist to define a vocabulary of sets underlying a domain of genes and to reason about these sets, yielding significant biological insight.

A systematic review of studies on forecasting the dynamics of influenza outbreaks

Forecasting the dynamics of influenza outbreaks could be useful for decision‐making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real‐time predictions.