Simultaneously Segmenting Multiple Gene Expression Time Courses by Analyzing Cluster Dynamics

Tadepalli, Satish; Ramakrishnan, Naren; Watson, Layne T.; Mishra, Bhubaneshwar; Helm, Richard F.

doi:10.1142/s0219720009004114

Cited by 7 publications

(2 citation statements)

References 22 publications

(32 reference statements)

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“…The Gantt chart can be used in several situations, each with a particular view, with the intention to evaluate “temporal dependencies” [ 38 ]. It was used to tell the “historiophoty” of a university department [ 39 ], and, more recently, to describe the clinical course of COVID-19 cases across time [ 40 ], and to describe cases of COVID-19 in some psychiatric facilities [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring the Gantt chart as a tool to highlight double report in case series published during the first wave of the COVID-19 pandemic

et al. 2022

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Background During the COVID-19 pandemic, some studies describing different aspects of the infection included very similar participants, rising suspicion about double reporting. We aimed to evaluate the Gantt chart as a tool to highlight possible double reporting. The chart is routinely used in business applications to depict tasks of a project, by plotting horizontal bars against time, showing their time span and overlaps. Methods All case reports and case series of pregnant women with COVID-19, published by July 15, 2020, were included. Initial and final dates of participants’ enrollment, country, city, hospital, and number of pregnancies were plotted in the Gantt chart. Bars stand for enrollment dates of each study, according to hospital and city, thus allowing comparisons. Results We included 116 articles in the present analysis. The Gantt chart highlighted papers in which some participants were likely the same, thus allowing easier identification of double reporting of cases. Combining all information and pregnancy characteristics and outcomes helped to recognize duplications when the authors did not acknowledged the previous publication. Conclusions Unintended double reporting may occur, especially in exceptional times. The Gantt chart may help researchers to visually identify potential duplications, thus avoiding biased estimates in systematic reviews or meta-analysis.

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Section: Discussionmentioning

confidence: 99%

Exploring the Gantt chart as a tool to highlight double report in case series published during the first wave of the COVID-19 pandemic

et al. 2022

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“…Also, in this work, we have relied on domain knowledge to determine interval lengths for snapshots. In practice, in the absence of domain information, methods such as time series segmentation and smoothing can be used to derive suitable time intervals [Tadepalli et al 2008].…”

Section: Discussionmentioning

confidence: 99%

An event-based framework for characterizing the evolutionary behavior of interaction graphs

Asur

Parthasarathy

Ucar

2009

ACM Trans. Knowl. Discov. Data

223

167

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Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an eventbased characterization of critical behavioral patterns for temporally varying interaction graphs. We use nonoverlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We show how semantic information can be incorporated to reason about community-behavior events. We also demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.

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Adaptive evolutionary clustering

Kliger²,

Hero

2013

Data Min Knowl Disc

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In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.

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Simultaneously Segmenting Multiple Gene Expression Time Courses by Analyzing Cluster Dynamics

Cited by 7 publications

References 22 publications

Exploring the Gantt chart as a tool to highlight double report in case series published during the first wave of the COVID-19 pandemic

Exploring the Gantt chart as a tool to highlight double report in case series published during the first wave of the COVID-19 pandemic

An event-based framework for characterizing the evolutionary behavior of interaction graphs

Adaptive evolutionary clustering

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