Webinars

An overview of LINCS Data Science Research (DSR) Webinars

The LINCS Data Science Research Webinars serve as a general forum to engage data scientists within and outside of the LINCS project to work on problems related to LINCS data analysis and integration.

Webinars are held on select Tuesdays at 3:00 PM Eastern Time

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For summary information about LINCS Data Science Webinars, please visit the BD2K-LINCS DCIC's webinars page or their YouTube channel.

Upcoming Webinars

There are currently no upcoming webinars. Please check back later.

Past Webinars

Integrative Gene Network Analysis Identifies Key Signatures, Intrinsic Networks and Host Factors for Influenza Virus A Infections

November 14, 2017

Christian Forst PhD

Icahn School of Medicine at Mount Sinai

▸ Abstract

Influenza A virus, with the limited coding capacity of 10 to 14 proteins, requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system, but also provides further targets for antiviral drug development. To uncover novel pathways and key targets of influenza infection, we assembled a large amount of data from 12 cell-based gene-expression studies of influenza infection for an integrative network analysis. We systematically identified differentially expressed genes and gene co-expression networks induced by influenza infection. We revealed the dedicator of cytokinesis 5 (DOCK5) played potentially an important role for influenza virus replication. CRISPR/Cas9 knockout of DOCK5 reduced influenza virus replication, indicating that DOCK5 is a key regulator for the viral life cycle. DOCK5’s targets determined by the DOCK5 knockout experiments strongly validated the predicted gene signatures and networks. This study systematically uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza virus infection.

Watch webinar on YouTube

The Microphysiology Systems Database (MPS-Db): Supporting the Development and Characterization of Tissue Chips and Assays, and Interpretation of the Data

October 31, 2017

Albert Gough PhD

University of Pittsburgh, Drug Discovery Institute

▸ Abstract

Lack of acceptable efficacy and safety continue to limit the efficiency in the discovery and development of therapeutics. The NIH Tissue Chip Consortium is a partnership between NIH/NCATS, organ model Tissue Chip Developers (TCDs), Tissue Chip Testing Centers (TCTCs), Disease Models and Efficacy Testing (DMET) centers, and industry representatives to develop and validate human Microphysiology Systems (MPS) for drug efficacy and toxicity testing. The University of Pittsburgh Drug Discovery Institute (UPDDI) has developed an internet-based Microphysiology Systems Database (MPS-Db) to support the design of MPS Studies, management and analysis of data generated by those studies, and interpretation of the results in the context of curated reference and clinical data. The MPS-Db supports data from plate-based and microfluidic organ models. We are currently developing tools/analyses that will enable the assessment of the reliability and reproducibility of the organ systems, and are designing tools to assess their correlation with clinically relevant responses to drugs.

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Combining Forward and Reverse Engineering to Understand Complex Fractional Killing

April 25, 2017

Tongli Zhang PhD

University of Cincinnati

▸ Abstract

Chemotherapy drugs applied to tumor cells with the same or similar genotypes kill only a percentage of the treated cells. Such fractional killing contributes to drug resistance of cancer. Recent observations indicate that timing of p53 activation decides cellular fate, the cells who activated p53 early undergo apoptosis the cells who activate p53 late survive. The determining role of p53 activation dynamics makes fractional killing a complex dynamical challenge. In order to grasp the essential dynamics of this process, we have constructed a representative model by integrating the control of apoptosis with the relevant signaling pathways. The model successfully recaptured many observed properties of fractional killing, furthermore, analysis of the model suggested that the cell fate is a function of the bifurcation geometry and the cellular trajectories. Hence, the cell fate can be altered in three possible ways: alteration of bifurcation geometry, alteration of cell trajectories or alteration of both. These predicted categories can explain the existing strategies known to combat fractional killing and allow us to design novel strategies. Meanwhile, we also describe our current effort in further extending this model for designing possible strategies that combat fractional killing.

Pharos – A Torch to Use in Your Journey In the Dark Genome

April 18, 2017

Rajarshi Guha PhD

NIH National Center for Advancing Translational Sciences

▸ Abstract

It is well known that a relatively small set of protein targets receive the bulk of research attention and thus funding. However, there are potential (druggable) opportunities in the remaining under-studied and un-studied proteins. To address this the NIH initiated the "Illuminating the Druggable Genome" program to characterize the dark regions of the druggable genome. As part of this program, a Knowledge Management Center (KMC) was created to aggregate and integrate heterogeneous data sources and data types creating a centralized location for information about all protein targets identified as part of the druggable genome. Since then the KMC has expanded to consider the entire human proteome. In this presentation, we describe Pharos, the user interface for the KMC knowledgebase. We provide an overview of the data sources and types made available via Pharos and then describe the architecture of the system and its integration with KMC & external resources. In particular we highlight the rich search facilities that enable a user to drill down to relevant subsets of data but also support the notion of "serendipitous search". Given the heterogeneous set of data types available for individual targets, it is useful to quantify how much and what types of data is available for a target. We describe the development of knowledge profiles and a Knowledge Availability Score (KAS), both derived from the Harmonizome, which is a resource that has characterized data availability across different data sources and types in a uniform manner. We then highlight how the KAS is concordant with knowledge trends characterized by traditional metrics such as publications and grants. We discuss the use of the KAS in the Pharos interface and an example of prioritizing understudied targets by computing the similarity of their knowledge availability profiles with that of well-studied targets.

Watch webinar on YouTube

Illuminating the Druggable Genome: Cure or no Cure?

April 4, 2017

Tudor Oprea MD PhD

University of New Mexico

▸ Abstract

We discuss the human proteome in light of the therapeutic Target Development Levels (TDLs). Four TDLs are defined: Tclin – “clinical”, efficacy targets for known drugs (3% of the proteome); Tchem – “chemical”, proteins for which interactions with small molecules above class-specific cut-offs are known; Tbio – “biological”, proteins for which functional or disease aspects are knonw; Tdark – “ignorome”, proteins that are poorly described based on fractional publication counts (from JensenLab.org), NCBI GeneRIFs and commercially available antibodies (from antibodypedia.com). More about Tclin, Tdark etc. in this Nature Reviews Drug Discovery poster: http://www.nature.com/nrd/posters/druggablegenome/index.html. The second part of my presentation will put the IDG aspects in the light of potential partnership with the pharmaceutical industry, starting from the following: 1) Diseases are concepts, they do not exist outside patients. 2) Successful launch implies good choice of chemical and target. Targets are the weak link. 3) it is time for pharma to declare the Target Selection process as Precompetitive Knowledge. 4) It's neither Affinity, nor Binding mode. It's what happens in patients 5) Pharma and academia should work jointly to select the optimal Disease-Target-Drug tuple.

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Identifying New Antiepileptic Drugs Through Genomics-Based Drug Repurposing

March 21, 2017

Nasir Mirza PhD

University of Liverpool

▸ Abstract

Currently available antiepileptic drugs (AEDs) fail to control seizures in 30% of patients. Genomics-based drug repurposing (GBR) offers the potential of savings in the time and cost of developing new AEDs. In the current study, we used published data and software to identify the transcriptomic signature of chornic temporal lobe epilepsy and the drugs that reverse it. After filtering out compounds based on exclusion criteria, such as toxicity, 36 drugs were retained. 11 of the 36 drugs identified (>30%) have published evidence of the antiepileptic efficacy (for example, curcumin) or antiepileptogenic affect (for example, atorvastatin) in recognised rodent models or patients. By objectively annotating all ∼20,000 compounds in the LINCS database as either having published evidence of antiepileptic efficacy or lacking such evidence, we demonstrated that our set of repurposable drugs is ∼6-fold more enriched with drugs having published evidence of antiepileptic efficacy in animal models than expected by chance (P-value <0.006). Further, we showed that another of our GBR-identified drugs, the commonly-used well-tolerated antihyperglycemic sitagliptin, produces a dose-dependent reduction in seizures in a mouse model of pharmacoresistant epilepsy. In conclusion, GBR successfully identifies compounds with antiepileptic efficacy in animal models and, hence, it is an appealing methodology for the discovery of potential AEDs.

Watch webinar on YouTube

FAIR Dataset Landing Pages for LINCS and BD2K

January 12, 2017

Stephan Schurer PhD

University of Miami, BD2K-LINCS DCIC

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Cell Line Ontology-based Standardization, Integration and Analysis of LINCS Cell Lines

December 20, 2016

Yongqun "Oliver" He DVM, PhD

University of Michigan Medical School, BD2K-LINCS DCIC External Data Science Research Project

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Dynamic Logic-Based Models Integrating Cardiac Signaling Networks and Cellular Phenotypes

December 6, 2016

Jeff Saucerman PhD

University of Virginia, BD2K-LINCS DCIC External Data Science Research Project

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Docker Pipelines for RNASeq Alignment and Analyses

November 22, 2016

Ling-Hong Hung PhD

University of Washington, BD2K-LINCS DCIC External Data Science Research Project

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Integration of Phosphorylation Knowledge Networks with LINCS Omics Data for Cancer Drug Analysis

November 8, 2016

Karen Ross PhD and Cathy Wu PhD

University of Delaware, BD2K-LINCS DCIC External Data Science Research Project

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Unravelling Hairballs: Biological Pathways from Protein Modification Cluster Networks

October 4, 2016

Mark Grimes PhD

University of Montana, BD2K-LINCS DCIC External Data Science Research Project

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Detection and Removal of Spatial Bias in Multi-Well Assays

May 24, 2016

Alexander Lachmann PhD

Ma'ayan Lab, Icahn School of Medicine at Mount Sinai, BD2K-LINCS DCIC

Watch webinar on YouTube

Combining Phenotypic and Biochemical Screening to Identify Drug Targets

November 10, 2015

Hassan Al-Ali PhD

University of Miami Miller School of Medicine

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Imputing New LINCS L1000 Drug Signatures

November 3, 2015

Rachel Hodos

Icahn School of Medicine at Mount Sinai

Watch webinar on YouTube

Using Machine Learning to Identify Aberrant Pathway Activity

September 8, 2015

Stephen Piccolo, PhD

Brigham Young University

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Biomedical Data Integration Toward Quality Healthcare Delivery

August 11, 2015

Qian Zhu, PhD

University of Maryland, Baltimore County

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The Construction of a Cluster Stack Based on Open Source Technologies

June 23, 2015

Matthew Jones

Ma’ayan Lab, Icahn School of Medicine at Mount Sinai, BD2K-LINCS DCIC

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L1K++: A Fast Pipeline that Increases the Accuracy of L1000 Gene Expression Data

June 15, 2015

Ling-Hong Hung PhD

Yeung Group, University of Washington, BD2K-LINCS DCIC External Data Science Research Project

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Drug Toxicity Signature Generation Center

June 9, 2015

Eric Sobie PhD

Icahn School of Medicine at Mount Sinai, DToxS

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Assessing the Global Dimensionality of LINCS Signatures

May 5, 2015

Neil Clark PhD

Ma’ayan Lab, Icahn School of Medicine at Mount Sinai, BD2K-LINCS DCIC

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Integrative Analysis of Proteomics Data of Multi-Layered Post Translational Modifications Collected from Human Lung Cancer Cell Lines

May 2, 2015

Peter Hornbeck PhD, Klarisa Rikova, Mark Grimes PhD, Neil Clark PhD, Nicolas Fernandez PhD

Cell Signaling Technology and Ma’ayan Lab, BD2K-LINCS DCIC External Data Science Research Project

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Target Predictions using LINCS Perturbation Data

April 21, 2015

Yan Xia

Bar-Joseph Group, Carnegie Mellon University, BD2K-LINCS DCIC External Data Science Research Project

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Leveraging Experimental Models to Advance Our Understanding of Cancer

February 24, 2015

Laura Heiser PhD

Oregon Health and Science University, MEP LINCS

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Discussion of Causes for Some Artifacts Observed in L1000 Data

February 17, 2015

William Chad Young

Yeung Group, University of Washington, BD2K-LINCS DCIC External Data Science Research Project

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Large-scale Integration of Small Molecule-induced Responses in Diverse LINCS Datasets

December 16, 2014

Dusica Vidovic PhD

Schurer Group, University of Miami, BD2K-LINCS DCIC

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Analysis of Lung Cancers Across Multiple Signaling Spaces to Identify New Disease Drivers and Network Connectivity

December 9, 2014

Peter Hornbeck PhD

Cell Signaling Technology, BD2K-LINCS DCIC External Data Science Research Project

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Explaining Artifacts in Level 3 L1000 Data, Benchmarking Perturbation Signatures

December 2, 2014

Mario Medvedovic PhD

University of Cincinnati, BD2K-LINCS DCIC

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Creating Signatures from the ENCODE Data

November 18, 2014

Yan Kou

Ma’ayan Lab, Icahn School of Medicine at Mount Sinai, BD2K-LINCS DCIC

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Issues in Level 3 L1000 Data

November 18, 2014

William Chad Young

Yeung Group, University of Washington, BD2K-LINCS DCIC External Data Science Research Project

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LINCS L1000 Analysis with the Characteristic Direction Method

October 28, 2014

Qiaonan Duan

Ma’ayan Lab, Icahn School of Medicine at Mount Sinai, BD2K-LINCS DCIC

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L1000 Indexing Process

October 21, 2014

Amar Koleti

Schurer Group, University of Miami, BD2K-LINCS DCIC

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