LINCS Virtual Symposium Agenda

Poster Session:

P1: Cardiomyocytes from Healthy Human Subjects to delineate mechanisms of myocardial damage in COVID-19—Mustafa Siddiq, Mt. Sinai School of Medicine, DToxS

P2: Predicting drivers of drug response from baseline omics data across breast cancer cell lines and models—Chiara Victor, Harvard Medical School, HMS LINCS 

P3: DyeDrop: a high-throughput single cell microscopy platform for phenotyping the response of cancer cell lines to therapeutic agents—Mirra Chung, Harvard Medical School, HMS LINCS

P4: Feature Controlled Variational Autoencoder for Single Cell Image Analysis—Luke Ternes, Oregon Health & Science University, MEP-LINCS

P5: Oncostatin M induces migratory and morphological changes in mammary epithelial cells—Ian McLean, Oregon Health & Science University, MEP-LINCS

P6: Appyters: Turning Jupyter Notebooks into Data Driven Web Apps—Daniel Clarke, Mt. Sinai School of Medicine, DCIC

P7: Access to LINCS data via the LINCS Data Portal and Google Big Query—Amar Koleti, University of Miami, DCICC

P8: Cheminformatics survey of LINCS data—Tanya Kelley, University of Miami, DCIC

P9: Sig2Lead: Accelerating Drug Discovery and Re-purposing with Connectivity Enhanced Structure Activity Relationship—Jim Reigel, University of Cincinnati College of Medicine, DCIC

P10: Transcriptional footprints of signaling pathway activity with LINCS genetic loss of function signatures—Yan Ren, University of Cincinnati College of Medicine, DCIC

P11: Results from the LINCS Mechanism of Action (MoA) Prediction Challenge—Andrea Blasco, Broad Transcriptomics

P12: Identification of dysregulated disease networks in ALS—Johnny Li, NeuroLINCS

P13: Robotic Microscopy and Multidimensional Feature Analysis to Interrogate iPSC-Derived Neurons from ALS patients—Julia Kaye, NeuroLINCS

Poster Session:

P1: Structural Signatures for Drug Action—Rayees Rahman, Mt. Sinai, DToxS

P2: Leave-pair-out cross-validation is the most robust method for evaluating model performance in presence of outliers and known confounders—Maulik Nariya, Harvard Medical School, HMS LINCS

P3: Reaction networks and toric geometry in single-cell, multiplex data—Shu Wang, Harvard Medical School, HMS LINCS

P4: A cloud-based pipeline for DIA data analysis enables phosphosignaling studies in genetic risk variants of Alzheimer’s Disease—Karen Christianson, Broad Proteomics

P5: Signature Commons: A Scalable Platform for Serving Diverse Biomedical Data—Erol Evangelista, Mt. Sinai School of Medicine, DCIC

P6: Deep Learning Models to Impute and Transform Gene Expression Across Platforms—Megan Wojciechowicz, Mt. Sinai School of Medicine, DCIC

P7: Automated Vertical Partitioning of Massive Co-Expression RNA-seq Data Improves Gene Function Prediction—Alexander Lachmann, Mt. Sinai School of Medicine, DCIC

P8: FAIR data management in the LINCS Consortium—Dusica Vidovic, University of Miami, DCIC

P9: Discovering existing medicines that abrogate cellular responses to SARS CoV-2 infection—Ted Natoli, Broad Transcriptomics

P10: Integrated Connectivity Analysis of Small-molecule and CRISPR signatures across Large-scale Compendia of Cellular fitness and Gene Expression—Rajiv Narayan, Broad Transcriptomics

P11: Differentially regulated PTMs and biological pathways identified in ALS neuronal cultures by capillary electrophoresis electro-spray ionization mass spectrometry (CESI-MS)—Andrea Matlock, Cedars-Sinai, NeuroLINCS

P12: Using Machine Learning to Uncover Non-linear Genomic Signatures of ALS—Leandro Lima and Julia Kaye, NeuroLINCS

P13: Transcriptomic signatures in motor neuron disease—Jenny Wu and Ryan Lim, NeuroLINCS