Article
Automation & Control Systems
Laura Forastiere, Fabrizia Mealli, Albert Wu, Edoardo M. Airoldi
Summary: In this study, a new covariate-adjustment estimator is proposed to estimate the direct treatment and spillover effects in observational studies on networks. Under assumptions of neighborhood interference and unconfoundedness of individual and neighborhood treatment, the estimator balances individual and neighborhood covariates using a generalized propensity score and conducts adjustment using penalized spline regression. The Bayesian inference strategy accounts for uncertainty in propensity score estimation and incorporates random effects and community detection algorithm to model the correlation among connected units. A simulation study is conducted to evaluate the performance of the proposed estimator on different network topologies.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Biochemical Research Methods
Ramon Vinas, Helena Andres-Terre, Pietro Lio, Kevin Bryson
Summary: The study developed a method based on conditional generative adversarial networks to generate realistic transcriptomics data for Escherichia coli and humans. Results showed that the approach performed better in preserving gene expression properties compared to existing simulators, maintaining tissue- and cancer-specific attributes, and exhibiting real gene clusters and ontologies at different scales.
Article
Biochemical Research Methods
Cassandra Burdziak, Chujun Julia Zhao, Doron Haviv, Direna Alonso-Curbelo, Scott W. Lowe, Dana Pe'er
Summary: scKINETICS is a dynamical model that fits gene expression change with the learning of per-cell transcriptional velocities and a governing gene regulatory network. It successfully recapitulates the process of acinar-to-ductal transdifferentiation and proposes novel regulators of this process in an acute pancreatitis dataset. In benchmarking experiments, scKINETICS extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics.
Article
Biochemical Research Methods
Sara Mohammad-Taheri, Jeremy Zucker, Charles Tapley Hoyt, Karen Sachs, Vartika Tewari, Robert Ness, Olga Vitek
Summary: This article proposes a general and practical approach for estimating causal queries based on latent variable models. It proves the accuracy of the approach under specific conditions and demonstrates its broad applicability and practicality through synthetic and experimental case studies.
Article
Biochemistry & Molecular Biology
Ruishan Liu, Angela Oliveira Pisco, Emelie Braun, Sten Linnarsson, James Zou
Summary: Recent development in inferring RNA velocity from single-cell RNA-seq provides new insights into developmental lineage and cellular dynamics. This study presents RNA-ODE, a computational framework based on ordinary differential equations, that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. Experimental results demonstrate that RNA-ODE improves the estimation of cell state lineage, pseudo-time, and gene regulatory networks compared to previous methods.
JOURNAL OF MOLECULAR BIOLOGY
(2022)
Article
Biology
Weiyang Tao, Timothy R. D. J. Radstake, Aridaman Pandit
Summary: RegEnrich is an open-source Bioconductor R package that can identify transcriptional regulators driving gene expression signatures through multiple analysis methods. This tool has important implications in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and drug development.
COMMUNICATIONS BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Stephanie Noble, Amanda F. Mejia, Andrew Zalesky, Dustin Scheinost
Summary: In neuroimaging, inference is often made at the level of focal brain areas or circuits. However, recent studies have shown that broad-scale effects distributed throughout the brain may play a more important role than previously thought. This study evaluates the impact of focal versus broad-scale perspectives on inference using real data, and finds that broad-scale procedures have higher statistical power and sensitivity.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Multidisciplinary Sciences
Hayato Idei, Wataru Ohata, Yuichi Yamashita, Tetsuya Ogata, Jun Tani
Summary: This study explores the mechanism behind sensory attenuation through a simulation using a neural network model. It suggests that sensory attenuation can develop through learning self-produced or externally produced exteroceptions, with prediction error and executive control playing crucial roles.
SCIENTIFIC REPORTS
(2022)
Article
Political Science
Naoki Egami
Summary: In social science experiments, the outcome of an individual can be influenced by the treatment status of others, requiring separate estimation of spillover effects in specific networks. Researchers can use parametric and nonparametric sensitivity analysis methods to assess the potential influence of unobserved networks on causal findings.
POLITICAL ANALYSIS
(2021)
Article
Mathematics, Applied
Sergio Conti, Franca Hoffmann, Michael Ortiz
Summary: We introduce a model-free data-driven inference method for physical systems, allowing inferences on system outcomes directly from empirical data without intermediate modeling. We consider physical systems with states characterized by phase space points defined by governing field equations. By introducing the notion of intersection between measures, we quantify the likelihood of system outcomes. We provide conditions for characterizing the intersection as the athermal limit and derive explicit analytic expressions for outcome expectations.
ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
(2023)
Article
Multidisciplinary Sciences
Takuya Isomura, Kiyoshi Kotani, Yasuhiko Jimbo, Karl J. Friston
Summary: Empirical applications of the free-energy principle entail a commitment to a particular process theory. Here, the authors reverse engineered generative models from neural responses of in vitro networks and demonstrated that the free-energy principle could predict how neural networks reorganized in response to external stimulation.
NATURE COMMUNICATIONS
(2023)
Article
Genetics & Heredity
Juan M. Escorcia-Rodriguez, Estefani Gaytan-Nunez, Ericka M. Hernandez-Benitez, Andrea Zorro-Aranda, Marco A. Tello-Palencia, Julio A. Freyre-Gonzalez
Summary: Gene regulatory networks are models representing cellular transcription events. Time and resource constraints make them far from complete. Previous assessments have shown the limitations of methods for inferring these networks. This study highlights the importance of data quality, assessment approach, and network structure for accurate network inference.
FRONTIERS IN GENETICS
(2023)
Article
Genetics & Heredity
Parul Maheshwari, Sarah M. Assmann, Reka Albert
Summary: This article presents a streamlined network inference method that combines causal logic analysis with Boolean modeling, reducing the manual work required for biological network construction and proving its effectiveness by applying it in plant hormone signaling.
FRONTIERS IN GENETICS
(2022)
Article
Multidisciplinary Sciences
Matteo Serafino, Giulio Cimini, Amos Maritan, Andrea Rinaldo, Samir Suweis, Jayanth R. Banavar, Guido Caldarelli
Summary: Through finite size scaling analysis of real network datasets, it was found that many networks follow a finite size scaling hypothesis without any self-tuning. Biological protein interaction networks, technological computer and hyperlink networks, and informational networks in general tend to adhere to this hypothesis. However, marked deviations appear in some cases, especially involving infrastructure and transportation as well as social networks.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Statistics & Probability
Laura Forastiere, Edoardo M. Airoldi, Fabrizia Mealli
Summary: This article addresses the technical challenges of causal inference on a population of units connected through a network, with a focus on how to handle interference. By proposing new estimands, extending the unconfoundedness assumption, and developing new covariate-adjustment methods, valid estimates of treatment and interference effects in observational studies on networks are achieved. The study also explores the finite-sample performance in different realistic settings using simulations based on friendship networks and covariates in a nationally representative longitudinal study of adolescents in the United States.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Oncology
Georgina D. Barnabas, Joo Sang Lee, Tamar Shami, Michal Harel, Lir Beck, Michael Selitrennik, Livnat Jerby-Arnon, Neta Erez, Eytan Ruppin, Tamar Geiger
Summary: This study highlights the crucial role of IDH2 in triple-negative breast cancer (TNBC) and HER2, and demonstrates its oncogenic effects in cell proliferation, glycolysis, and other metabolic processes. PHGDH and PSAT1 are identified as synthetic dosage lethal (SDL) partners of IDH2, and their knockout shows the essentiality in IDH2-high cells. Clinical findings suggest that patients with IDH2-high/PHGDH-low tumors have longer survival, and PHGDH inhibitors show effectiveness in treating IDH2-high cells.
Correction
Genetics & Heredity
Erez Persi, Yuri I. Wolf, David Horn, Eytan Ruppin, Francesca Demichelis, Robert A. Gatenby, Robert J. Gillies, Eugene V. Koonin
NATURE REVIEWS GENETICS
(2021)
Review
Genetics & Heredity
Erez Persi, Yuri Wolf, David Horn, Eytan Ruppin, Francesca Demichelis, Robert A. Gatenby, Robert J. Gillies, Eugene Koonin
Summary: Intratumour heterogeneity and phenotypic plasticity enabled by various somatic aberrations, epigenetic and metabolic adaptations, play a crucial role in helping cancers resist treatment and survive under environmental stress. Understanding the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection, prevention, and development of efficient therapeutic strategies for cancer.
NATURE REVIEWS GENETICS
(2021)
Article
Multidisciplinary Sciences
Kuoyuan Cheng, Nishanth Ulhas Nair, Joo Sang Lee, Eytan Ruppin
Summary: This study investigates the role of synthetic lethality in cancer risk, finding that the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs in normal tissues is associated with lower and delayed cancer risk. The up-regulation of more cSL gene pairs in cells exposed to carcinogens and in premalignant stages suggests a potential role of synthetic lethality in tumorigenesis. Moreover, the tissue specificity of tumor suppressor genes is linked to the expression of their cSL partner genes in normal tissues.
Article
Multidisciplinary Sciences
Mahashweta Basu, Kun Wang, Eytan Ruppin, Sridhar Hannenhalli
Summary: This study demonstrates that an individual's whole blood transcriptome can predict tissue-specific expression levels for around 60% of genes across various tissues, with particularly high accuracy in skeletal muscle. Predictions based on blood transcriptome are almost as effective as actual tissue expression in identifying disease states for six different complex disorders, surpassing the traditional blood transcriptome approach. The development of TEEBoT provides a valuable tool for further research in other medical conditions.
Article
Oncology
Vishaka Gopalan, Arashdeep Singh, Farid Rashidi Mehrabadi, Li Wang, Eytan Ruppin, H. Efsun Arda, Sridhar Hannenhalli
Summary: This study identifies edge epithelial cell states with oncogenic transcriptional activity in human organs without oncogenic mutations, with a particular focus on pancreatic ductal adenocarcinoma (PDAC). It also highlights the increase in the fraction of acinar cells with age in the pancreas, as well as the significantly higher presence of AE-like cells in human pancreatitis samples.
Article
Biology
Ariel Israel, Alejandro A. Schaffer, Assi Cicurel, Kuoyuan Cheng, Sanju Sinha, Eyal Schiff, Ilan Feldhamer, Ameer Tal, Gil Lavie, Eytan Ruppin
Summary: This study aimed to investigate the impact of existing medications on the risk of severe COVID-19 hospitalization and found that several medications, including ubiquinone, ezetimibe, rosuvastatin, flecainide, and vitamin D, were associated with reduced risk of hospitalization. These findings suggest a promising protective effect that warrants further investigation in prospective studies.
Article
Biochemistry & Molecular Biology
Kuoyuan Cheng, Laura Martin-Sancho, Lipika R. Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K. Chanda, Eytan Ruppin
Summary: The study utilized genome-scale metabolic modeling to analyze host metabolism changes during SARS-CoV-2 infection, predicted anti-viral targets, validated these targets using drug and genetic screening data, and provided potential anti-SARS-CoV-2 targets supported by clinical data for future evaluation.
MOLECULAR SYSTEMS BIOLOGY
(2021)
Article
Oncology
Kun Wang, Sushant Patkar, Joo Sang Lee, E. Michael Gertz, Welles Robinson, Fiorella Schischlik, David R. Crawford, Alejandro A. Schaeffer, Eytan Ruppin
Summary: This study presents two new computational methods that deconvolve tumor gene expression profiles and predict response to immune checkpoint blockade therapy.
Article
Medicine, General & Internal
Ariel Israel, Eugene Merzon, Alejandro A. Schaffer, Yotam Shenhar, Ilan Green, Avivit Golan-Cohen, Eytan Ruppin, Eli Magen, Shlomo Vinker
Summary: In this study, it was found that the risk of COVID-19 infection gradually increased in adults who received their second dose of the BNT162b2 mRNA vaccine after at least 90 days, based on electronic health records.
BMJ-BRITISH MEDICAL JOURNAL
(2021)
Article
Immunology
Ariel Israel, Yotam Shenhar, Ilan Green, Eugene Merzon, Avivit Golan-Cohen, Alejandro A. Schaeffer, Eytan Ruppin, Shlomo Vinker, Eli Magen
Summary: This study demonstrates that individuals who received the Pfizer-BioNTech mRNA vaccine have higher initial levels of antibodies compared to patients who had been infected with the SARS-CoV-2 virus, but experience a much faster exponential decrease in antibody levels.
Article
Oncology
Marzia Scortegagna, Yuanning Du, Linda M. Bradley, Kun Wang, Alfredo Molinolo, Eytan Ruppin, Rabi Murad, Ze'ev A. Ronai
Summary: Cellular components, such as myeloid cells, in the tumor microenvironment have significant impact on the progression and treatment response of lung adenocarcinoma (LUAD). This study reveals that the ubiquitin ligases Siah1a/2 regulate the differentiation and activity of alveolar macrophages (AM), and their control of AMs influences carcinogen-induced LUAD. The findings indicate that Siah1a/2 in AMs act as gatekeepers of lung cancer development by controlling inflammatory signaling, differentiation, and profibrotic phenotypes.
Review
Oncology
Peng Jiang, Sanju Sinha, Kenneth Aldape, Sridhar Hannenhalli, Cenk Sahinalp, Eytan Ruppin
Summary: Historically, cancer research has focused on a few essential pathways and genes, but recent advances in high-throughput technologies have led to the rapid accumulation of large-scale cancer omics data. The analysis of this "big data" requires significant computational resources and has the potential to bring new insights to cancer research. The combination of big data, bioinformatics, and artificial intelligence has already made notable advances in our understanding of cancer biology and translational research. Future progress will require collaboration among data scientists, clinicians, biologists, and policymakers.
NATURE REVIEWS CANCER
(2022)
Article
Oncology
Neelam Sinha, Sanju Sinha, Cristina Valero, Alejandro A. Scha, Kenneth Aldape, Kevin Litch, Timothy A. Chan, Luc G. T. Morris, Eytan Ruppin
Summary: This study uncovers immune-related factors that may modulate the relationship between high tumor mutational burden and ICI response, which can help prioritize cancer types for clinical trials.
Meeting Abstract
Oncology
Xuan C. Li, Yuelin Liu, Farid Rashidi, Salem Malikic, Stephen M. Mount, Eytan Ruppin, Kenneth Aldape, Cenk Sahinalp