22 research outputs found
Direct Estimation of Differences in Causal Graphs
We consider the problem of estimating the differences between two causal
directed acyclic graph (DAG) models with a shared topological order given
i.i.d. samples from each model. This is of interest for example in genomics,
where changes in the structure or edge weights of the underlying causal graphs
reflect alterations in the gene regulatory networks. We here provide the first
provably consistent method for directly estimating the differences in a pair of
causal DAGs without separately learning two possibly large and dense DAG models
and computing their difference. Our two-step algorithm first uses invariance
tests between regression coefficients of the two data sets to estimate the
skeleton of the difference graph and then orients some of the edges using
invariance tests between regression residual variances. We demonstrate the
properties of our method through a simulation study and apply it to the
analysis of gene expression data from ovarian cancer and during T-cell
activation
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to
rapidly repurpose existing approved drugs for clinical interventions. While a
number of data-driven and experimental approaches have been suggested in the
context of drug repurposing, a platform that systematically integrates
available transcriptomic, proteomic and structural data is missing. More
importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is
critical to integrate aging signatures into drug discovery platforms. We here
take advantage of large-scale transcriptional drug screens combined with
RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the
aging lung. To identify robust druggable protein targets, we propose a
principled causal framework that makes use of multiple data modalities. Our
analysis highlights the importance of serine/threonine and tyrosine kinases as
potential targets that intersect the SARS-CoV-2 and aging pathways. By
integrating transcriptomic, proteomic and structural data that is available for
many diseases, our drug discovery platform is broadly applicable. Rigorous in
vitro experiments as well as clinical trials are needed to validate the
identified candidate drugs
Multimodal LLMs for health grounded in individual-specific data
Foundation large language models (LLMs) have shown an impressive ability to
solve tasks across a wide range of fields including health. To effectively
solve personalized health tasks, LLMs need the ability to ingest a diversity of
data modalities that are relevant to an individual's health status. In this
paper, we take a step towards creating multimodal LLMs for health that are
grounded in individual-specific data by developing a framework (HeLM: Health
Large Language Model for Multimodal Understanding) that enables LLMs to use
high-dimensional clinical modalities to estimate underlying disease risk. HeLM
encodes complex data modalities by learning an encoder that maps them into the
LLM's token embedding space and for simple modalities like tabular data by
serializing the data into text. Using data from the UK Biobank, we show that
HeLM can effectively use demographic and clinical features in addition to
high-dimensional time-series data to estimate disease risk. For example, HeLM
achieves an AUROC of 0.75 for asthma prediction when combining tabular and
spirogram data modalities compared with 0.49 when only using tabular data.
Overall, we find that HeLM outperforms or performs at parity with classical
machine learning approaches across a selection of eight binary traits.
Furthermore, we investigate the downstream uses of this model such as its
generalizability to out-of-distribution traits and its ability to power
conversations around individual health and wellness
Computational methods for analyzing and modeling gene regulation and 3D genome organization
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2021Cataloged from the official PDF of thesis.Includes bibliographical references (pages 261-281).Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis.Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data.DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.by Anastasiya Belyaeva.Ph. D.Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Progra
DCI: learning causal differences between gene regulatory networks
Abstract
Summary
Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.
Availability and implementation
Python package freely available at http://uhlerlab.github.io/causaldag/dci.
Supplementary information
Supplementary data are available at Bioinformatics online.
</jats:sec
Pedagogical Value of Folklore Prosaic Genres in Intellectual and Moral Education of Future Teachers at the Foreign Language Lessons
The article is devoted to the issue of pedagogical value of folklore in intellectual and moral education of future teachers at the foreign language lessons. The authors of the article analyze why is folklore a valuable source in training teachers. In the article it was researched folklore genres which can be successfully used with educational purpose. It was also researched the works of well ā known foreign and Russian scientists who made a big contribution to the theoretical and practical research of folklore. The authors of the article researched the works of pedagogues who used Russian folklore genres in kindergartens, elementary and secondary schools. The aim of the study is to demonstrate the pedagogical value of folklore prosaic genres in intellectual and moral education of future teachers at the foreign language lessons.
Before analyzing the pedagogical value of folklore genres in intellectual and moral education of future teachers the authors held an opinion poll at the university. It was surveyed 200 students of the first and the second courses at the institute of psychology and education. There were some questions about folklore of the native language: if they knew some cradle songs or if they remembered tongue twisters which their parents had used to improve their pronunciation skills or if they remembered some fairy tales from their childhood, if they remembered counting out rhymes. 47 % of the future primary teachers couldnāt remember cradle songs. 33 % students responded that they knew some cradle songs but they couldnāt sing the whole songs. 20% of students didnāt hear cradle songs from their parents. Students who took part in questionnaire and in polling showed better results in retelling fairy tales of the native languages (Russian and Tatar). 88% of future infant school teachers could remember the content of fairy tales. They told that they heard them from parents and read them at primary schools. Just 5 % students could remember local legends and fables. 60 students (2 groups) of primary school teachers and English language teachers were taken to the experimental groups where the authors of the article evaluated the results of the experiment using folklore genres of the native and second languages in teaching foreign language. At the end of the experimental year students replied to some questionnaires. 100 % of students liked to learn foreign language by means of folklore as native so foreign one. They said that they would use them in their future work. 99% students underlined educational value of folklore in bringing up children, in intellectual and moral education of younger generation. 100 % students who learned foreign language by means of folklore genres highlighted that their acquired knowledge, competence, and skills would be very useful in their future work with children at primary schools
Pedagogical Value of Folklore Prosaic Genres in Intellectual and Moral Education of Future Teachers at the Foreign Language Lessons
The article is devoted to the issue of pedagogical value of folklore in intellectual and moral education of future teachers at the foreign language lessons. The authors of the article analyze why is folklore a valuable source in training teachers. In the article it was researched folklore genres which can be successfully used with educational purpose. It was also researched the works of well ā known foreign and Russian scientists who made a big contribution to the theoretical and practical research of folklore. The authors of the article researched the works of pedagogues who used Russian folklore genres in kindergartens, elementary and secondary schools. The aim of the study is to demonstrate the pedagogical value of folklore prosaic genres in intellectual and moral education of future teachers at the foreign language lessons.
Before analyzing the pedagogical value of folklore genres in intellectual and moral education of future teachers the authors held an opinion poll at the university. It was surveyed 200 students of the first and the second courses at the institute of psychology and education. There were some questions about folklore of the native language: if they knew some cradle songs or if they remembered tongue twisters which their parents had used to improve their pronunciation skills or if they remembered some fairy tales from their childhood, if they remembered counting out rhymes. 47 % of the future primary teachers couldnāt remember cradle songs. 33 % students responded that they knew some cradle songs but they couldnāt sing the whole songs. 20% of students didnāt hear cradle songs from their parents. Students who took part in questionnaire and in polling showed better results in retelling fairy tales of the native languages (Russian and Tatar). 88% of future infant school teachers could remember the content of fairy tales. They told that they heard them from parents and read them at primary schools. Just 5 % students could remember local legends and fables. 60 students (2 groups) of primary school teachers and English language teachers were taken to the experimental groups where the authors of the article evaluated the results of the experiment using folklore genres of the native and second languages in teaching foreign language. At the end of the experimental year students replied to some questionnaires. 100 % of students liked to learn foreign language by means of folklore as native so foreign one. They said that they would use them in their future work. 99% students underlined educational value of folklore in bringing up children, in intellectual and moral education of younger generation. 100 % students who learned foreign language by means of folklore genres highlighted that their acquired knowledge, competence, and skills would be very useful in their future work with children at primary schools
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry
| openaire: EC/H2020/748354/EU//NonnegativeRankThe spatial organization of the genome in the cell nucleus plays an important role for gene regulation, replication of the deoxyribonucleic acid (DNA), and genomic integrity. Through the development of chromosome conformation capture experiments (such as 3C, 4C, and Hi-C) it is now possible to obtain the contact frequencies of the DNA at the whole-genome level. In this paper, we study the problem of reconstructing the three-dimensional (3D) organization of the genome from such whole-genome contact frequencies. A standard approach is to transform the contact frequencies into noisy distance measurements and then apply semidefinite programming formulations to obtain the 3D configuration. However, neglected in such reconstructions is the fact that most eukaryotes including humans are diploid and therefore contain two copies of each genomic locus. We prove that the 3D organization of the DNA is not identifiable from the distance measurements derived from contact frequencies in diploid organisms. In fact, there areinfinitely many solutions even in the noise-free setting. We then discuss various additional biologically relevant and experimentally measurable constraints (including distances between neighboring genomic loci and higher-order interactions) and prove identifiability under these conditions. Furthermore, we provide semidefinite programming formulations for computing the 3D embedding of the DNA with these additional constraints and show that we can recover the true 3D embedding with high accuracy from both noiseless and noisy measurements. Finally, we apply our algorithm to real pairwise and higher-order contact frequency data and show that we can recover known genome organization patterns.Peer reviewe