VictorJuiz/thesis huggingface dataset:
Data-driven transcriptomic modules

High throughput measurement of gene expression levels have dramatically increased the amount of biological information we collect from experiments, and presents a tantalizing possibility of gaining novel insights into human biology at the molecular level. Realization of this possibility is dependent on our ability to interpret the tens of thousands of numerical values, and to draw inferences from the patterns. Analysis of these data typically begins with the identification of genes that are differentially expressed between two or more groups. Unfortunately, this either entails testing all the genes (and hence suffer from low statistical power), or using pre-defined subsets that introduce researcher bias. In recent years, it is more common for researchers to perform Gene Set Enrichment Analysis (GSEA), which restores statistical power by testing at the gene set level. The choice of which gene sets and how many of them to test is, however, a source of researcher bias. Furthermore, the reliance on pre-defined gene sets means that we are, once again, limited to our current understanding of human biology. Other problems that have plagued transcriptomic analysis include inter-platform incompatibility and batch effects. This dissertation seeks to provide an alternative approach for transcriptomic research. Specifically, I describe a data-driven methodology for obtaining transcriptomic modules from a large compendium of microarray experiments, and demonstrate how these modules can be used to perform classification of samples and also to extract biologically-meaningful properties from gene expression datasets. I also describe an imputation method to allow cross-platform analysis between two of the most common microarray platforms in the Gene Expression Omnibus, and use this to transform the drug-gene database, Connectivity Map (CMap), into a compatible form for the transcriptomic module analysis. Finally, I demonstrate how drug repurposing could be achieved using CMap in the framework of these transcriptomic modules.

Keywords: Bioengineering.
Advisor: Altman Russ 
Hastie Trevor 
Khatri Purvesh
Data-Space Inversion with Variable Well Controls in the Prediction Period

Data-space inversion (DSI) and related procedures represent a family of methods that provide posterior (history-matched) predictions for quantities of interest, along with uncertainty quantification, without constructing posterior models. Rather, posterior predictions are generated directly from a large set of prior-model simulations and observed data. Current DSI methods require that well controls (e.g., bottom-hole pressures) be specified for the full duration of the simulation time frame (historical period plus prediction period). This means that, if well settings are changed, the full set of prior models must be re-simulated using the new controls, which would be very time consuming for optimization computations.
In this work we develop a data-space inversion with variable controls (DSIVC) procedure. DSIVC enables the generation of posterior forecasts, under user-specified well controls in the post-history-match period, without re-running any prior simulations. In DSIVC, we first perform flow simulations on all prior realizations with randomly generated well controls (with a prescribed number of stages). User-specified controls are then treated as additional `observations' to be matched in posterior predictions. Posterior data samples are generated using a randomized maximum likelihood procedure in data space. Data-space principal component analysis and histogram transformation are applied to improve DSIVC performance and efficiency.
Extensive results are presented for a bimodal channelized system. Different (synthetic) true models, and different sets of post-history-match controls, are considered. For any well control specification, a set of 100 posterior predictions can be generated in seconds or minutes. Posterior predictions from our DSIVC procedure are compared to those from an existing DSI method for a number of cases. The DSI results require all prior models to be re-simulated using the specified controls, while DSIVC requires only one set of prior simulation runs. Substantial uncertainty reduction is typically achieved through data-space inversion, and general agreement between DSIVC and DSI results is consistently observed. The DSIVC method is then applied for well control optimization under uncertainty. A mesh adaptive direct search algorithm is used to maximize expected net present value (NPV) over 100 posterior predictions. Clear improvement in expected NPV is achieved using DSIVC. The method may thus be suitable for use in closed-loop reservoir management, with both data assimilation and production optimization performed in data space.

Keywords: School of Earth Energy &amp; Environmental Sciences
Data-space inversion
Variable controls
Optimization
Thesis
Advisor: Durlofsky Louis J.
Deciphering the regulation of A-to-I RNA editing in the Drosophila brain [electronic resource]

RNA editing increases genetic diversity by altering RNA sequences from those encoded by the genome. The most prevalent type of RNA editing is adenosine-to-inosine (A-to-I) RNA editing, where the Adenosine Deaminase Acting on RNA (ADAR) protein family converts adenosines into inosines, which are then recognized as guanosines by the cellular machinery. ADAR enzymes are conserved in numerous species including Drosophila, where dADAR edits over 5,000 sites at widely varying frequency, creating different ratios of modified transcripts. These editing events are necessary for proper neuronal function of the fly; however, the regulation of editing levels at particular editing sites is poorly understood. This thesis addresses the two primary modes of regulation of RNA editing in Drosophila, cis and trans regulation. Chapter 1 provides an introduction to ADAR editing, the regulatory role of both the cis determinants and the trans factors that influence editing levels, and the function of RNA editing in the brain. In Chapter 2, I evaluate RNA editing level differences between different neuronal cell types in the fly brain, uncovering the extent to which RNA editing is differentially regulated in different cell types. Next, in Chapter 3, I analyze how both cis and trans regulatory factors contribute to RNA editing differences between two closely related fly species, D. melanogaster and D. sechellia, highlighting the critical role of cis sequence changes in altering RNA structural stability around editing sites. Finally, in Chapter 4, I discuss the novel and evolutionarily conserved role of the RNA binding protein Zinc-finger protein at 72D (Zn72D) as an ADAR-interacting protein and regulator of RNA editing. Together, these chapters provide insight into the variability of editing levels within the brain and the cis and trans regulatory factors that influence these editing events, improving our understanding of this critical co-transcriptional process.

Keywords: Genetics.
Advisor: Li Jin (Billy) primary advisor. Thesis advisor
Clandinin Thomas R. (Thomas Robert)
Fire Andrew Zachary advisor. Thesis advisor
Gitler Aaron D. advisor. Thesis advisor
Deep 3D representation learning

Among all digital representations we have for real physical objects, 3D is arguably the most expressive encoding. 3D representations allow storage and manipulation of high-level information (e.g. semantics, affordances, function) as well as low-level features (e.g. appearance, materials) about the object. How much we can understand and transform the 3D world is thus largely determined by the performance of algorithms that analyze and create 3D data. While 3D visual computing has predominantly focused on single 3D models or small model collections, the amount of accessible 3D models has increased by several orders of magnitude during the past few years. This significant growth pushes us to redefine 3D visual computing from the perspective of big 3D data. In the thesis, a series of topics on data-driven 3D visual computing will be discussed. These include: constructing an information-rich large-scale 3D model repository, generating synthetic data for supervising neural networks, and learning end-to-end neural networks for analysis and synthesis of 3D geometries. Under the guiding principle of using large-scale 3D data for representation learning, the efforts described in this thesis have led to top-performing algorithms for pure-3D data processing, as well as 3D-assisted semantic, geometric and physical property inference from 2D images. The thesis will conclude by describing several promising directions for future research.

Advisor: Guibas Leonidas J. 
Ermon Stefano 
Savarese Silvio 
Wetzstein Gordon
Deep episodic value iteration [electronic resource] : a theory of sample efficient learning for machine learning and cognitive (neuro) science

This thesis aims to show that, with some extensions, deep learning is computationally capable of addressing rapid adaptation, can account for behavioral phenomena surrounding this ability, and is parsimonious with existing theories regarding the functional role of the relevant neuroanatomy. The main contribution of this thesis is an algorithm that can account for planning from limited data, which we call Deep Episodic Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity metric, which is applied recursively for planning over episodic memories. By training this network end-to-end based on planning performance, we posit that DEVI should be capable of meta reinforcement learning, even in high dimensional state spaces. We evaluate DEVI in comparison with traditional deep learning techniques as well as other approaches to meta learning. We close by showing how DEVI can be seen as a computational account of schema formation and schema-consistent learning.

Keywords: Psychology.
Advisor: McClelland James L. primary advisor. Thesis advisor
Goodman Noah (Noah D.) advisor. Thesis advisor
Poldrack Russell A.
Deep learning in-vivo transcription factor binding

Transcription factors (TFs) affect gene expression by interpreting regulatory DNA within a genome. Models of DNA sequence and shape can explain in-vitro TF-DNA interactions outside a cellular context. But in-vivo TF-DNA interactions in cells are influenced by additional factors, such as interactions between TFs, and interactions between TFs and nucleosomes. Here, we present the application of deep learning, a class of modern machine learning techniques, to the task of modeling in-vivo transcription factor binding at a genome-wide scale. Deep learning has powered significant breakthroughs in complex pattern recognition tasks across several data-rich domain and successful applications have primarily focused on image, speech, and text data modalities. In this thesis, we present three new contribution to the field of deep learning applications to genomics: (1) Adapation of deep learning methods to regulatory DNA sequence data using simulations, (2) development of deep learning models of in-vivo TF binding at a genome-wide scale, and (3) interrogations of these models to reveal determinants of in-vivo TF binding sites. Our results demonstrate that deep learning can be used to build accurate, interpretable models of in-vivo TF binding. That there are guiding principles to systematic development and interpretation of such models. Finally, we discuss limitations of our methods and point to directions for future work.

Keywords: Biophysics.
Advisor: Kundaje Anshul
Altman Russ 
Fordyce Polly 
Greenleaf William James
Deep learning methods for modeling interrelations of dynamic and static objects

We are witnessing an increasing growth of autonomous platforms such as self-driving cars and social robots around us. These smart machines will be navigating and interacting with us in various scenarios. However, understanding the behavior and relations between the humans and objects in different scenes is very important for these smart agents navigating in this environments. Modeling the interrelations among objects and humans enables the computer vision systems to better understand complex scenes and it comes with many challenges: 1) How to model the complex scene context? 2) How to model the interrelations among objects and humans? 3) How to capture and model the unwritten rules of human social behavior? The goal of this thesis is to address some of these challenges. Modeling the inter-relations of dynamic and static objects or agents within an environment applies to a wide range of domains from autonomous driving vehicles and social robot navigation, to abnormal behavior detection in surveillance. For example, for an autonomous vehicle to navigate in a space, it has to understand the surrounding environment and model the inter-relations of dynamic and static objects, i.e. the relationship between the agent and the static objects, the moving objects and their joint interactions. To this end, we need to track all moving objects (known as multi-object tracking problem), predict their future positions and interactions with the environment (known as path prediction problem), and find the traversability of a path given the state of static and moving objects (knows as traversability estimation). To tackle these problems we proposed several new deep learning methods as follows: 1- In the first chapter of this thesis, we tackle the multi-object tracking problem, we proposed an online method that encodes long-term temporal dependencies across multiple cues using deep learning. Our framework uses a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. 2- The second part of this thesis (Chapters 2 and 3) tackles the problem of path prediction, we proposed two interpretable frameworks CAR-Net and Sophie based on Attentive Generative Adversarial Network (GAN), that can leverage two sources of information: the past trajectory of all the agents in the scene and a wide top-view image of the navigation scene. Our predicted trajectories are obeying physical constraints of the environment while anticipating the movements and social behavior of other people. 3- Finally, in the last part of the this thesis, chapters 3 and 4 tackle the traversability estimation problem, we proposed GONet and GONet++ which uses several deep learning networks trained on a large amount of data from a single RGB camera to identify the traversable and non-traversable spaces in an environment around an agent. All proposed approaches were evaluated on relevant benchmarks and outperform all previous state-of-the-art methods.

Keywords: Electrical Engineering.
Advisor: Savarese Silvio 
Sadigh Dorsa 
Wetzstein Gordon
Deep learning on point clouds for 3D scene understanding

Point cloud is a commonly used geometric data type with many applications in computer vision, computer graphics and robotics. The availability of inexpensive 3D sensors has made point cloud data widely available and the current interest in self-driving vehicles has highlighted the importance of reliable and efficient point cloud processing. Due to its irregular format, however, current convolutional deep learning methods cannot be directly used with point clouds. Most researchers transform such data to regular 3D voxel grids or collections of images, which renders data unnecessarily voluminous and causes quantization and other issues. In this thesis, we present novel types of neural networks (PointNet and PointNet++) that directly consume point clouds, in ways that respect the permutation invariance of points in the input. Our network provides a unified architecture for applications ranging from object classification and part segmentation to semantic scene parsing, while being efficient and robust against various input perturbations and data corruption. We provide a theoretical analysis of our approach, showing that our network can approximate any set function that is continuous, and explain its robustness. In PointNet++, we further exploit local contexts in point clouds, investigate the challenge of non-uniform sampling density in common 3D scans, and design new layers that learn to adapt to varying sampling densities. The proposed architectures have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointNets to two important perception problems in robotics: 3D object detection and 3D scene flow estimation. In 3D object detection, we propose a new frustum-based detection framework that achieves 3D instance segmentation and 3D amodal box estimation in point clouds. Our model, called Frustum PointNets, benefits from accurate geometry provided by 3D points and is able to canonicalize the learning problem by applying both non-parametric and data-driven geometric transformations on the inputs. Evaluated on large-scale indoor and outdoor datasets, our real-time detector significantly advances state of the art. In scene flow estimation, we propose a new deep network called FlowNet3D that learns to recover 3D motion flow from two frames of point clouds. Compared with previous work that focuses on 2D representations and optimizes for optical flow, our model directly optimizes 3D scene flow and shows great advantages in evaluations on real LiDAR scans. As point clouds are prevalent, our architectures are not restricted to the above two applications or even 3D scene understanding. This thesis concludes with a discussion on other potential application domains and directions for future research.

Keywords: Electrical Engineering.
Advisor: Guibas Leonidas J. 
Girod Bernd 
Savarese Silvio
Defining a Role for RSK in Normal and Aberrant Myelopoiesis

Recent studies have established that the Ribosomal S6 Kinases (RSKs) drive cell growth, survival, and proliferation in solid cancers including breast, lung, and prostate. However, the role of RSK in normal and malignant blood cell formation, or hematopoiesis, remains undetermined. RSK phosphorylation targets such as the transcription factor cyclic AMP response element binding protein (CREB) are known to be regulators of hematopoietic differentiation as well as potential therapeutic targets for acute myeloid leukemia (AML). Here, we describe the function of RSKs in hematopoietic differentiation and growth. RSK1 and RSK2 were identified as the predominant isoforms in hematopoietic cells, particularly in the myeloid lineage. RSK inhibition using the small molecule inhibitor BI-D1870 selectively reduced AML cell survival and development as compared to normal hematopoietic cells. When utilized to investigate the isoform-specific function of RSKs in hematopoiesis, small hairpin RNA (shRNA) knockdown of either RSK1 or RSK2 in AML cells failed to induce a growth defect. Additionally, knockdown of RSK1 and RSK2 together slightly weakened CREB activation but also did not impair cell growth, indicating that residual expression and redundant function of RSKs in knockdown cells allowed for relatively normal growth of RSK knockdown AML cells. A large reduction in AML cell growth was only observed when RSK1 or RSK2 were completely knocked out using CRISPR. Taken together, our data demonstrate that RSK is crucial for the tumorigenesis and proliferation of AML cells as well as normal myeloid development (myelopoiesis). Increased susceptibility of AML cells to RSK inhibition signify RSK’s potential as a future therapeutic target for AML.

Keywords:  Department of Biology
Biology
RSK
CREB
myelopoiesis
hematopoiesis
acute myeloid leukemia
Thesis
Advisor: Sakamoto Kathleen
Fernald Russell
Defining the trophic role of a ubiquitous kelp forest echinoderm, the bat star Patiria miniata

Trophic interactions play crucial roles in structuring complex ecosystems. The bat star (Patiria miniata) is an abundant kelp forest echinoderm whose trophic ecology remains poorly understood. Though it has traditionally been considered an omnivorous generalist, most work on Patiria has focused on specific effects of predation on select groups of flora and fauna, with little consideration of its overall role in the kelp forest ecosystem. Understanding Patiria’s trophic role is especially important given the recent declines in many asteroid species due to sea star wasting disease. Patiria was left mostly untouched by the outbreak and could potentially fill the niches left by other ecologically-important sea star species. We used a combination of feeding experiments and comparative stable isotope analysis to examine various aspects of Patiria’s trophic ecology. Contrary to Patiria’s omnivorous reputation, initial feeding experiments revealed a predominantly carnivorous diet with little evidence of herbivory. Perplexed by these results, we conducted additional experiments which suggested that Patiria may feed on the epiphytic communities growing on algae, but not on the algae themselves. Finally, isotopic comparisons of bat stars with giant sea stars (Pisaster giganteus), purple sea urchins (Strongylocentrotus purpuratus), and warty sea cucumbers (Parastichopus parvimensis) revealed that bat stars occupy a high trophic level, have relatively low trophic diversity, and are isotopically most similar to giant sea stars (a carnivorous species). Altogether, these results suggest that Patiria has a narrower niche than previously expected and a trophic role similar to that of carnivorous sea stars like Pisaster.

Keywords:  Department of Biology
Stanford Department of Biology
Marine Ecology
Trophic Ecology
Food Web
Patiria miniata
Kelp Forest
Sea Star
Wasting Disease
Stable Isotopes
Niche Overlap
Thesis
Advisor: Micheli Fiorenza
Dunbar Robert
Design for collective action

From Twitter hashtags such as #metoo to protests by Amazon Mechanical Turk workers in the public sphere, collectives come together online to make progress on shared issues. Research has long celebrated the web's affordances for galvanizing such coordinated action. By lowering the costs of communication, the web promises to enable distributed collectives to gather and act around shared issues. However, in practice even highly motivated collectives often fail to envision shared outcomes and act on them collectively. This dissertation introduces social computing systems that directly center trust and familiarity to more effectively support collective action. I will demonstrate this concept through three projects. The first, Hive, explores how social systems can help build strong networks by organizing a collective into small teams, then intermixing viewpoints by gradually rotating team membership. I deployed this project with Mozilla to reimagine accessible web browsing with disability advocates online. The second project, Huddler, demonstrates an algorithmic solution to the problem of teams benefiting from highly familiar members but experiencing unpredictable availability. Finally, the third project, Dynamo, shows how structured human labor can help move efforts forward when they stall. I undertook this project in collaboration with worker rights advocates on Amazon Mechanical Turk. Through the body of work in this dissertation I envision alternate roles for social computing ecosystems in society that directly support efforts for social change.

Advisor: Bernstein Michael S.
Landay James A.