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The 3D Genome Structure of Single Cells

Biomedical Data Science 2021年第1期4卷 21-41页

作者： Tianming Zhou Ruochi Zhang Jian Ma Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: jianma@cs.cmu.edu

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts.

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Special issue on Logical Frameworks and Metalanguages http//***/certilab/LFM00/***

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Journal of Functional Programming 2000年第1期10卷 135-136页

作者： Joëlle Despeyroux Robert Harper INRIA Email: Joelle.Despeyroux@inria.fr CMU Email: Robert.Harper@cs.cmu.edu

Logical frameworks and meta-languages are intended as a common substrate for representing and implementing a wide variety of logics and formal systems. Their definition and implementation have been the focus of considerable work over the last decade. At the heart of this work is a quest for generality: A logical framework provides a basis for capturing uniformities across deductive systems and support for implementing particular systems. Similarly a meta-language supports reasoning about and using *** frameworks have been based on a variety of different languages including higher-order logics, type theories with dependent types, linear logic, and modal logic. Techniques of representation of logics include higher-order abstract syntax, inductive definitions or some form of equational or rewriting logic in which substitution is explicitly *** of systems that implement logical frameworks include Alf, Coq, NuPrl, HOL, Isabelle, Maude, lambda-Prolog and Twelf. An active area of research in such systems is the study of automated reasoning techniques. Current work includes the development of various automated procedures as well as the investigation of rewriting tools that use reflection or make use of links with systems that already have sophisticated rewriting systems. Program extraction and optimization are additional topics of ongoing work.

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Sketching and Sublinear Data Structures in Genomics

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Biomedical Data Science 1000年第1期2卷 93-118页

作者： Guillaume Marçais Brad Solomon Rob Patro Carl Kingsford 1Computational Biology Department Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: gmarcais@cs.cmu.edu carlk@cs.cmu.edu 2Department of Computer Science Whiting School of Engineering Johns Hopkins University Baltimore Maryland 21218 USA email: brad.sol@*** 3Department of Computer Science Stony Brook University Stony Brook New York 11794 USA email: rob.patro@cs.stonybrook.edu

Large-scale genomics demands computational methods that scale sublinearly with the growth of data. We review several data structures and sketching techniques that have been used in genomic analysis methods. Specifically, we focus on four key ideas that take different approaches to achieve sublinear space usage and processing time: compressed full-text indices, approximate membership query data structures, locality-sensitive hashing, and minimizers schemes. We describe these techniques at a high level and give several representative applications of each.

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Toward Robotic Manipulation

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Control, Robotics, and Autonomous Systems 1000年第1期1卷 1-28页

作者： Matthew T. Mason Robotics Institute and Computer Science Department Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: matt.mason@cs.cmu.edu

This article surveys manipulation, including both biological and robotic manipulation. Biology inspires robotics and demonstrates aspects of manipulation that are far in the future of robotics. Robotics develops concepts and principles that become evident only in the creative process. Robotics also provides a test of our understanding. As Richard Feynman put it: “What I cannot create, I do not understand.”

关键词： robot manipulation evolution engineering

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Machine Learning for Sustainable Energy Systems

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Environment and Resources 1000年第1期46卷 719-747页

作者： Priya L. Donti J. Zico Kolter 2Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA 1Computer Science Department Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: pdonti@cs.cmu.edu 3Bosch Center for Artificial Intelligence Pittsburgh Pennsylvania 15222 USA

In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment.

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