Pushing ML Predictions Into DBMSs.

In the past decade, many approaches have been suggested to execute ML workloads on a DBMS. However, most of them have looked at in-DBMS ML from a training perspective, whereas ML inference has been largely overlooked. We think that this is an important gap to fill for two main reasons: (1) in the near future, every application will be infused with some sort of ML capability; (2) behind every web page, application, and enterprise there is a DBMS, whereby in-DBMS inference is an appealing solution both for efficiency (e.

Single-cell transcriptomics identifies potential cells of origin of MYC rhabdoid tumors.

Rhabdoid tumors (RT) are rare and highly aggressive pediatric neoplasms. Their epigenetically-driven intertumoral heterogeneity is well described; however, the cellular origin of RT remains an enigma. Here, we establish and characterize different genetically engineered mouse models driven under the control of distinct promoters and being active in early progenitor cell types with diverse embryonic onsets.

InterCellar enables interactive analysis and exploration of cell-cell communication in single-cell transcriptomic data.

Deciphering cell-cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand-receptor interactions. Despite the existence of various tools capable of inferring cell-cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize.

Benchmarking atlas-level data integration in single-cell genomics.

Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches).

An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma.

Background: Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises 4 subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts.

A Hybrid In Silico/In Vitro Target Fishing Study to Mine Novel Targets of Urolithin A and B: A Step Towards a Better Comprehension of Their Estrogenicity.

Scope: Urolithin A and B are gut metabolites of ellagic acid and ellagitannins associated with many beneficial effects. Evidence in vitro pointed to their potential as estrogenic modulators. However, both molecular mechanisms and biological targets involved in such activity are still poorly characterized, preventing a comprehensive understanding of their bioactivity in living organisms.

Macrophage-tumor cell interaction promotes ATRT progression and chemoresistance.

Atypical teratoid/rhabdoid tumors (ATRT) are known for their heterogeneity concerning pathophysiology and outcome. However, predictive factors within distinct subgroups still need to be uncovered. Using multiplex immunofluorescent staining and single-cell RNA sequencing we unraveled distinct compositions of the immunological tumor microenvironment (TME) across ATRT subgroups.

Adding data provenance support to Apache Spark.

Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g.

Automated Debugging in Data-Intensive Scalable Computing.

Developing Big Data Analytics workloads often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (., program crash, outlier results, etc.

Big Data Analytics with Datalog Queries on Spark.

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.

Optimizing Interactive Development of Data-Intensive Applications.

Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch jobs that execute programs (e.g., queries) compiled from a high-level language.

Programming and Runtime Support to FPGA Accelerator Deployment at Datacenter Scale.

With the end of CPU core scaling due to dark silicon limitations, customized accelerators on FPGAs have gained increased attention in modern datacenters due to their lower power, high performance and energy efficiency. Evidenced by Microsoft's FPGA deployment in its Bing search engine and Intel's 16.7 billion acquisition of Altera, integrating FPGAs into datacenters is considered one of the most promising approaches to sustain future datacenter growth.

BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark.

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform.

Titian: Data Provenance Support in Spark.

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence ( from log files) and performing trial and error debugging. To aid this effort, we built , a library that enables -tracking data through transformations-in Apache Spark.