In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.
In order to solve the problem of indoor target movement autonomous positioning error accumulation, this paper proposes a method of the RSST assisted positioning error compensation. In the first place, the identification card position is determined using optimizing the distance and orientation between the main station and the auxiliary station. Secondly, the Auxiliary correction of time positioning error is proceeding by RSSI measured value. Finally, the singular data are corrected and least squares processed through multiple measurements to reduce the influence of measurement data fluctuation on positioning. Practical application has been carried out in the corridor environment consisting of 1 main station,2 auxiliary stations and 20 terminal nodes and the direct-vision distance is 200m. The results show that the method reduces the errors of environment, scattering and system. Compared with the positioning error of point-to-point ranging in the corridor environment, the error decreases from 12m to 3m by 75%. Compared with the corridor environment 1, the location error of main station and auxiliary station is reduced from 15m to 3m by 80%.Application environment: as shown in figure 1, it is an unobstructed network environment with direct vision distance of 200m, consisting of 1 main station, 2 auxiliary stations and 20 identification CARDS.Test method: mobile positioning test was carried out on 20 identification CARDS in the application environment, and the recorded time value was tested every one meter for 20 times. The measured distance was calculated according to the TOF ranging method, and the singular value was corrected with the RSSI strength value. Three groups of distance data and three groups of time-stamps were obtained, and error compensation was conducted according to the method in 2.2. Test ResultCompared with the point-to-point network communication in the corridor environment, the communication overhead is dispersed from 2 to 4 nodes. For each node, the communication overhead is reduced by 50%, and the ranging and positioning error is reduced by 75% from 12m to 3m.Compared with the corridor environment 1 for the main station and the auxiliary station, the communication overhead is dispersed from 3 to 4 nodes. For each node, the communication overhead is reduced by 25%, and the ranging and positioning error is reduced by 80% from 15m to 3m. Interpretation of ResultThe positioning method and application test of the system are carried out under the environment of slow movement of identification card nodes. By analyzing the causes of positioning error and the compensation method, the communication overhead and location error of the system are reduced.Since the auxiliary station is added, the measurement error caused by the node movement is compensated by the time difference of the measurement of the two auxiliary stations, which provides the basis for accurate positioning.The influence of singular data on location error is eliminated by RSSI compensation and least square regression analysis. ConclusionT...
Patient-derived xenograft (PDX) models of human tumors are an important and widely used platform for cancer research. Cancer drug development relies on PDX models to screen drugs and characterize tumor biology for potential drug targets. It has been well established that PDX models maintain similar biology as their original tumors, including histological patterning, gene expression, single-nucleotide variants, and copy number alterations. Using short-read sequencing technology to profile and characterize genomic alterations within PDX tumor models is becoming a common practice in cancer research. Mouse read contamination is a relevant source of noise in PDX tumor sequencing data and needs to be addressed prior to downstream analyses. Therefore, a key consideration for downstream analysis of PDX sequencing data, such as determining variant calls or gene expression values, is effectively removing contaminating mouse sequence. Removing contamination from PDX sequencing data is necessary for accurate and reproducible downstream analyses. A limited number of studies establishing best practices for handling PDX sequencing data exist. Thus, we set out to compare different strategies for removing mouse contamination from PDX tumor sequencing data for DNA and RNA using a set of controlled experimental in silico datasets and data from PDX tumors. We designed a set of in silico experiments using these sequencing data to assess a range of approaches for removing contaminating mouse reads from human data. Our experiments used a set of publically available human and mouse DNA and RNA sequencing data available at the SRA site. Subsets of the raw human and mouse reads were mixed at different ratios and analyzed with five different approaches: 1) raw alignment to the human reference genome, 2) filtering with the Xenome algorithm followed by alignment to the human genome, 3) alignment to the human reference genome followed by filtering with the XenofilteR algorithm, 4) mouse-human hybrid reference genome alignment, and 5) our novel NextCODE approach. We assessed the sensitivity and specificity of each procedure for removing mouse sequence and maintaining human sequence for downstream analyses. We also assessed the effects of each filtering procedure on gene expression quantification and variant calling. Our results introduce a novel, improved method for removing mouse DNA, facilitating better-quality data for downstream analysis. Citation Format: Ryan P. Abo, Zehua Chen, Shannon Bailey, Hao Wang, Sharvari Gujja, Pengwei Yang, Jim Lund, Jeff Gulcher, Tom Chittenden. Comprehensive assessment of mouse contamination removal strategies from patient-derived xenograft model sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3586. doi:10.1158/1538-7445.AM2017-3586
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