Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

A lattice-based approach for chemical structural retrieval

Searching for chemica lstructures with similarstructural and functional information of organic chemicals is an important part of the drug discovery process. However, the current chemical structural retrieval methods have focused mainly on finding chemicals with similar structures to the input chemical structural query, and tend to ignore the functional features which are important for determining the chemical property and activity of the chemicals. In this paper, we propose a lattice-based approach for chemical structural retrieval. The proposed lattice-based approach is based on Formal Concept Analysis. It retrieves chemical structures that have functional groups and interactions between functional groups similar to the chemical structural query. The performance of the proposed lattice-based approach is evaluated and its promising performance results have shown that the proposed approach is effective for chemical structural retrieval

Does there exist relationship between personality and handwriting of Chinese characters? A view from image mining

This paper presents a study on the relationship between personality and handwriting of Chinese characters through image mining technologies. A questionnaire of personality test is used to quantify the 5 global personality factors of participants. The handwriting samples of participants are acquired and scanned into computer images. 23 handwriting features can be extracted from these sample images through image processing methods. Considering the imbalanced distribution of the sample data, a cost-sensitive neural network with modified training algorithms and correlation analysis are employed to examine the association between the handwriting features and global personality factors. The results hint that there indeed exist some weak linear and strong non-linear relationships between most of personality factors and specific handwriting features. These relationships provide the possibility for computerized analyzing people's personality by their Chinese handwriting

Neural networks for web content filtering

With the proliferation of harmful Internet content such as pornography, violence, and hate messages, effective content-filtering systems are essential. Many Web-filtering systems are commercially available, and potential users can download trial versions from the Internet. However, the techniques these systems use are insufficiently accurate and do not adapt well to the ever-changing Web. To solve this problem, we propose using artificial neural networks to classify Web pages during content filtering. We focus on blocking pornography because it is among the most prolific and harmful Web content. However, our general framework is adaptable for filtering other objectionable Web material