Extracting health-related causality from twitter messages using natural language processing.

BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. METHODS: Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: "stress", "insomnia", and "headache." A large dataset consisting of 24 million tweets are used. RESULTS: The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. CONCLUSIONS: Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.

Common Oncogene Mutations and Novel SND1-BRAF Transcript Fusion in Lung Adenocarcinoma from Never Smokers.

Lung adenocarcinomas from never smokers account for approximately 15 to 20% of all lung cancers and these tumors often carry genetic alterations that are responsive to targeted therapy. Here we examined mutation status in 10 oncogenes among 89 lung adenocarcinomas from never smokers. We also screened for oncogene fusion transcripts in 20 of the 89 tumors by RNA-Seq. In total, 62 tumors had mutations in at least one of the 10 oncogenes, including EGFR (49 cases, 55%), K-ras (5 cases, 6%), BRAF (4 cases, 5%), PIK3CA (3 cases, 3%), and ERBB2 (4 cases, 5%). In addition to ALK fusions identified by IHC/FISH in four cases, two previously known fusions involving EZR- ROS1 and KIF5B-RET were identified by RNA-Seq as well as a third novel fusion transcript that was formed between exons 1-9 of SND1 and exons 2 to 3' end of BRAF. This in-frame fusion was observed in 3/89 tested tumors and 2/64 additional never smoker lung adenocarcinoma samples. Ectopic expression of SND1-BRAF in H1299 cells increased phosphorylation levels of MEK/ERK, cell proliferation, and spheroid formation compared to parental mock-transfected control. Jointly, our results suggest a potential role of the novel BRAF fusion in lung cancer development and therapy.

Inflammation and the depot-specific secretome of human preadipocytes.

OBJECTIVE: Visceral white adipose tissue (WAT) expansion and macrophage accumulation are associated with metabolic dysfunction. Visceral WAT typically shows greater macrophage infiltration. Preadipocytes show varying proinflammatory expression profiles among WAT depots. The objective was to examine the secretomes and chemoattractive properties of preadipocytes from visceral and subcutaneous WAT. METHODS: A label-free quantitative proteomics approach was applied to study secretomes of subcutaneous and omental preadipocytes from obese subjects. Enzyme-linked immunosorbent assays and chemotaxis assays were used to confirm proinflammatory chemokine secretion between depots. RESULTS: Preadipocyte secretomes showed greater variation between depots than did intracellular protein expression. Chemokines were the most differentially secreted proteins. Omental preadipocytes induced chemoattraction of macrophages and monocytes. Neutralizing antibodies to the identified chemokines reduced macrophage/monocyte chemoattraction. Subcutaneous preadipocytes treated with interleukin-6 (IL-6) resembled omental preadipocytes in terms of chemokine secretion and macrophage/monocyte chemoattraction. Janus-activated kinase (JAK1/2) protein expression, which transduces IL-6 signaling, was higher in omental than subcutaneous preadipocytes and WAT. Inhibiting JAK in omental preadipocytes decreased chemokine secretion and macrophage/monocyte chemoattraction to levels closer to that observed in subcutaneous preadipocytes. CONCLUSIONS: Secretomes of omental and subcutaneous preadipocytes are distinct, with the former inducing more macrophage/monocyte chemoattraction, in part through IL-6/JAK-mediated signaling.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php.

Survival association rule mining towards type 2 diabetes risk assessment.

Type-2 Diabetes Mellitus is a growing epidemic that often leads to severe complications. Effective preventive measures exist and identifying patients at high risk of diabetes is a major health-care need. The use of association rule mining (ARM) is advantageous, as it was specifically developed to identify associations between risk factors in an interpretable form. Unfortunately, traditional ARM is not directly applicable to survival outcomes and it lacks the ability to compensate for confounders and to incorporate dosage effects. In this work, we propose Survival Association Rule (SAR) Mining, which addresses these shortcomings. We demonstrate on a real diabetes data set that SARs are naturally more interpretable than the traditional association rules, and predictive models built on top of these rules are very competitive relative to state of the art survival models and substantially outperform the most widely used diabetes index, the Framingham score.

Recent segmental duplications in the human genome.

Primate-specific segmental duplications are considered important in human disease and evolution. The inability to distinguish between allelic and duplication sequence overlap has hampered their characterization as well as assembly and annotation of our genome. We developed a method whereby each public sequence is analyzed at the clone level for overrepresentation within a whole-genome shotgun sequence. This test has the ability to detect duplications larger than 15 kilobases irrespective of copy number, location, or high sequence similarity. We mapped 169 large regions flanked by highly similar duplications. Twenty-four of these hot spots of genomic instability have been associated with genetic disease. Our analysis indicates a highly nonrandom chromosomal and genic distribution of recent segmental duplications, with a likely role in expanding protein diversity.

A comparison of whole-genome shotgun-derived mouse chromosome 16 and the human genome.

The high degree of similarity between the mouse and human genomes is demonstrated through analysis of the sequence of mouse chromosome 16 (Mmu 16), which was obtained as part of a whole-genome shotgun assembly of the mouse genome. The mouse genome is about 10% smaller than the human genome, owing to a lower repetitive DNA content. Comparison of the structure and protein-coding potential of Mmu 16 with that of the homologous segments of the human genome identifies regions of conserved synteny with human chromosomes (Hsa) 3, 8, 12, 16, 21, and 22. Gene content and order are highly conserved between Mmu 16 and the syntenic blocks of the human genome. Of the 731 predicted genes on Mmu 16, 509 align with orthologs on the corresponding portions of the human genome, 44 are likely paralogous to these genes, and 164 genes have homologs elsewhere in the human genome; there are 14 genes for which we could find no human counterpart.