Differential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumorspecific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

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Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typically formulates boolean queries followed by scanning the retrieved records for relevance, which is very time consuming and error prone. We applied Support Vector Machines (SVM) for automatic retrieval of PubMed articles related to Human genome epidemiological research at CDC (Center for disease Control and Prevention). In this paper, we discuss various investigations into biomedical literature classification and analyze the effect of various issues related to the choice of keywords, training sets, kernel functions and parameters for the SVM technique. We report on the various factors above to show that SVM is a viable technique for automatic classification of biomedical literature into topics of interest such as epidemiology, cancer, birth defects etc. In all our experiments, we achieved high values of PPV, sensitivity and specificity.

Background: Cholera, a diarrheal illness causes millions of deaths worldwide due to large outbreaks. Monoclonal antibody used as therapeutic purposes of cholera are prone to be unstable due to various factors including self-aggregation. Objectives: In this bioinformatic analysis, we identified the aggregation prone regions (APRs) of different immunogens of antibody sequences (i.e., CTB, ZnM-CTB, ZnP-CTB, TcpA-CT-CTB, ZnM-TcpA-CT-CTB, ZnP-TcpA-CT-CTB, ZnM-TcpA, ZnP-TcpA, TcpA-CT-TcpA, ZnM-TcpA-CT-TcpA, ZnP-TcpA-CT-TcpA, Ogawa, Inaba and ZnM-Inaba) raised against Vibrio cholerae. Methods: To determine APRs in antibody sequences that were generated after immunizing Vibrio cholerae immunogens on Mus musculus, a total of 94 sequences were downloaded as FASTA format from a protein database and the algorithms such as Tango, Waltz, PASTA 2.0, and AGGRESCAN were followed to analyze probable APRs in all of the sequences. Results: A remarkably high number of regions in the monoclonal antibodies were identified to be APRs which could explain a cause of instability/short term protection of anticholera vaccine. Conclusion: To increase the stability, it would be interesting to eliminate the APR residues from the therapeutic antibodies in a such way that the antigen binding sites or the complementarity determining region loops involved in antigen recognition are not disrupted.