Abstract: The disclosure relates to prostate neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the prostate neoantigens, and methods of making and using them.

Abstract: The disclosure relates to ovarian cancer neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the ovarian cancer neoantigens, and methods of making and using them.

Abstract: The disclosure relates to prostate neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the prostate neoantigens, and methods of making and using them.

Abstract: Disclosed herein are methods of diagnosing and treating a subject with prostate cancer, as well as methods of monitoring the responsiveness of a subject having prostate cancer to a therapeutic agent.

Abstract: Disclosed herein are self-replicating RNA molecules encoding prostate neoantigens, vaccines, and methods of treating and preventing prostate cancer.

Abstract: Disclosed herein are self-replicating RNA molecules encoding prostate neoantigens, vaccines, and method of treating and preventing prostate cancer using the self-replicating RNA molecules and vaccines.

Abstract: Disclosed herein are polypeptide fragments and polynucleotides based on mutant capicua transcriptional repressor (CIC), catenin beta 1 (CTNNB1), v-erb-b2 erythroblastic leukemia viral oncogene homolog B (ERBB2), kirsten rat sarcoma (KRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphatase and tensin homolog (PTEN), splicing factor 3b subunit 1 (SF3B1), SRY-box transcription factor 17 (SOX17), tumor protein 53 (TP53), and cytomegalovirus (CMV) sequences, vectors, host cells, viruses, methods for generating CD8+ T-cells, and methods of treatment. Also disclosed herein are T-cell receptors (TCRs), polynucleotides, vectors and cells comprising the TCRs, and methods of treatment.

Abstract: The disclosure relates to multiple myeloma neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the multiple myeloma neoantigens, and methods of making and using them.

Abstract: The disclosure relates to ovarian cancer neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the ovarian cancer neoantigens, and methods of making and using them.

Abstract: The disclosure relates to prostate neoantigens, polynucleotides encoding them, vectors, host cells, recombinant virus particles, vaccines comprising the neoantigens, proteinaceous molecules binding the prostate neoantigens, and methods of making and using them.

Abstract: Techniques are disclosed herein for reactively identifying software products, available from an electronic marketplace, that are exhibiting anomalous behavior. Data associated with software products is accessed and analyzed to determine anomalous behavior. The data analyzed may include, but is not limited to, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to classify the application into a category relating to whether a potential anomaly is identified for the software product. A score may also be calculated for the software applications that indicates a severity of the anomalous behavior. The classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product. For instance, the score may be used to determine that the software product is to be tested by a testing service.

Abstract: Techniques are disclosed herein for identifying software products, available from an electronic marketplace, to be tested. Data associated with software products is accessed and analyzed to determine what software products to test. The data analyzed may include, but is not limited to, download data, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to predict a popularity of a software product, classify the application into a category relating to whether a potential anomaly is identified for the software product, and determine whether to test the software product. A score may also be calculated for the software products that indicates whether or not to test the software product. The predicted popularity, the classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product.

Abstract: The present invention provides methods and systems for analyzing mammalian transcriptomes, particularly, for low abundant transcripts, and with the use of high throughput technologies. Heptamer primers and sequence tags generated by the iterative randomized algorithm, as well as the sequencing-library generation system for amplifying and synthesis-based sequencing low abundant transcripts using the heptamer primers are also provided. The present invention further provides the use of the invention system and method for identifying key embryological lineage specific transcripts that anticipate differentiation of specific cell types.

Abstract: The present invention provides methods and systems for analyzing mammalian transcriptomes, particularly, for low abundant transcripts, and with the use of high throughput technologies. Heptamer primers and sequence tags generated by the iterative randomized algorithm, as well as the sequencing-library generation system for amplifying and synthesis-based sequencing low abundant transcripts using the heptamer primers are also provided. The present invention further provides the use of the invention system and method for identifying key embryological lineage specific transcripts that anticipate differentiation of specific cell types.