Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods and systems for identifying and quantifying molecular biomarkers and for predicting patient response to cancer therapy are provided. The disclosed methods and systems make use of artificial intelligence to capture morphometric changes from histopathology tissue that correlate with molecular changes. The system may analyze molecular markers which are predictive of tumor response and have no defined correlation with morphological features. The system may use artificial intelligence to correlate morphometric changes with critical gene modifications and molecular changes.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods and systems for identifying and quantifying molecular biomarkers and for predicting patient response to cancer therapy are provided. The disclosed methods and systems make use of artificial intelligence to capture morphometric changes from histopathology tissue that correlate with molecular changes. The system may analyze molecular markers which are predictive of tumor response and have no defined correlation with morphological features. The system may use artificial intelligence to correlate morphometric changes with critical gene modifications and molecular changes.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: The subject invention relates to a new forward motility protein isolated from goat epididymal plasma and a process for the isolation of the said epididymal forward motility protein useful as a fertility promoter/blocker comprising subjecting goat epididymal plasma to multiple fractionations till purification to obtain purified new epididymal forward motility protein by the chromatographic method.

Abstract: The subject invention relates to a new forward motility protein isolated from goat epididymal plasma and a process for the isolation of the said epididymal forward motility protein useful as a fertility promoter/blocker comprising subjecting goat epididymal plasma to multiple fractionations till purification to obtain purified new epididymal forward motility protein by the chromatographic method.