Abstract: A method for training a deep learning model configured for virtual screening of molecules for micro ribonucleic acid (miRNA) drug discovery includes creating a training dataset including a plurality of assay datasets. The method also includes training the deep learning model to learn a plurality of tasks; performing, using the deep learning model, inference for the respective task associated with at least one miRNA assay dataset; and evaluating respective performance metrics associated with the respective predictions for each of the plurality of tasks. The method further includes selecting a set of the plurality of assay datasets for training; and training the deep learning model to learn a set of the plurality of tasks using the set of the plurality of assay datasets. The trained deep learning model is configured to predict molecules capable of affecting a target miRNA.

Abstract: The present disclosure relates generally to detection on non-coding RNAS molecules in a sample or diagnosis of subject based upon detection or quantification of non-coding nucleic acid sequences in a sample, specifically to identify and use of molecular biomarkers for cancer including breast cancer.

Abstract: The present disclosure relates generally to detection on non-coding RNAS molecules in a sample or diagnosis of subject based upon detection or quantification of non-coding nucleic acid sequences in a sample, specifically to identify and use of molecular biomarkers for cancer including breast cancer.

Abstract: Provided herein are in-vitro, in-vivo, and ex-vivo models systems, methods of creating such model systems, and methods of using such model systems for assessing one or more therapeutic properties of a candidate agent or identifying a new therapeutic target. Also provided are computer-readable media and systems that find use, e.g., in practicing the methods of the present disclosure.

Abstract: Embodiments described herein provide a neural network based cancer detection and subtyping tool for predicting the presence of a tumor, its tissue of origin, and its subtype using small RNA sequencing (smRNA-seq) data, for example, the oncRNA count data. Specifically, the AI-based cancer detection and subtyping tool uses variational Bayes inference and semi-supervised training to adjust for batch effects and learn a low dimensional distribution explaining biological variability of the data. A method is also provided for determining the likely subtype(s) in a cancer sample.

Abstract: Methods are provided that implement tagmentation for single-molecule sequencing use 90-99% less input than current protocols: SMRT-Tag, which allows detection of genetic variation and CpG methylation, and SAMOSA-Tag, which uses exogenous adenine methylation to add a third channel for probing chromatin accessibility. SAMOSA-Tag of 30,000-50,000 nuclei resolved single-fiber chromatin structure, CTCF binding, and DNA methylation in patient-derived prostate cancer xenografts and uncovered metastasis-associated global epigenome disorganization.

Abstract: The present disclosure relates generally to detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of non-coding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis.

Abstract: The present disclosure relates generally to detection on non-coding RNAS molecules in a sample or diagnosis of subject based upon detection or quantification of non-coding nucleic acid sequences in a sample, specifically to identify and use of molecular biomarkers for cancer including breast cancer.

Abstract: The present disclosure relates generally to detection of non-coding RNAS molecules in a sample or diagnosis of subject based upon detection or quantification of non-coding nucleic acid sequences in a sample, specifically to identify and use of molecular biomarkers for cancer including breast cancer.

Abstract: In one embodiment, the present invention relates to a method for optimizing cell-type specific protein expression. In another embodiment, the present invention relates to a method for creating a tRNA profile of a cell.

Abstract: In one embodiment, the present invention relates to a method for optimizing cell-type specific protein expression. In another embodiment, the present invention relates to a method for creating a tRNA profile of a cell.