Abstract: The present disclosure encompasses systems and methods for predicting embryo ploidy. Specific embodiments encompass methods of non-invasively predicting ploidy status of an embryo, by receiving a dataset with video including a plurality of image frames of the embryo, analyzing the plurality of image frames by one or more machine and/or deep learning model via one or more classification task applied to the dataset; and generating an output prediction of the ploidy status of the embryo. Particular methods relate to methods wherein the dataset additionally includes one or more clinical and/or morphological features for the embryo, such as maternal age at the time of oocyte retrieval. Embodiments also relate to predicting embryo viability and/or improving embryo selection, such as during in vitro fertilization, and uses thereof.

Abstract: A method for classifying human blastocysts includes obtaining images of a set of artificially fertilized (AF) embryos incubating in an incubator. A morphological quality of the AF embryos is determined based on a classification of the images by a convolutional neural network trained using images of pre-classified embryos. Each of the AF embryos is graded based on the morphological quality. A probability that a given graded AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female is computed for each of the AF embryos from the set based on a grade of the given AF embryo and clinical parameters associated with the gestating female. One or more graded AF embryos to be recommended to be implanted in the gestating female from the set are selected based on the probability of successful pregnancy.

Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Abstract: The present application provides methods and systems for detecting and quantifying chromosomal instability from histology images with machine learning. Also described herein are methods for selecting treatments for a medical disease, by determining a chromosomal instability pathological metric from histology images. The disclosed methods and systems may also be used to investigate disease progression and prognosis.

Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Abstract: A computational model may be used to predict targets of a candidate, or predict candidates that interact with a target. A plurality of pairs may be established, each including a candidate and a respective one of a plurality of controls, each of the plurality of controls known to bind with a target. For each pair, values of at least two datatypes of the candidate may be compared to values of the at least two datatypes of the respective one of the plurality of controls in the pair to generate a similarity score for each of the at least two datatypes of each pair. Similarity scores may be converted to likelihood values indicating likelihood that the candidate and the controls have a shared target based on the respective one of the at least two datatypes. Tests may be performed to validate predictions regarding interactivity of candidates and targets.

Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Abstract: The present invention relates to biomarkers of use for treating Trop-2 expressing cancer with an anti-Trop-2 ADC comprising an anti-Trop-2 antibody conjugated to an inhibitor of topoisomerase I, preferably SN-38 or DxD. The anti-Trop-2 ADC may be administered as a monotherapy or as a combination therapy with one or more anti-cancer agents, such as DDR inhibitors. Therapy with the ADC alone or in combination with other anti-cancer agents can reduce solid tumors in size, reduce or eliminate metastases and is effective to treat cancers resistant to standard therapies. Preferably, the combination therapy has an additive effect on inhibiting tumor growth. Most preferably, the combination therapy has a synergistic effect on inhibiting tumor growth.

Abstract: A method for classifying human blastocysts includes obtaining images of a set of artificially fertilized (AF) embryos incubating in an incubator. A morphological quality of the AF embryos is determined based on a classification of the images by a convolutional neural network trained using images of pre-classified embryos. Each of the AF embryos is graded based on the morphological quality. A probability that a given graded AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female is computed for each of the AF embryos from the set based on a grade of the given AF embryo and clinical parameters associated with the gestating female. One or more graded AF embryos to be recommended to be implanted in the gestating female from the set are selected based on the probability of successful pregnancy.

Abstract: Systems and methods for computational analysis of chemical data to predict binding targets of a chemical are provided. A plurality of chemical pairs is established, each including a first chemical for which binding targets are to be predicted and a respective one of the second chemicals. For each chemical pair, values of at least two datatypes of the first chemical can be compared to values of the at least two datatypes of the respective one of the plurality of second chemicals in the chemical pair to generate a similarity score. The similarity scores can be converted to a likelihood value. For each chemical pair, a total likelihood value can be determined based on respective likelihood values for each of the at least two datatypes of the chemical pair. A candidate binding target is predicted to bind to the first chemical, based on the total likelihood value of each chemical pair.

Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Abstract: The application describes methods for accurately evaluating whether thyroid test samples, especially indeterminate thyroid samples, are benign or malignant.

Abstract: The application describes methods for accurately evaluating whether thyroid test samples, especially indeterminate thyroid samples, are benign or malignant.

Abstract: The application describes methods for accurately evaluating whether thyroid test samples, especially indeterminate thyroid samples, are benign or malignant.

Abstract: The application describes methods for accurately evaluating whether thyroid test samples, especially indeterminate thyroid samples, are benign or malignant.

Abstract: A method of identification of drug targets and drug resistance mechanisms in human cells of a drug comprising the steps of: generating at least one drug-resistant sample and at least one drug-sensitive sample; analyzing substantial portions of the genome and/or transcriptome of the least one drug-resistant sample and drug-sensitive sample to obtain sequencing data; detecting substantially all alterations in the at least drug-resistant sample; deriving a resistance signature; and performing analysis of the drug resistance signature of at least one recurrently altered gene using bioinformatic tools and cellular biology methods to determine if alteration of the at least one gene of the drug resistance signature is sufficient to confer at least partial resistance to cells or tissues against the drug.