Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.

Abstract: The present invention is directed to combinations of menin inhibitors with one or more CYP3A4 inhibitors, pharmaceutical compositions thereof, and methods of treating cancer and other diseases mediated by the menin-MLL interaction.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.

Abstract: Described herein are methods for selecting cancer patients for treatment with a combination therapy comprising entinostat and a second therapeutic agent. In particular, methods are provided for the examination of a non-cancer cell type, myeloid-derived suppressor cells, e.g., those which are CD14-positive and HLA-DR-(lo/negative), as a therapeutic indicator in the setting of entinostat combination therapies.

Abstract: The present invention relates to methods of treating sclerotic conditions and more specifically to methods of treating chronic graft versus host disease with specific dosages of an anti-CSF-1R antibody, specifically axatilimab.

Abstract: Described herein are methods for the treatment of cancer in a subject. In particular, methods are provided for the treatment of non small cell lung cancer and melanoma with a combination of entinostat and an anti-PD-1 or an anti-PD-L1 antibody.

Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.

Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: Described herein are methods for selecting cancer patients for treatment with a combination therapy comprising entinostat and a second therapeutic agent. In particular, methods are provided for the examination of a non-cancer cell type, myeloid-derived suppressor cells, e.g., those which are CD14-positive and HLA-DR-(lo/negative), as a therapeutic indicator in the setting of entinostat combination therapies.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: Described herein are methods for the treatment of cancer in a subject. In particular, methods are provided for the treatment of non small cell lung cancer and melanoma with a combination of entinostat and an anti-PD-1 or an anti-PD-L1 antibody.

Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

Abstract: Described herein are methods for selecting cancer patients for treatment with a combination therapy comprising an HDAC inhibitor and a second therapeutic agent. In particular, methods are provided for the examination of a non-cancer cell type which are CD14-positive, HLA-DR-high and/or CD16-negative, as a therapeutic indicator in the setting of HDAC inhibitor combination therapies.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present disclosure relates to a combination of an anti-CSF-1R antibody or anti-CSF-1 antibody or antigen binding fragment thereof or an inhibitor of CSF-1R activity and an HDAC inhibitor, e.g., entinostat, and methods of using the combination for administering to subjects in need thereof for the treatment of cancer.

Abstract: Described herein are methods for the treatment of breast cancer in a subject. In particular, methods are provided for the treatment of resistant, metastatic breast cancer with a combination of entinostat and an aromatase inhibitor.

Abstract: Described herein are methods for the treatment of heavily pre-treated recurrent ovarian cancer. In particular, methods are provided for the treatment of heavily pre-treated recurrent ovarian cancer with a combination of entinostat and an anti-PD-L1 antibody.