Abstract: A system includes a computing hardware system, a network storage, and one or more data sources. A local processor of a data collector node associated with each data source is configured to collect and store incremental application data to the network storage and transmit a file path of the application data to a data API of the computing hardware system. A processor of the computing hardware system is configured to access the received file path and load the application data into a local memory. The processor is configured to generate a compute thread comprising compute queue and data queue and associate the compute thread to the host application. The processor updates the incremental application data from the local memory to the GPU memory through the compute thread and subsequently to local storage. In addition, the processor updates the application data without restarting the host application.

Abstract: System (100) and method (200) for aiding drug development by determining a curable action value of a target protein are disclosed. The system (100) comprises a protein data extraction module (110), a protein data filtration module (120), and a final expression level calculator (150). The protein data extraction module (110) is configured for parsing and identifying information related to target proteins from a public database. The public database comprises data related to proteins. The protein data filtration module (120) is configured for filtering out irrelevant information from the identified information related to the target proteins. The final expression level calculator (150) is configured for calculating the final expression level of the identified relevant information. The curable action of the target protein is based upon the final expression level. The invention aids in drug development by determining the curable action of the protein in the disease based on biomedical data corpus.

Abstract: A method for electronic processing of data items for enhanced search that includes receiving, a plurality of data items from a defined database, pairing of the plurality of data items based on one or more criterions, determining a regression score for each pair of data items associated with a corresponding pair of data items performing a hierarchical clustering of the plurality of data items and constructing a visual representation comprising hierarchical relationship among the plurality of data items clustered based on the hierarchical clustering and segregating the visual representation into a plurality of clusters based on a predetermined threshold and probabilistic similarity information. Moreover, each cluster of the plurality of clusters includes a group of data items that are similar to each other with respect to a defined number of parameters to allow retrieval during a search against one or more parameters from the defined number of parameters.

Abstract: A method for identifying molecular pathways perturbed under influence of a drug or a disease includes extracting a relationship dataset related to genes and molecular pathways associated with the genes, from a pre-curated database. The method further includes mapping the relationship dataset onto a research graph. The method further includes identifying, sub-networks within the research graph, and assigning a gene score to each gene in the identified sub-networks, based on whether a gene is neutral, dysregulated, or associated with a disease-specific organ. The method further includes determining a perturbed molecular pathway for genes within the identified sub-networks based on the gene score and a molecular pathway interconnectivity within the research graph. The perturbed molecular pathway for genes has a highest association with a pathophysiology of the disease or the drug response as compared to other molecular pathways associated with the genes in the research graph.

Abstract: A method for electronic decomposition of a data string into structurally meaningful parts. The method includes receiving the data string as an input data and splitting the data string into a plurality of tokens. The method further includes performing an inter token modelling on the plurality of tokens to obtain a first probability of arrangement of tokens with respect to each other and performing an intra token modelling on the plurality of tokens to obtain a second probability indicative of a number of occurrences of each individual token. The method further includes combining the first probability of arrangement of tokens, with the second probability to obtain a final probability of an arrangement for the data string and displaying one or more arrangements for the data string based on the final probability. The method is advantageous to remove ambiguity while determining structurally meaningful parts of the data string.

Abstract: A method for processing documents for enhanced search includes identifying a set of bounding boxes in the document. The method further includes defining one or more pairs of bounding boxes in the document. Each pair of bounding boxes is defined by a binary relation. The method further includes constructing a directed acyclic graph (DAG) from the one or more pairs of bounding boxes. The method further includes determining a topological sorting of each bounding box in the document based on the DAG. The topological sorting defines an adjacency relationship between the bounding boxes in the document. The method further includes extracting key-value pairs from the document based on the adjacency relationship between the bounding boxes in the document. The method further includes storing the key-value pairs in a key-value pair database.

Abstract: A method for electronically determining semantic relationship in data items includes extracting an entity and trigger terms associated with the entity from a sentence in the data items associated with a candidate identifier. The method further includes parsing the sentence in the data items to generate a parse tree. The method further includes identifying a node with a specific syntactic function associated with a candidate trigger term in the parse tree. The method further includes executing a search for the entity in the subtree originating at the identified node of the parse tree and then annotating context of the entity based on a presence of the entity in the subtree. The annotated context of the entity is used for determining semantic relationship in the data items to enable context-based search.

Abstract: A system and a method for token-based classification for reducing overlap in field extraction during parsing of a text is disclosed. The method includes extracting text from a resource. The method further includes splitting one or more sentences into a predetermined number of plurality of tokens. The method furthermore includes generating a plurality of lists using a machine learning model for identifying one or more fields in the text. The plurality of lists comprises at least a list of tokens, a list of tags and a list of confidence scores of tokens. The method furthermore includes post processing the plurality of lists for extracting one or more fields for parsing the text.

Abstract: A system for identifying one or more changes in a Biological Network, comprising a processor configured to construct a Heterogeneous Biological Network that comprises a plurality of nodes and a plurality of edges. The processor is configured to derive one or more sub-networks from the constructed HBN, and determine an embedding vector for each node of each sub-network. The processor is configured to identify one or more changes in each sub-network by comparing the embedding vector of each node of in a respective sub-network before and after an input action associated with a change in at least one sub-network and determine a plurality of scores for each node of each sub-network based on a pre-defined set of parameters. The processor is configured to identify the one or more changes in the BN based on the determined plurality of scores for each node in each sub-network.

Abstract: A method of generating an ontology for enhanced search is disclosed that includes an iterative process. The method includes loading seed entities in a memory and using a look-up API to search a first set of similar terms for each seed entity from a web-based database. The first set of similar terms is then clustered based on different concepts to obtain a first cluster of similar terms. The method further includes determining a second set of similar terms for each seed entity based on a category of each seed entity. The second set of similar terms is then clustered based on the different concepts to obtain a second cluster of similar terms. The first and second clusters are merged to generate the ontology of the seed entities. The iterative process is repeated until a saturation point is reached, at which point the generated ontology is considered complete.

Abstract: A system and a method for converting protein data bank (PDB) files into a grayscale image array is provided. The method includes extracting PDB files from a PDB, using a data extraction module. The method also includes converting, using a file conversion module, the PDB files into a data frame based on BioPandas. The method further includes selecting, using a column selection module, one or more columns from the data frame based on a pre-determined criteria. The method furthermore includes converting, using a column conversion module, the selected one or more columns into a NumPy array for rendering the data frame to resemble an image. The method furthermore includes resizing the NumPy array using an OpenCV for making size of the NumPy array uniform and for generating a grayscale image array.

Abstract: A system and a method for converting a protein data bank file into a two-dimensional numerical matrix is provided. The method also includes extracting PDB files from a PDB, using a data extraction module. The method further includes analyzing the PDB files using an analysis module for calculating and visualizing interatomic interactions in protein structures. The method further includes generating, using a file generation module, a comma separated values (CSV) file based on the analysis. The method further includes performing a featurization of the CSV file using a featurization module. The method further includes generating, using a matrix generating module, the two-dimensional numerical matrix based on the featurization.

Abstract: A system and a method for predicting a binding affinity of protein structures based on deep learning is disclosed. The method includes capturing, using a data capture module, a multi-dimensional structure of a plurality of protein-protein complexes from a protein sequence data set. The method also includes performing featurization, using a featurization module, of the multi-dimensional structure of the plurality of protein-protein complexes by creating an adjacency matrix for successive spherical shells centered around each type of a plurality of amino acid sequences. The method further includes predicting, using a prediction module, a binding affinity from text sequence of the plurality of amino acid sequences using a pre-trained artificial intelligence model based on the adjacency matrix.

Abstract: A system for electronic identification of one or more biomarkers associated with a pathology. The system includes a processor configured to receive an expression data and utilize a predefined degenerative model to generate a Transcription Factor (TF)-target interaction information based on the received expression data. The processor is configured to execute the predefined degenerative model to generate a Gene Regulatory Network (GRN) based on the TF-target interaction information and construct a hierarchical network defining hierarchical relationships among two or more nodes and one or more edges of the GRN. The processor is configured to prioritize a set of TFs and a set of targets using a Protein-Protein Interaction (PPI) network and construct a modified GRN. The system efficiently and reliably identifies the one or more biomarkers associated with the pathology by use of a combination of the GRN, the hierarchical network and the PPI network.

Abstract: A system for an electronic identification of responders and non-responders for one or more drugs, includes a processor to obtain a pre-labeled training dataset from a first database, which includes a first type of input of labeled pretreatment data and a second type of input of labeled post-treatment response data of the plurality of subjects. The processor is configured to pre-process the pre-labeled training data to generate a modified labeled training dataset and train an ensemble machine learning (ML) model from the modified labeled training dataset. The processor is further configured to train a prediction model based on set of features extracted via the ensemble ML model to obtain a trained prediction model to detect whether an unknown subject is a responder or a non-responder for a given drug from the one or more drugs, such as only responders can be given a drug to spare non-responders from unwarranted effects.

Abstract: A method of workload management for distributing workload over a single instruction and multiple data (SIMD) based parallel processing architecture includes pre-processing of vert large ontology file on the host processor. The outcome of the preprocessing step is a set of arrays which are loaded on the SIMD based parallel processing architecture to process the ontology file over input text documents of any kind and generate the tagged outcome. The method provides the maximum granularity to facilitate allocation of the maximum number of software threads on the SIMD based parallel processing architecture to achieve minimum document processing latency.

Abstract: A method and system for generating a plurality of antibody sequences of a target from one or more framework regions based on at least one model. The model is trained on a training dataset of high binding affinity to generate the complementarity determining regions (CDR) from the received one or more framework regions (FR). The generated complementarity determining regions (CDR) from the each of the one or more framework regions (FR) are combined with the associated one or more framework regions to generate one or more regions of the target. The generated one or more regions comprises each of the received one or more framework regions (FR) and corresponding each of the generated complementarity determining regions (CDR). The generated one or more regions are concatenated to generate the plurality of antibody sequences of the target. The generated plurality of antibody sequences of the target has high binding affinity.

Abstract: A system for predicting biological activities or properties of one or more chemical compounds in a new drug. The system includes a processor configured to receive a plurality of input datasets including a plurality of simplified molecular-input line-entry system (SMILES) notations and execute a pre-trained Natural Language Processing (NLP) model to transform the plurality of SMILES notations into a plurality of non-sparse matrices. The processor is configured to train a deep learning model using the plurality of non-sparse matrices to obtain a trained deep learning model, where the trained deep learning model is used to predict one or more biological activities of an untested chemical compound in the new drug when the chemical compound in the drug is subjected to at least one chemical modification to form the new drug. The system efficiently and reliably predicts the biological activities or properties of the untested chemical compound in the new drug.

Abstract: A method and system for generating a plurality of new antibody sequences corresponding to a target from a single lead antibody sequence. A model is pre-trained with a training dataset of plurality of known antibody sequences to learn a structural pattern from the plurality of known antibody sequences. The method includes receiving the single lead antibody sequence. The lead antibody sequence has one or more regions. The regions have one or more lead framework regions (FR) and one or more lead complementarity determining regions (CDR). The pre-trained model is configured to process the single lead antibody sequence to identify a relationship between the one or more lead framework regions (FR) and one or more lead complementarity determining regions (CDR). A plurality of new antibody sequences are generated from the single lead antibody amino acid of the sequence by the pre-trained model based on the identified relationship and the structural pattern.

Abstract: A method and system for evaluating the performance of the cluster algorithm is provided herein. The method and system obtain a first field and a plurality of records, wherein each of the plurality of records is associated with one or more fields. The method and system further compare the first field with the each of the one or more fields, and compute a first score of similarities based on the comparison The method and system further generate a vector for the first field and one or more vectors for each of the one or more fields, and compare the vector for the first field with each of the one or more vectors. The method and system further identify a first plurality of clusters based on the first score of similarities, and generate a tree-structured graph-based model based on the first plurality of clusters. The method and system further calculate a second score of similarities based on the tree-structured graph-based model.