Patents by Inventor Nilanjana Banerjee

Nilanjana Banerjee has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200395128
    Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.
    Type: Application
    Filed: June 2, 2020
    Publication date: December 17, 2020
    Inventors: Angel Janevski, Nevenka Dimitrova, Sitharthan Kamalakaran, Yasser Alsafadi, Nilanjana Banerjee, Anca Ioana Daniela Bucur, Jasper Johannes Adrianus van Leeuwen, Vinay Varadan
  • Patent number: 10679726
    Abstract: Relevance of a study genetic variant observed in diagnostic subject genetic data that is associated by a clinical study with a phenotype characteristic is assessed as follows. A set of polymorphisms functionally related to the study genetic variant are identified. A foreground distribution is computed of variants observed in the diagnostic subject genetic data for the set of polymorphisms. A background distribution is computed of variants observed in genetic data of subjects of the clinical study for the set of polymorphisms. A comparison metric is computed comparing the foreground distribution and the background distribution. Relevance of the study variant to the diagnostic subject is quantified based on the comparison metric, with higher similarity of the foreground and background distributions corresponding to higher relevance.
    Type: Grant
    Filed: November 15, 2013
    Date of Patent: June 9, 2020
    Assignee: Koninklijke Philips N.V.
    Inventors: Sitharthan Kalalakaran, Vinay Varadan, Nilanjana Banerjee, Angel Janevski, Nevenka Dimitrova
  • Patent number: 10541052
    Abstract: A catalog (34) of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record (22) stores genetic sequencing data (20) of a patient. A clinical decision support (CDS) system (30) is configured to track the clinical context of the patient wherein the clinical context includes at least a disease diagnosis and a current patient care stage. A catalog search module (32) is configured to search the catalog of molecular marker tests to identify a molecular marker test having clinical applicability to the patient in the clinical context tracked by the CDS system. The search is automatically triggered by occurrence of a trigger event defined by a set of triggering rules. A testing module (44) is configured to perform a molecular marker test identified by the identification module in silico using the genetic sequencing data of the patient stored in the electronic patient medical record.
    Type: Grant
    Filed: November 29, 2012
    Date of Patent: January 21, 2020
    Assignee: Koninklijke Philip N.V.
    Inventors: Vinay Varadan, Sitharthan Kamalakaran, Angel Janevski, Nilanjana Banerjee, Nevenka Dimitrova
  • Patent number: 10460831
    Abstract: In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-? signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-? signaling pathway. Related predictive outcome assessment test development methods are also disclosed.
    Type: Grant
    Filed: November 22, 2013
    Date of Patent: October 29, 2019
    Assignee: Koninklijke Philips N.V.
    Inventors: Vinay Varadan, Sitharthan Kamalakaran, Angel Janevski, Nilanjana Banerjee, Nevenka Dimitrova, Lyndsay Harris
  • Patent number: 10340027
    Abstract: The present invention relates to a method for identifying multi-modal associations between biomedical markers which allows for the determination of network nodes and/or high ranking network members or combinations thereof, indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular ovarian cancer. The present invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably a platinum based cancer therapy, wherein said biomedical marker or group of biomedical markers comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 8, 19, 20 or all markers selected from PKMYT1, SKIL, RAB8A, HIRIP3, CTNNB1, NGFR, ZCCHC11, LSP1, CD200, PAX8, CYBRD1, HOXC11, TCEAL1, FZD10, FZD1, BBS4, IRS2, TLX3, TSPAN2, TXN, and CFLAR.
    Type: Grant
    Filed: October 4, 2011
    Date of Patent: July 2, 2019
    Assignee: Koninklijke Philips N.V.
    Inventors: Nilanjana Banerjee, Angel Janevski, Sitharthan Kamalakaran, Vinay Varadan, Nevenka Dimitrova, Robert Lucito
  • Publication number: 20190079938
    Abstract: In patient cohort identification, clustering (30) of patients is performed using a patient comparison metric dependent on a set of features (24). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster (34) containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations (70, 72, 74) each plotting the sample patients and the query patient against two or more features of the modality.
    Type: Application
    Filed: March 8, 2017
    Publication date: March 14, 2019
    Inventors: Vartika Agrawal, Alexander Ryan Mankovich, Nevenka Dimitrova, Nilanjana Banerjee, Yee Him Cheung, Johanna Maria De Bont, Jozef Hieronymus Maria Raijmakers
  • Publication number: 20180330805
    Abstract: A data-driven integrative visualization system and a method for visualization and exploration of the multi-modal features of a cohort of samples, is disclosed Specifically, a method for providing an interactive computation and visualization front-end of a genomics platform for presenting the complex multiparametric and high dimensional, multi-omic data of a patient with respect to a cohort of samples, that assists the user in understanding the similarities and differences across individual or groups of samples, identify correlation among different features and improve treatment planning and long term patient care, is described. The method may include obtaining and inputting multi-omic data of a patient and/or cohorts, identifying multi-modal feature variations and their relationships, and displaying this information in an interactive circular format on a GUI, from which the user can access further information.
    Type: Application
    Filed: May 8, 2018
    Publication date: November 15, 2018
    Inventors: Yee Him Cheung, Yong Mao, Nevenka Dimitrova, Nilanjana Banerjee, Johanna Maria de Bont, Jozef Hieronymus Maria Raijmakers, Kostyantyn Volyanskyy
  • Publication number: 20180247010
    Abstract: A system and method for determining the functional impact of somatic mutations and genomic aberrations on downstream cellular processes by integrating multi-omics measurements in cancer samples with community-curated biological pathways are disclosed.
    Type: Application
    Filed: August 26, 2016
    Publication date: August 30, 2018
    Inventors: Abolfazl Razi, Vinay Varadan, Nevenka Dimitrova, Nilanjana Banerjee
  • Publication number: 20180089392
    Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.
    Type: Application
    Filed: November 29, 2017
    Publication date: March 29, 2018
    Inventors: Angel Janevski, Nevenka Dimitrova, Sitharthan Kamalakaran, Yasser Alsafadi, Nilanjana Banerjee, Anca Ioana Daniela Bacur, Jasper Johannes Adrianus van Leeuwen, Vinay Varadan
  • Patent number: 9858392
    Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.
    Type: Grant
    Filed: May 6, 2009
    Date of Patent: January 2, 2018
    Assignee: Koninklijke Philips N.V.
    Inventors: Angel J. Janevski, Nevenka Dimitrova, Sitharthan Kamalakaran, Yasser Alsafadi, Nilanjana Banerjee, Anca Ioana Daniela Bacur, Jasper Van Leeuwen, Vinay Varadan
  • Publication number: 20170364633
    Abstract: A method of identifying co-expressed coding and noncoding genes is disclosed. The method may include receiving genetic sequences, mapping the genetic sequences to known coding and noncoding genes, correlating the mapped genes, and generating a co-expression network. A system for generating a co-expression network and providing the co-expression network to a user on a display is disclosed. The system may include a memory, one or more processors, one or more databases, and a display.
    Type: Application
    Filed: December 7, 2015
    Publication date: December 21, 2017
    Inventors: NILANJANA BANERJEE, NEVENKA DIMITROVA, SONIA CHOTHANI, WILHELMUS FRANCISCUS JOHANNES VERHAEGH, YEE HIM CHEUNG
  • Patent number: 9798856
    Abstract: An imaging visualization workstation (30) includes a graphical display device (32) and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image (12) of the medical subject; combining the medical image with a graphical representation of information (20, 22) generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.
    Type: Grant
    Filed: March 20, 2013
    Date of Patent: October 24, 2017
    Assignee: Koninklijke Philips N.V.
    Inventors: Nilanjana Banerjee, Sitharthan Kamalakaran, Vinay Varadan, Angel Janevski, Nevenka Dimitrova
  • Publication number: 20170286597
    Abstract: Methods and systems for visualizing gene expression data in a way that permits the comparison of different patient groups to facilitate medical applications, including cancer diagnostics and treatment planning, particularly breast cancer. The method organises gene expression data for at least one patient into a plurality of windows of a specified size, calculates an average RSEM score for all of the genes in each window and presents the average RSEM scores in a two-dimensional array, wherein one axis organises the windows by patient and the other axis organises the windows by sequence.
    Type: Application
    Filed: August 17, 2015
    Publication date: October 5, 2017
    Inventors: ALEXANDER RYAN MANKOVICH, NEVENKA DIMITROVA, VARTIKA AGRAWAL, NILANJANA BANERJEE
  • Patent number: 9552649
    Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.
    Type: Grant
    Filed: October 25, 2013
    Date of Patent: January 24, 2017
    Assignee: Koninklijke Philips N.V.
    Inventors: Nilanjana Banerjee, Nevenka Dimitrova, Vinay Varadan, Sitharthan Kamalakaran, Angel Janevski, Sayan Maity
  • Publication number: 20150347679
    Abstract: In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-? signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-? signaling pathway. Related predictive outcome assessment test development methods are also disclosed.
    Type: Application
    Filed: November 22, 2013
    Publication date: December 3, 2015
    Inventors: VINAY VARADAN, SITHARTHAN KAMALAKARAN, ANGEL JANEVSKI, NILANJANA BANERJEE, NEVENKA DIMITROVA, LYNDSAY HARRIS
  • Publication number: 20150310632
    Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.
    Type: Application
    Filed: October 25, 2013
    Publication date: October 29, 2015
    Inventors: NILANJANA BANERJEE, NEVENKA DIMITROVA, VINAY VARADAN, SITHARTHAN KAMALAKARAN, ANGEL JANEVSKI, SAYAN MAITY
  • Publication number: 20150294063
    Abstract: Relevance of a study genetic variant observed in diagnostic subject genetic data that is associated by a clinical study with a phenotype characteristic is assessed as follows. A set of polymorphisms functionally related to the study genetic variant are identified. A foreground distribution is computed of variants observed in the diagnostic subject genetic data for the set of polymorphisms. A background distribution is computed of variants observed in genetic data of subjects of the clinical study for the set of polymorphisms. A comparison metric is computed comparing the foreground distribution and the background distribution. Relevance of the study variant to the diagnostic subject is quantified based on the comparison metric, with higher similarity of the foreground and background distributions corresponding to higher relevance.
    Type: Application
    Filed: November 15, 2013
    Publication date: October 15, 2015
    Applicant: KONINKLIJKE PHILIPS N.V.
    Inventors: Sitharthan Kalalakaran, Vinay Varadan, Nilanjana Banerjee, Angel Janevski, Nevenka Dimitrova
  • Publication number: 20150097868
    Abstract: An imaging visualization workstation (30) includes a graphical display device (32) and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image (12) of the medical subject; combining the medical image with a graphical representation of information (20, 22) generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.
    Type: Application
    Filed: March 20, 2013
    Publication date: April 9, 2015
    Inventors: Nilanjana Banerjee, Sitharthan Kamalakaran, Vinay Varadan, Angel Janevski, Nevenka Dimitrova
  • Publication number: 20150058322
    Abstract: When generating visual representations of gene activity pathways for clinical decision support, a validated pathway database that stores a plurality of validated pathways is accessed, wherein each pathway describes at least one interaction between a plurality of genes. A processor (18) is configured to execute computer-executable instructions stored in a memory (16), the instructions comprising visually representing gene activity level (28) for at least one gene across a plurality of populations, retrieving a pathway (32) from the validated pathway database, wherein the pathway includes the at least one gene, and visually representing gene activity levels for all genes in the pathway.
    Type: Application
    Filed: March 27, 2013
    Publication date: February 26, 2015
    Applicant: KONINKLIJKE PHILIPS N.V.
    Inventors: Nevenka Dimitrova, Angel Janevski, Nilanjana Banerjee, Vinay Varadan, Sitharthan Kamalakaran
  • Patent number: 8924232
    Abstract: A method (10) for clinical decision support by comparison of multiple molecular signatures of biological data is provided. The method comprises comparing at least two of said molecular signatures are different kinds of molecular signatures. Furthermore, a device (70), a system (100), and a computer program product (200) and a use for clinical decision support, performing the steps according to the method (10) is provided.
    Type: Grant
    Filed: May 5, 2010
    Date of Patent: December 30, 2014
    Assignee: Koninklijke Philips N.V.
    Inventors: Yasser H. Alsafadi, Nilanjana Banerjee, Vinay Varadan, Angel J. Janevski