Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Autor: Aditya Rao, Saipradeep VG, Thomas Joseph, Sujatha Kotte, Naveen Sivadasan, Rajgopal Srinivasan

Publikováno v: BMC Medical Genomics, Vol 11, Iss 1, Pp 1-12 (2018)

Abstract Background One of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype in

Externí odkaz: https://doaj.org/article/2f226c2edccf47bc8554e5da314add54

Attention based Transformers for Mitosis Domain Generalization in H&E stained WSIs

Autor: Saipradeep, VG, Kotte, Sujatha, Sivadasan, Naveen, Joseph, Thomas, Sharma, Hrishikesh, Walia, Vidushi, Varma, Binuja, Mukherjee, Geetashree

Digital pathology is increasingly being used in the diagnosis and prognosis of tumors. Detection of increased mitosis is a key component of tumor prognostication for various tumors. While manual interpretation is still the most widely used, it suffer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fd763314007da0448c1e29cb9f0349c9

Text and Network-Mining for COVID-19 Intervention Studies

Autor: Sivadasan Naveen, Joseph Thomas, Saipradeep Vg, Srinivasan Rajgopal, Kotte Sujatha, Rao Aditya

Background: The COVID-19 pandemic has led to a massive and collective pursuit by the research community to find effective diagnostics, drugs and vaccines The large and growing body of literature present in MEDLINE and other online resources including

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f28214f797327964794ac6ba016beff8
https://doi.org/10.26434/chemrxiv.12234564.v1

Additional file 2 of Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Autor: Rao, Aditya, Saipradeep VG, Joseph, Thomas, Sujatha Kotte, Sivadasan, Naveen, Rajgopal Srinivasan

Experimental Results in tabular format. (PDF 66 kb)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ef71225ec64760beeb65fccc819dc72

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.

Autor: Aubreville M; Technische Hochschule Ingolstadt, Ingolstadt, Germany. Electronic address: marc@deepmicroscopy.org., Stathonikos N; Pathology Department, UMC Utrecht, The Netherlands., Donovan TA; Department of Anatomic Pathology, The Schwarzman Animal Medical Center, NY, USA., Klopfleisch R; Institute of Veterinary Pathology, Freie Universität Berlin, Berlin, Germany., Ammeling J; Technische Hochschule Ingolstadt, Ingolstadt, Germany., Ganz J; Technische Hochschule Ingolstadt, Ingolstadt, Germany., Wilm F; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Veta M; Computational Pathology Group, Radboud UMC Nijmegen, The Netherlands., Jabari S; Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Eckstein M; Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nünberg, Erlangen, Germany., Annuscheit J; University of Applied Sciences (HTW) Berlin, Berlin, Germany., Krumnow C; University of Applied Sciences (HTW) Berlin, Berlin, Germany., Bozaba E; Artificial Intelligence Research Team, Virasoft Corporation, NY, USA., Çayır S; Artificial Intelligence Research Team, Virasoft Corporation, NY, USA., Gu H; University of California, Los Angeles, USA., Chen X'; University of California, Los Angeles, USA., Jahanifar M; University of Warwick, United Kingdom., Shephard A; University of Warwick, United Kingdom., Kondo S; Muroran Institute of Technology, Muroran, Japan., Kasai S; Niigata University of Health and Welfare, Niigata, Japan., Kotte S; TCS Research, Tata Consultancy Services Ltd, Hyderabad, India., Saipradeep VG; TCS Research, Tata Consultancy Services Ltd, Hyderabad, India., Lafarge MW; Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Koelzer VH; Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Wang Z; Harbin Institute of Technology, Shenzhen, China., Zhang Y; Harbin Institute of Technology, Shenzhen, China., Yang S; College of Biomedical Engineering, Sichuan University, Chengdu, China., Wang X; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, USA., Breininger K; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Bertram CA; Institute of Pathology, University of Veterinary Medicine, Vienna, Austria.

Publikováno v: Medical image analysis [Med Image Anal] 2024 May; Vol. 94, pp. 103155. Date of Electronic Publication: 2024 Mar 22.