Chitrita Goswami

DuraLoop: Reducing Rework, Cost, and Customer-Visible Errors in AI Coding Agents. A Production Case Study and Operational Framework for Reducing Rework in AI Coding-agent Fleets

Abhishek Sehgal, Chitrita Goswami

Author response: Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

au, in, fr (code pays fourni par la source)

Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of …

in, au, gb, us, it, fr, ca (code pays fourni par la source)

Author response: SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of …

au, gb, in, us, it, fr (code pays fourni par la source)

SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Abstract Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent …

in, au, gb, us, it, fr, ca (code pays fourni par la source)

Author response: Pseudo-grading of tumor subpopulations from single-cell transcriptomic data using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of …

au, gb, in, us, it, fr (code pays fourni par la source)

Pseudo-grading of tumor subpopulations from single-cell transcriptomic data using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Abstract Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent …

in, au, gb, us, it, fr, ca (code pays fourni par la source)

Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of …

in, au, gb, us, it, fr, ca (code pays fourni par la source)

InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniques

Chitrita Goswami, N Bhattacharya, Debarka Sengupta

Abstract We introduce InGene , the first of its kind, fast and scalable non-linear, unsupervised method for analyzing single-cell RNA sequencing data (scRNA-seq). While non-linear dimensionality reduction techniques such as t-SNE and UMAP are effective at visualizing cellular sub-populations in low-dimensional space, …

in, us, gb, au (code pays fourni par la source)

SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas et autres

ABSTRACT Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent …

in, au, gb, us, it, fr, ca (code pays fourni par la source)

Correction to: Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC

Chitrita Goswami, Smriti Chawla, Deepshi Thakral, Himanshu Pant et autres

An amendment to this paper has been published and can be accessed via the original article.

in, au (code pays fourni par la source)

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC

Chitrita Goswami, Smriti Chawla, Deepshi Thakral, Himanshu Pant et autres

BACKGROUND: Early diagnosis is crucial for effective medical management of cancer patients. Tissue biopsy has been widely used for cancer diagnosis, but its invasive nature limits its application, especially when repeated biopsies are needed. Over the past few years, genomic explorations have …

in, au (code pays fourni par la source)