Results 161 to 170 of about 1,014,031 (308)

Tumor B‐cell infiltration in platinum‐treated advanced muscle‐invasive urothelial carcinoma

Molecular Oncology, EarlyView.
Bladder tumors with higher pretreatment memory B‐cell infiltration were linked to longer survival after cisplatin chemotherapy, but not carboplatin. These tumors also showed more organized immune structures (tertiary lymphoid structures) and a shared pro‐inflammatory B‐cell‐rich community, suggesting that memory B cells may help identify patients most ...
Konrad Stawiski, Júlia Perera‐Bel, Alejo Rodriguez‐Vida, Nuria Juanpere, Jihyun Lee, Daniel E. Michaud, Jennifer L. Guerriero, Kent W. Mouw, Aristotelis Bamias, Filipe L. F. Carvalho, Joaquim Bellmunt   +10 more
wiley   +1 more source

Classification of Brain Tissues Using Multiwavelet Transformation and Probabilistic Neural Network

, 2006
: This paper describes a new approach for the classification of brain tissues into White Matter, Gray Matter, Cerebral Spinal Fluid, Glial Matter, Connective and MS lesion in multiple sclerosis.
Selvan: Classification Of Ramakrishnan, Brain Tissues I   +1 more
core  

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Text Classification in Information Retrieval using Winnow

Text classification in Information Retrieval can be done by using a linear classifier. Linear learning algorithms classify documents by learning a linear separator based on the document features.
P Mun, Winnow Text Classification
core  

Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer. [PDF]

Sci Rep
Mallika C, Dinesh E, Alsolai H, Arasi MA.   +3 more
europepmc   +1 more source

Patient therapy outcome modeling in cancer organoids is improved by cancer‐associated fibroblasts and organoid assembly convolution

Molecular Oncology, EarlyView.
Patient‐derived organoids (PDOs) from pancreatic, colorectal, and gastric cancers were used to evaluate standard and experimental therapies. Incorporating cancer‐associated fibroblasts (CAFs) into organoid cultures improved patient therapy outcome prediction.
Marcin Grochowski, Liudmyla Dolinchuk, Michał Jerzak, Albert Gandurski, Tomasz Grochowski, Weronika Wojtyś, Maciej Zadrożny, Wojciech Kaźmierczak, Małgorzata Lenarcik, Marta Matejak‐Górska, Radosław Samsel, Tomasz Olesiński, Dawid Walerych   +12 more
wiley   +1 more source

Data cleaning for classification using misclassification analysis

, 2009
In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data.
Wong, K.W., Fung, C.C., Jeatrakul, P.
core  

Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement. [PDF]

Sensors (Basel)
Chaitan C, Tengjongdee S, Pechprasarn S, Thadson K.   +3 more
europepmc   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source

Evaluating AI in leukocyte classification: performance of the AI system against 15 morphology experts. [PDF]

NPJ Digit Med
Xu W, Jiang H, Zhang J, Chen K, Fei Y, Guo P, He Y, Ye Y, Qu L, Yang M, Yan L, Wang D, Qi H, Zhang S, Zhang C, Wang J, Guan M.   +16 more
europepmc   +1 more source