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Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

Injy Sarhan, Marco Spruit
2020 Applied Sciences  
When transferring to the OIE bio-medical domain, we achieved an F-measure of 78.0%, only 1% lower when compared to traditional learning.  ...  Hereby, our analysis shows that OIE can act as a reliable source task.  ...  Semi-supervised approaches mainly depend on bootstrapping techniques. Distant supervision techniques merge both semi-supervised and unsupervised approaches.  ... 
doi:10.3390/app10175758 fatcat:cylkvqnzsrcxlf2nxs2k5gwozq

AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

Mridul Sharma
2021 International journal of research in medical sciences and technology  
These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment.  ...  The output in this research is based on the State-of-the-art technique.  ...  These Semi-Supervised contains missing targets, and transduction has issue cases that are blown easy, aside from a portion of the objectives being removed.  ... 
doi:10.37648/ijrmst.v11i02.006 fatcat:olmnuhxkpbayflapc4jgsflpfi

Patient-Specific Semi-supervised Learning for Postoperative Brain Tumor Segmentation [chapter]

Raphael Meier, Stefan Bauer, Johannes Slotboom, Roland Wiest, Mauricio Reyes
2014 Lecture Notes in Computer Science  
The idea behind our semi-supervised approach is to effectively fuse information from both pre-and postoperative image data of the same patient to improve segmentation of the postoperative image.  ...  We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest.  ...  A thorough introduction into decision forests in the context of computer vision and medical image analysis can be found in the book of Criminisi et al. [13] .  ... 
doi:10.1007/978-3-319-10404-1_89 fatcat:3u3th5fcj5bjldtv66hxv56qau

Novel Semi-Replicative Retroviral Vector Mediated Double Suicide Gene Transfer Enhances Antitumor Effects in Patient-Derived Glioblastoma Models

Lee, Kim, Lee, Kang, Shin, Oh, Koo, Kim, Kim, Kong, Nam, Lee
2019 Cancers  
Flow cytometry and high-content analysis revealed a wide range of transduction efficiency and good correlation between the delivery of therapeutic genes and susceptibility to the prodrugs ganciclovir and  ...  In this study, we constructed a semi-and pseudotyped-RRV (sp-RRV) system harboring two suicide genes—herpes simplex virus type 1 thymidine kinase (TK) and yeast cytosine deaminase (CD)—to verify the dissemination  ...  Acknowledgments: The biospecimens for this study were provided by the Samsung Medical Center BioBank. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers11081090 pmid:31370279 pmcid:PMC6721803 fatcat:43ik4qa6yrajxc5gvbs2qnavbq

EPHA2, EPHA4, and EPHA7 Expression in Triple-Negative Breast Cancer

Ilias Nikas, Constantinos Giaginis, Kalliopi Petrouska, Paraskevi Alexandrou, Artemis Michail, Panagiotis Sarantis, Gerasimos Tsourouflis, Eugene Danas, Alexandros Pergaris, Panagiotis K. Politis, Lydia Nakopoulou, Stamatios Theocharis
2022 Diagnostics  
data (overall survival (OS); disease-free survival (DFS)).  ...  Given the limited treatment options and poorer outcome that accompany the TNBC subtype, EPHA2 could also pose as a target for novel, more personalized, and effective therapeutic approaches for those patients  ...  Likewise, multivariate analysis identified Ki-67 status as an independent prognostic factor for disease-free but not for overall survival (Tables 4 and 5 Cox-regression analysis; p = 0.0481 and p = 0.0808  ... 
doi:10.3390/diagnostics12020366 pmid:35204461 pmcid:PMC8871500 fatcat:2t47fwaotjevnfoxed3xwlzyz4

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

Elaheh Moradi, Antonietta Pepe, Christian Gaser, Heikki Huttunen, Jussi Tohka
2015 NeuroImage  
First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm  ...  The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed  ...  ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the  ... 
doi:10.1016/j.neuroimage.2014.10.002 pmid:25312773 pmcid:PMC5957071 fatcat:ypkm4hxu6bgv7mqix7sshli2te

Self-supervised driven consistency training for annotation efficient histopathology image analysis [article]

Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen, Anne L. Martel
2021 arXiv   pre-print
Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks.  ...  Under limited-label data, the proposed method yields tangible improvements, which is close or even outperforming other state-of-the-art self-supervised and supervised baselines.  ...  Acknowledgment This work was funded by Canadian Cancer Society and Canadian Institutes of Health Research (CIHR).  ... 
arXiv:2102.03897v3 fatcat:gydyinplx5gttgziblpdmi6vnm

Total Lesion Glycolysis Estimated by a Radiomics Model From CT Image Alone

Hongwei Si, Xinzhong Hao, Lianyu Zhang, Xiaokai Xu, Jianzhong Cao, Ping Wu, Li Li, Zhifang Wu, Shengyang Zhang, Sijin Li
2021 Frontiers in Oncology  
model of 3 features was trained by the deep learning and linear regression method.  ...  It performed well in all validation cohorts (n = 5), and a linear regression could correct the bias from different scanners.  ...  regression or deep learning analysis.  ... 
doi:10.3389/fonc.2021.664346 pmid:34221979 pmcid:PMC8247448 fatcat:6yksfasgybbuff5wyj6unefdo4

Machine and deep learning methods for radiomics

Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières, Arvind Rao, Olivier Morin, Sarah A. Mattonen, Issam El Naqa
2020 Medical Physics (Lancaster)  
These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches.  ...  The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis  ...  84 Graph-based methods construct a graph connecting similar observations and enable the class information being transported through the graph. 85 or the survival analysis, Cox regression, 86 random  ... 
doi:10.1002/mp.13678 pmid:32418336 pmcid:PMC8965689 fatcat:m2fed2mssvbzzpomhtfkem65ty

Deep Learning Based Pain Treatment

Tarun Jaiswal, Sushma Jaiswal
2019 International Journal of Trend in Scientific Research and Development  
Among machine learning methods, a subset has so far been applied to pain research-related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed  ...  The current review objectives to familiarize pain area professionals with the methods and current applications of machine learning in pain investigation, possibly simplifying the awareness of the methods  ...  As in the semi-supervised scenario, the learner receives a labeled training sample along with a set of unlabeled test points.  ... 
doi:10.31142/ijtsrd23639 fatcat:tqg4u3tkgjhmjpya67g3lnewwu

Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

Nikola Kasabov
2007 Pattern Recognition Letters  
The paper is offering a comparative study of major modeling and pattern discovery approaches applicable to the area of data analysis and decision support systems in general, and to the area of Bioinformatics  ...  Compared are inductive versus transductive reasoning, global, local, and personalised modeling, and all these approaches are illustrated on a case study of gene expression and clinical data related to  ...  The data analysis in the paper was conducted with the use of two software environments -NeuCom (www.theneucom.com, or www.kedri. info/) and SIFTWARE (available from Pacific Edge Biotechnology Ltd.)  ... 
doi:10.1016/j.patrec.2006.08.007 fatcat:atuz6wpuc5dzlj3anx6uwqzzzu

Expression of Spermine Oxidase Is Associated with Colorectal Carcinogenesis and Prognosis of Patients

Sooyoun Kim, Doyeon Kim, Sanghyun Roh, Inpyo Hong, Hyeongjoo Kim, Tae Sung Ahn, Dong Hyun Kang, Moon Soo Lee, Moo-Jun Baek, Hyoung Jong Kwak, Chang-Jin Kim, Dongjun Jeong
2022 Biomedicines  
SMOX overexpression in tumor tissues was an independent prognostic factor, worsening overall survival (p = 0.001).  ...  This study indicated that SMOX overexpression could be an important oncogene in CRC and might serve as a valuable prognostic marker and potential therapeutic target for CRC.  ...  Kaplan-Meier curves and Cox regression tests were used for survival rate analysis, and the log-rank test was used to compare survival rates.  ... 
doi:10.3390/biomedicines10030626 pmid:35327428 pmcid:PMC8944969 fatcat:stl2lavrkbdr7mo6mt3xwxmeum

Radiomics and radiogenomics in gliomas: a contemporary update

Gagandeep Singh, Sunil Manjila, Nicole Sakla, Alan True, Amr H Wardeh, Niha Beig, Anatoliy Vaysberg, John Matthews, Prateek Prasanna, Vadim Spektor
2021 British Journal of Cancer  
The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as  ...  This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics.  ...  The sparse availability of ground truth labels in radiogenomic models can be modelled as a 'weak supervision' or 'incomplete supervision' task.  ... 
doi:10.1038/s41416-021-01387-w pmid:33958734 pmcid:PMC8405677 fatcat:x3b3bmjobfaepbkocurc2v3zeq

Differential expression analysis for sequence count data

Simon Anders, Wolfgang Huber
2010 Genome Biology  
One of the basic statistical tasks is inference (testing, regression) on discrete count values (e.g., representing the number of times a certain type of mRNA was sampled by the sequencing machine).  ...  I will present an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data.  ...  We compare several supervised algorithms to their transductive (or semi-supervised) counterparts in real and artificial settings.  ... 
doi:10.1186/gb-2010-11-10-r106 pmid:20979621 pmcid:PMC3218662 fatcat:ala2sfzzbnfzfgeompuuveneca

Whole-transcriptome analysis links trastuzumab sensitivity of breast tumors to both HER2 dependence and immune cell infiltration

Tiziana Triulzi, Loris De Cecco, Marco Sandri, Aleix Prat, Marta Giussani, Biagio Paolini, Marialuisa L. Carcangiu, Silvana Canevari, Alberto Bottini, Andrea Balsari, Sylvie Menard, Daniele Generali (+3 others)
2015 OncoTarget  
Pathway analysis revealed that TRAR-low tumors expressed genes of the immune response, with higher numbers of CD8-positive cells detected immunohistochemically compared to TRARhigh tumors.  ...  To identify a molecular predictor of trastuzumab benefit, we conducted whole-transcriptome analysis of primary HER2+ breast carcinomas obtained from patients treated with trastuzumab-containing therapies  ...  Using a semi-supervised principal component method, we identified patients with high and low risk of relapse ( Figure 2A ).  ... 
doi:10.18632/oncotarget.4405 pmid:26334217 pmcid:PMC4695052 fatcat:2jxv5fyybbggjlk4sn3dtbi7ha
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