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Analyzing Microarray Gene Expression Data
Geoffrey J. McLachlan,Kim Anh Do,Christophe Ambroise +2 more
- 04 Aug 2004
875
TL;DR: In this article, the authors proposed a supervised classification of Tissue Samples and linked the supervised classification with survival analysis, and showed that the classification of tissue samples is more accurate than that of microarray data.
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Abstract: Preface. 1. Microarrays in Gene Expression Studies. 2. Cleaning and Normalization. 3. Some Cluster Analysis Methods. 4. Clustering of Tissue Samples. 5. Screening and Clustering of Genes. 6. Discriminant Analysis. 7. Supervised Classification of Tissue Samples. 8. Linking Microarray Data with Survival Analysis. References. Author Index. Subject Index.
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Citations
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Statistical analysis of genomic data : a new model for class prediction and inference
Zhenyu Jiang
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TL;DR: New statistical methodologies for the analysis of schizophrenia genomic data from the WA Genetic Epidemiology Resource are developed with the goal of contributing to the understanding of complex human diseases or traits such as mental health.
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Location-Based Species Recommendation - GeoLifeCLEF 2019 Challenge.
Costel-Sergiu Atodiresei,Adrian Iftene +1 more
- 01 Jan 2019
TL;DR: A system built with the aim to predict plant species given their location in the context of the GeoLifeCLEF 2019 challenge is presented.
Optimally weighted maximum a posteriori probabilities based on minimum classification error for dual-microphone voice activity detection
Seng Hyun Huang,Joon-Hyuk Chang +1 more
TL;DR: In this article, a dual-microphone voice activity detection (VAD) technique is proposed by applying discriminative weight training to achieve optimal weighting of spatial features available within the dualmicrophone VAD.
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Effective CAD Research in the Sea of Papers
Jinglan Liu,Da-Cheng Juan,Yiyu Shi +2 more
- 02 Nov 2015
TL;DR: This paper demonstrates a novel deep learning based framework that can automatically search for papers related to a given abstract of research, and suggests how they are correlated, and provides the analysis and comparison among several classic machine-learning approaches.
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A cluster analysis of European life in recovery data: what are the typical patterns of recovery experience?
David Best,Arun Sondhi,David Patton,Valeria Abreu,Thomas Martinelli,Lore Bellaert,Wouter Vanderplasschen,Mulka Nisic +7 more
- 05 Feb 2024
TL;DR: This study applies cluster analysis to European life in recovery data, identifying typical patterns of recovery experience influenced by individual and situational factors, addressing a knowledge gap in recovery pathways and their temporal dynamics.
2