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1  the applications of biomedical research and knowledge discovery.
2 hine learning, knowledge representation, and knowledge discovery.
3 obust and automated method for miRNA related knowledge discovery.
4 ntial of the proposed approach to biomedical knowledge discovery.
5 edical entities from texts into networks for knowledge discovery.
6 ing network data from external databases for knowledge discovery.
7 mbedded in scientific literature for further knowledge discovery.
8  protocols in both prediction and biological knowledge discovery.
9 sign, data normalization and true biological knowledge discovery.
10 ne tools for gene similarity measurement and knowledge discovery.
11 t for facilitating hypothesis generation and knowledge discovery.
12 abases to support their complex analysis and knowledge discovery.
13  insights into gene functions and expediting knowledge discovery.
14 tween genetic data generation and biomedical knowledge discovery.
15 rmatics analyses in terms of data mining and knowledge discovery.
16 ghlighting the power of Bayesian networks in knowledge discovery.
17 any hypotheses simultaneously can facilitate knowledge discovery.
18 ble biomedical data can significantly impact knowledge discovery.
19 odel-free function inference for data-driven knowledge discovery.
20 netic disease analysis, and literature-based knowledge discovery.
21 vigation of the data manifold and meaningful knowledge discovery.
22                      "Full feature spectrum" knowledge discovery across heterogeneous data sources re
23 g and statistical inference have overlapping knowledge discovery aims and approaches.
24 graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypoth
25 cer could greatly support literature review, knowledge discovery and applications in cancer research.
26  tailoring HMM topologies to data for use in knowledge discovery and clustering.
27 enge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup.
28 logy would allow the development of advanced knowledge discovery and data mining tools for across com
29 pted in biomedical research and practice for knowledge discovery and decision support.
30 hop was 'Roles for text mining in biomedical knowledge discovery and translational medicine'.
31  and in the real world context of biomedical knowledge discovery applicability.
32                                  Data-driven knowledge discovery approaches can potentially unveil hi
33                     Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupe
34 ilarity searches, information retrieval, and knowledge discovery by providing the Protein Sequence Da
35 fore feature extraction, data reduction, and knowledge discovery can ensue.
36      The lack of full genome coverage limits knowledge discovery for half of the human protein coding
37 hich will be seamlessly integrated forming a knowledge discovery framework.
38 monly used inductive programming methods for knowledge discovery from data assume that the elements o
39 h shows great potential for novel biomedical knowledge discovery from deep learning models.
40  sonification algorithms that aim to improve knowledge discovery from protein sequences and small pro
41                   Text mining systems aim at knowledge discovery from text collections.
42 ioinformatics toolkits that will lead to new knowledge discovery from their data.
43 isual analytic platform that will facilitate knowledge discoveries in future network and systems biol
44 e interaction to facilitate novel biological knowledge discoveries in modern plant genomics.
45                   We developed and applied a Knowledge Discovery in Databases procedure to analyse me
46               GPS is particularly useful for knowledge discovery in environments with reduced dataset
47 arkers and genes, and it greatly facilitates knowledge discovery in genome-wide SNP scanning experime
48 g evidence-driven decisions and accelerating knowledge discovery in life sciences.
49 are critical for representation learning and knowledge discovery in real world biomedical problems.
50  mining can provide an efficient pathway for knowledge discovery in the next generation of materials
51                      Enabling data reuse and knowledge discovery is increasingly critical in modern s
52 tifying by transplant center and using novel knowledge discovery methods.
53 experimental findings here represent, to our knowledge, discovery of a previously undescribed set of
54    How to design experiments that accelerate knowledge discovery on complex biological landscapes rem
55   While essential to create a foundation for knowledge discovery, optimized solutions to deliver high
56                                 However, the knowledge discovery process from these heterogeneous dat
57 sets required for the data analysis in their knowledge discovery process.
58 based phenotypes and integrate them into the knowledge discovery process.
59 arious blockade modes for classification and knowledge discovery purposes.
60 nce, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes a
61 of the TM, we made the important new (to our knowledge) discovery that the stiffness of the TM is red
62 ent a machine learning framework to automate knowledge discovery through knowledge graph construction
63  progress made in the field of computational knowledge discovery to present a reconstructive simulati
64 e changes to automate the TO-CO mappings and knowledge discovery tools ensure that the Planteome will
65 ed for innovative information management and knowledge discovery tools to sift through these vast vol
66 riven decisions are a step toward automating knowledge discovery with high confidence and accelerated
67 ying databases for information retrieval and knowledge discovery, with functionalities for interactiv