ライフサイエンスコーパス: semi-supervised

1 eled data during training, making our method semi-supervised.

2 d in the gene selection step, this method is semi-supervised.

3 We also used a semi-supervised algorithm that leverages unlabeled data

4 challenges in the application of supervised, semi-supervised and unsupervised machine learning method

5 jugates an efficient mapping solution with a semi-supervised anomaly detection scheme to filter out f

6 eveloped ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genoty

7 t generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts auto

8 The semi-supervised auxiliary task shares network layers of

9 ng on a supervised classification task and a semi-supervised auxiliary task.

10 al cancer patients, we demonstrated that (i) semi-supervised classification improved prediction accur

11 In total, 11 different supervised and semi-supervised classifiers were trained and assessed re

12 ciparum life cycle microarray data using the semi-supervised clustering algorithm Ontology-based Patt

13 TFCC is a semi-supervised clustering algorithm which relies on the

14 ompeting semi-supervised methods, including: semi-supervised clustering and supervised principal comp

15 Unlike other semi-supervised clustering classification methods, SS-RP

16 Semi-supervised clustering of discovery (n=168) and vali

17 Semi-supervised clustering, based on KRAS(G12D) mutant e

18 ic organisms using Markov Random Fields in a semi-supervised fashion.

19 We introduce a new graph-based semi-supervised feature classification algorithm to iden

20 It leverages supervised and semi-supervised feature-based classifiers, including our

21 ne (SVM)-based tool to detect homology using semi-supervised iterative learning (SVM-HUSTLE) that ide

22 Our study innovates a semi-supervised iterative pattern learning approach that

23 In this study we introduce GLAD, a new Semi-Supervised Learning (SSL) method for combining inde

24 We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to

25 The method employs a semi-supervised learning algorithm that discovers natura

26 nks target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene ex

27 Both our methods are based on semi-supervised learning and involve augmenting the limi

28 (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying struct

29 es or to form their own new class, we take a semi-supervised learning approach; for high-dimensional

30 ion can be successfully implemented within a semi-supervised learning framework that exploits the int

31 Our results demonstrated great potential of semi-supervised learning in gene expression-based outcom

32 We present a Bayesian semi-supervised learning method, called BGEN, that impro

33 Unlike currently available semi-supervised learning methods, this new method trains

34 ous data in public databases, we turned to a semi-supervised learning technique, low density separati

35 diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their acc

36 SVM-HUSTLE combines principles of semi-supervised learning theory with statistical samplin

37 ntegration algorithms, including graph-based semi-supervised learning, graph sharpening integration,

38 In this study, we apply state-of-the-art semi-supervised machine learning methods to the Alzheime

39 Percolator uses semi-supervised machine learning to discriminate between

40 ctions of TF-miRNA interactions, we extended semi-supervised machine-learning approaches to integrate

41 To test the general applicability of this semi-supervised method, we further applied LDS on human

42 thod compared favorably with other competing semi-supervised methods, including: semi-supervised clus

43 resent out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensiona

44 icipants were randomized to either a 6-month semi-supervised moderate exercise protocol (EX, n = 66)

45 We propose a semi-supervised multi-task framework for predicting PPIs

46 hod for this task indicating the benefits of semi-supervised multi-task learning using auxiliary info

47 We develop semi-supervised normalization pipelines and perform expe

48 Semi-supervised pattern recognition has been proposed to

49 e (AL), alone or in combination with 14 d of semi-supervised primaquine (PQ) (3.5 mg/kg total).

50 Experiments demonstrate that the new semi-supervised protocol can result in improved accuracy

51 We developed a new semi-supervised protocol that can use unlabeled cancer p

52 We propose a method called semi-supervised recursively partitioned mixture models (

53 The semi-supervised trained classifier can then be used to e