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of 10
pro vyhledávání: '"Pavel, P Kuksa"'
Autor:
Pavel P. Kuksa, Li-San Wang, Sampath Kannan, Lyle H. Ungar, Alexandre Amlie-Wolf, Otto Valladares, Brian D. Gregory, Yuk Yee Leung
Publikováno v:
Nucleic Acids Research
Small non-coding RNAs (sncRNAs) are highly abundant RNAs, typically
Publikováno v:
Bioinformatics. 31:3600-3607
Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between diff
Autor:
Pavel P. Kuksa, Chiao-Feng Lin, John Malamon, Yih-Chii Hwang, Brian D. Gregory, Qi Zheng, Li-San Wang, Otto Valladares
Publikováno v:
Bioinformatics. 31:1290-1292
Summary: We implemented a high-throughput identification pipeline for promoter interacting enhancer element to streamline the workflow from mapping raw Hi-C reads, identifying DNA–DNA interacting fragments with high confidence and quality control,
Autor:
Pavel P. Kuksa
Publikováno v:
IEEE/ACM transactions on computational biology and bioinformatics. 10(5)
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequenc
Autor:
Jason Weston, Pavel P. Kuksa, Bing Bai, Yanjun Qi, Xia Ning, Ronan Collobert, Vladimir Pavlovic
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783642158827
ECML/PKDD (2)
ECML/PKDD (2)
Bio-relation extraction (bRE), an important goal in bio-text mining, involves subtasks identifying relationships between bio-entities in text at multiple levels, e.g., at the article, sentence or relation level. A key limitation of current bRE system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ca7a927c66063428c90714f900d268d
https://doi.org/10.1007/978-3-642-15883-4_9
https://doi.org/10.1007/978-3-642-15883-4_9
Autor:
Koray Kavukcuoglu, Pavel P. Kuksa, Ronan Collobert, Yanjun Qi, Jason Weston, Kunihiko Sadamasa
Publikováno v:
ICDM
Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of labeled words. To tackle this issue, we propose a semi-supervised approach
Publikováno v:
CIKM
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it task of information extraction (IE) by utilizing unlabeled sentences to improve supervised classification methods. WCDL
Autor:
Pavel P. Kuksa, Vladimir Pavlovic
Publikováno v:
BIBM
We consider the problem of identifying motifs, recurring or conserved patterns, in the sets of biological sequences. To solve this task, we present new deterministic and exact algorithms for finding patterns that are embedded as exact or inexact inst
Publikováno v:
BMC Bioinformatics
Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies,
Publikováno v:
BIBM
Recent studies in protein sequence analysis have leveraged the power of unlabeled data. For example, the profile and mismatch neighborhood kernels have shown significant improvements over classifiers estimated under the fully supervised setting. In t