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Multiple kernel learning
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Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases.
16p
vitzuyu2711
29-09-2021
9
1
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Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency.
14p
vilichoo2711
25-06-2021
11
1
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Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods.
16p
vioklahoma2711
19-11-2020
9
2
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Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data.
13p
vioklahoma2711
19-11-2020
13
0
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Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information.
11p
vioklahoma2711
19-11-2020
9
1
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Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies.
18p
viflorida2711
30-10-2020
13
3
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High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources.
13p
viconnecticut2711
29-10-2020
12
1
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Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems.
18p
viconnecticut2711
28-10-2020
22
0
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Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources.
7p
vicolorado2711
23-10-2020
6
1
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These results prove our proposed methods are effective and feasible for activity recognition system in the daily environment.
12p
everydaywish
24-12-2018
20
1
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(bq) part 2 book "introduction to machine learning" has contents: linear discrimination, multilayer perceptrons, local models, kernel machines, hidden markov models, graphical models, combining multiple learners, combining multiple learners, design and analysis of machine learning experiments.
331p
bautroibinhyen20
06-03-2017
64
7
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Network security spans a large number of disciplines, ranging from management and policy topics to operating system kernel fundamentals. Historically, the coverage of these and the other network security areas was presented in multiple, specialized publications or given a high-level treatment that was not suited to the practitioner. Network Security Bible approaches network security from the view of the individual who wants to learn and apply the associated network security best practices without having to sort through a myriad of extraneous material from multiple sources.
697p
vinhnghi
17-08-2009
156
34
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