Kernel learning
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Part 1 of ebook "Statistical learning from a regression perspective (Second edition)" provides readers with contents including: Chapter 1 - Statistical learning as a regression problem; Chapter 2 - Splines, smoothers, and kernels;...
149p daonhiennhien 03-07-2024 2 1 Download
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Part 1 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Overview of supervised learning; Chapter 3 - Linear methods for regression; Chapter 4 - Linear methods for classification; Chapter 5 - Basis expansions and regularization; Chapter 6 - Kernel smoothing methods; Chapter 7 - Model assessment and selection; Chapter 8 - Model inference and averaging; Chapter 9 - Additive models, trees, and related methods;...
355p daonhiennhien 03-07-2024 2 1 Download
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Part 2 of book "Introduction to machine learning" provide with knowledge about: optimization; unconstrained smooth convex minimization; online learning and boosting; conditional densities; kernels and function spaces; linear models;...
136p britaikridanik 05-07-2022 32 4 Download
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Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants).
9p thebadguys 15-01-2022 11 0 Download
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In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data. To realize this, we employ a score function based on cross-validation.
19p guernsey 28-12-2021 14 0 Download
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Nghiên cứu này đề xuất một phương pháp phân loại ảnh viễn thám siêu phổ (AVTSP). Chúng tôi sử dụng một khung mạng nơ-ron tích chập mới để trích xuất các đặc điểm cục bộ của ảnh viễn thám siêu phổ, và sau đó sử dụng một thuật toán máy học hạt nhân cấp tốc (kernel extreme learning machine, KELM) để phân loại các đối tượng khác nhau.
4p visergeybrin 25-11-2021 26 4 Download
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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 Download
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In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations.
15p viseulgi2711 31-08-2021 6 1 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information.
12p viconnecticut2711 28-10-2020 11 0 Download
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The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge.
14p vijisoo2711 27-10-2020 6 1 Download
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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 Download
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In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.
13p vicolorado2711 23-10-2020 11 0 Download
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Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods.
10p vicolorado2711 23-10-2020 9 1 Download