Data mining for systems biology: methods and protocols
This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowle...
Gespeichert in:
Weitere Verfasser: | |
---|---|
Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
New York, NY
Springer New York
2018
|
Ausgabe: | 2nd ed. 2018 |
Schriftenreihe: | Methods in Molecular Biology
1807 |
Schlagworte: | |
Online-Zugang: | UBR01 TUM01 Volltext |
Zusammenfassung: | This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency |
Beschreibung: | Identifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG. |
Beschreibung: | 1 Online-Ressource (XI, 243 Seiten) Illustrationen |
ISBN: | 9781493985616 |
DOI: | 10.1007/978-1-4939-8561-6 |
Internformat
MARC
LEADER | 00000nmm a2200000zc 4500 | ||
---|---|---|---|
001 | BV047617317 | ||
003 | DE-604 | ||
005 | 00000000000000.0 | ||
007 | cr|uuu---uuuuu | ||
008 | 211130s2018 |||| o||u| ||||||eng d | ||
020 | |a 9781493985616 |c Online |9 978-1-4939-8561-6 | ||
024 | 7 | |a 10.1007/978-1-4939-8561-6 |2 doi | |
035 | |a (ZDB-2-PRO)978-1-4939-8561-6 | ||
035 | |a (OCoLC)1286876400 | ||
035 | |a (DE-599)BVBBV047617317 | ||
040 | |a DE-604 |b ger |e rda | ||
041 | 0 | |a eng | |
049 | |a DE-355 |a DE-91 | ||
082 | 0 | |a 570.285 | |
084 | |a WD 9000 |0 (DE-625)148252: |2 rvk | ||
084 | |a WC 7700 |0 (DE-625)148144: |2 rvk | ||
245 | 1 | 0 | |a Data mining for systems biology |b methods and protocols |c edited by Hiroshi Mamitsuka |
250 | |a 2nd ed. 2018 | ||
264 | 1 | |a New York, NY |b Springer New York |c 2018 | |
300 | |a 1 Online-Ressource (XI, 243 Seiten) |b Illustrationen | ||
336 | |b txt |2 rdacontent | ||
337 | |b c |2 rdamedia | ||
338 | |b cr |2 rdacarrier | ||
490 | 0 | |a Methods in Molecular Biology | |
490 | 0 | |a 1807 | |
500 | |a Identifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG. | ||
520 | |a This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency | ||
650 | 4 | |a Bioinformatics | |
650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Systembiologie |0 (DE-588)4809615-5 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Bioinformatik |0 (DE-588)4611085-9 |2 gnd |9 rswk-swf |
655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
689 | 0 | 0 | |a Bioinformatik |0 (DE-588)4611085-9 |D s |
689 | 0 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
689 | 0 | 2 | |a Systembiologie |0 (DE-588)4809615-5 |D s |
689 | 0 | |5 DE-604 | |
700 | 1 | |a Mamitsuka, Hiroshi |d ca. 20./21. Jh. |0 (DE-588)116454473X |4 edt | |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-4939-8560-9 |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-4939-8562-3 |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-4939-9326-0 |
856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4939-8561-6 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
912 | |a ZDB-2-PRO | ||
999 | |a oai:aleph.bib-bvb.de:BVB01-033002012 | ||
966 | e | |u https://doi.org/10.1007/978-1-4939-8561-6 |l UBR01 |p ZDB-2-PRO |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1007/978-1-4939-8561-6 |l TUM01 |p ZDB-2-PRO |x Verlag |3 Volltext |
Datensatz im Suchindex
_version_ | 1804183052312641536 |
---|---|
adam_txt | |
any_adam_object | |
any_adam_object_boolean | |
author2 | Mamitsuka, Hiroshi ca. 20./21. Jh |
author2_role | edt |
author2_variant | h m hm |
author_GND | (DE-588)116454473X |
author_facet | Mamitsuka, Hiroshi ca. 20./21. Jh |
building | Verbundindex |
bvnumber | BV047617317 |
classification_rvk | WD 9000 WC 7700 |
collection | ZDB-2-PRO |
ctrlnum | (ZDB-2-PRO)978-1-4939-8561-6 (OCoLC)1286876400 (DE-599)BVBBV047617317 |
dewey-full | 570.285 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 570 - Biology |
dewey-raw | 570.285 |
dewey-search | 570.285 |
dewey-sort | 3570.285 |
dewey-tens | 570 - Biology |
discipline | Biologie |
discipline_str_mv | Biologie |
doi_str_mv | 10.1007/978-1-4939-8561-6 |
edition | 2nd ed. 2018 |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04689nmm a2200565zc 4500</leader><controlfield tag="001">BV047617317</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">211130s2018 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781493985616</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4939-8561-6</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4939-8561-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-PRO)978-1-4939-8561-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1286876400</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047617317</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-355</subfield><subfield code="a">DE-91</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">570.285</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WD 9000</subfield><subfield code="0">(DE-625)148252:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WC 7700</subfield><subfield code="0">(DE-625)148144:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data mining for systems biology</subfield><subfield code="b">methods and protocols</subfield><subfield code="c">edited by Hiroshi Mamitsuka</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">2nd ed. 2018</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer New York</subfield><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XI, 243 Seiten)</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Methods in Molecular Biology</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">1807</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Identifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bioinformatics</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Systembiologie</subfield><subfield code="0">(DE-588)4809615-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bioinformatik</subfield><subfield code="0">(DE-588)4611085-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Bioinformatik</subfield><subfield code="0">(DE-588)4611085-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Systembiologie</subfield><subfield code="0">(DE-588)4809615-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mamitsuka, Hiroshi</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="0">(DE-588)116454473X</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-4939-8560-9</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-4939-8562-3</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-4939-9326-0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4939-8561-6</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-PRO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033002012</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-1-4939-8561-6</subfield><subfield code="l">UBR01</subfield><subfield code="p">ZDB-2-PRO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-1-4939-8561-6</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-2-PRO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
genre_facet | Aufsatzsammlung |
id | DE-604.BV047617317 |
illustrated | Not Illustrated |
index_date | 2024-07-03T18:42:06Z |
indexdate | 2024-07-10T09:17:19Z |
institution | BVB |
isbn | 9781493985616 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033002012 |
oclc_num | 1286876400 |
open_access_boolean | |
owner | DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
owner_facet | DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
physical | 1 Online-Ressource (XI, 243 Seiten) Illustrationen |
psigel | ZDB-2-PRO |
publishDate | 2018 |
publishDateSearch | 2018 |
publishDateSort | 2018 |
publisher | Springer New York |
record_format | marc |
series2 | Methods in Molecular Biology 1807 |
spelling | Data mining for systems biology methods and protocols edited by Hiroshi Mamitsuka 2nd ed. 2018 New York, NY Springer New York 2018 1 Online-Ressource (XI, 243 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Methods in Molecular Biology 1807 Identifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG. This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency Bioinformatics Data Mining (DE-588)4428654-5 gnd rswk-swf Systembiologie (DE-588)4809615-5 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Bioinformatik (DE-588)4611085-9 s Data Mining (DE-588)4428654-5 s Systembiologie (DE-588)4809615-5 s DE-604 Mamitsuka, Hiroshi ca. 20./21. Jh. (DE-588)116454473X edt Erscheint auch als Druck-Ausgabe 978-1-4939-8560-9 Erscheint auch als Druck-Ausgabe 978-1-4939-8562-3 Erscheint auch als Druck-Ausgabe 978-1-4939-9326-0 https://doi.org/10.1007/978-1-4939-8561-6 Verlag URL des Erstveröffentlichers Volltext |
spellingShingle | Data mining for systems biology methods and protocols Bioinformatics Data Mining (DE-588)4428654-5 gnd Systembiologie (DE-588)4809615-5 gnd Bioinformatik (DE-588)4611085-9 gnd |
subject_GND | (DE-588)4428654-5 (DE-588)4809615-5 (DE-588)4611085-9 (DE-588)4143413-4 |
title | Data mining for systems biology methods and protocols |
title_auth | Data mining for systems biology methods and protocols |
title_exact_search | Data mining for systems biology methods and protocols |
title_exact_search_txtP | Data mining for systems biology methods and protocols |
title_full | Data mining for systems biology methods and protocols edited by Hiroshi Mamitsuka |
title_fullStr | Data mining for systems biology methods and protocols edited by Hiroshi Mamitsuka |
title_full_unstemmed | Data mining for systems biology methods and protocols edited by Hiroshi Mamitsuka |
title_short | Data mining for systems biology |
title_sort | data mining for systems biology methods and protocols |
title_sub | methods and protocols |
topic | Bioinformatics Data Mining (DE-588)4428654-5 gnd Systembiologie (DE-588)4809615-5 gnd Bioinformatik (DE-588)4611085-9 gnd |
topic_facet | Bioinformatics Data Mining Systembiologie Bioinformatik Aufsatzsammlung |
url | https://doi.org/10.1007/978-1-4939-8561-6 |
work_keys_str_mv | AT mamitsukahiroshi dataminingforsystemsbiologymethodsandprotocols |