Search Results - anzsrc-for: 3102 ((bioinformatics OR information) OR informatik) AND computational biology

How mandibular and hyoid morphology alters tongue muscle architecture in healthy adults: An anatomical atlas and statistical shape model of the tongue

Authors: Lloyd, RA Dissanayake, E Jugé, L et al.

Source: urn:ISSN:0010-4825 ; urn:ISSN:1879-0534 ; Computers in Biology and Medicine, 189, 110006-110006

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, 4203 Health Services and Systems, 42 Health Sciences, 46 Information and Computing Sciences, 4601 Applied Computing, Biomedical Imaging, Dental/Oral and Craniofacial Disease, Clinical Research, Humans, Tongue, Male, Female, Adult, Hyoid Bone, Mandible, Magnetic Resonance Imaging, Models, Anatomic, Young Adult, Diffusion Magnetic Resonance Imaging, DWI, MRI, Muscle architecture, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 4203 Health Services and Systems, anzsrc-for: 42 Health Sciences, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 4601 Applied Computing

File Description: application/pdf

Relation: https://hdl.handle.net/1959.4/104552; https://doi.org/10.1016/j.compbiomed.2025.110006

Availability: https://hdl.handle.net/1959.4/104552
https://unsworks.unsw.edu.au/bitstreams/c5e53fcc-256f-485d-9ea2-bde0f2ddc61f/download
https://doi.org/10.1016/j.compbiomed.2025.110006

Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study

Authors: Wang, X Zhang, Y Guo, T et al.

Source: urn:ISSN:0010-4825 ; urn:ISSN:1879-0534 ; Computers in Biology and Medicine, 176, 108556-108556

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, 4203 Health Services and Systems, 42 Health Sciences, 46 Information and Computing Sciences, 4601 Applied Computing, Neurosciences, Animals, Nerve Fibers, Myelinated, Unmyelinated, Rats, Electric Stimulation, Nanotubes, Carbon, Models, Neurological, Sciatic Nerve, Electrodes, Computational modeling, Intrafascicular electrical stimulation, Longitudinal electrode, Peripheral nerve, Selective activation, Unmyelinated C fiber, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 4203 Health Services and Systems, anzsrc-for: 42 Health Sciences, anzsrc-for: 46 Information and Computing Sciences

Relation: http://hdl.handle.net/1959.4/102145; https://doi.org/10.1016/j.compbiomed.2024.108556

Availability: http://hdl.handle.net/1959.4/102145
https://doi.org/10.1016/j.compbiomed.2024.108556

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Authors: Duy Pham Xiao Tan Brad Balderson et al.

Source: Nat Commun
Nature Communications, Vol 14, Iss 1, Pp 1-25 (2023)

Subject Terms: Neural Networks, 1.1 Normal biological development and functioning, Science, Gene Expression Profiling, Bioengineering, Cell Communication, Article, 3102 Bioinformatics and Computational Biology, Computer, Networking and Information Technology R&D (NITRD), Genetics, 2.1 Biological and endogenous factors, Neural Networks, Computer, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, Gene Expression Profiling/methods, Transcriptome, Software, Algorithms, 31 Biological Sciences, Cancer

Access URL: https://pubmed.ncbi.nlm.nih.gov/38007580
https://doaj.org/article/90386dc9c14b4450ad2ce44635d342ec

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Authors: Cai, Xinhui Li, Jiao Jiao Liu, Tao et al.

Source: Briefings in Functional Genomics. 20:289-303

Subject Terms: 0301 basic medicine, Coronaviruses, Messenger, Coronaviruses Vaccines, Machine Learning, 3102 Bioinformatics and Computational Biology, epitope prediction, Epitopes, Neoplasms, Machine Learning and Artificial Intelligence, anzsrc-for: 31 Biological Sciences, Vaccines, 0303 health sciences, Zika Virus Infection, other infectious diseases, 3 Good Health and Well Being, 3. Good health, mRNA vaccines, Infectious Diseases, Networking and Information Technology R&D (NITRD), 3.4 Vaccines, Virus Diseases, Influenza Vaccines, Patient Safety, anzsrc-for: 3102 Bioinformatics and Computational Biology, Infection, Algorithms, Biotechnology, COVID-19 Vaccines, anzsrc-for: 0604 Genetics, Bioengineering, Dengue Vaccines, Cancer Vaccines, Vaccine Related, 03 medical and health sciences, Biodefense, Diabetes Mellitus, Respiratory Syncytial Virus Vaccines, Animals, Humans, RNA, Messenger, Pandemics, SARS-CoV-2, Prevention, COVID-19, Computational Biology, Vector-Borne Diseases, Emerging Infectious Diseases, Communicable Disease Control, RNA, Immunization, Software, 31 Biological Sciences

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Access URL: https://academic.oup.com/bfg/article-pdf/20/5/289/40349787/elab027.pdf
https://pubmed.ncbi.nlm.nih.gov/34089044
https://pubmed.ncbi.nlm.nih.gov/34089044/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194884
https://europepmc.org/article/PMC/PMC8194884
https://academic.oup.com/bfg/article/20/5/289/6292285
https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1258749

The oncofetal RNA-binding protein IGF2BP1 is a druggable, post-transcriptional super-enhancer of E2F-driven gene expression in cancer

Authors: Müller, S Bley, N Busch, B et al.

Source: Nucleic Acids Res

Subject Terms: 0301 basic medicine, 1.1 Normal biological development and functioning, 3101 Biochemistry and Cell Biology, Cell Line, Small Molecule Libraries, 3102 Bioinformatics and Computational Biology, Mice, 03 medical and health sciences, Rare Diseases, Cancer Genomics, anzsrc-for: 34 Chemical sciences, Cell Line, Tumor, Neoplasms, Genetics, 2.1 Biological and endogenous factors, Animals, Humans, anzsrc-for: 31 Biological Sciences, Molecular Biology, 3' Untranslated Regions, Cancer, Neoplastic, 0303 health sciences, Tumor, Human Genome, anzsrc-for: 3101 Biochemistry and Cell Biology, anzsrc-for: 05 Environmental Sciences, RNA-Binding Proteins, E2F Transcription Factors, 3. Good health, anzsrc-for: 41 Environmental sciences, Gene Expression Regulation, Neoplastic, Gene Expression Regulation, anzsrc-for: 06 Biological Sciences, Generic health relevance, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 08 Information and Computing Sciences, E2F1 Transcription Factor, 31 Biological Sciences, Biotechnology

File Description: application/pdf

Access URL: https://academic.oup.com/nar/article-pdf/48/15/8576/33697465/gkaa653.pdf
https://pubmed.ncbi.nlm.nih.gov/32761127
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470957
https://pubmed.ncbi.nlm.nih.gov/32761127/
https://academic.oup.com/nar/article/48/15/8576/5881798
https://europepmc.org/articles/PMC7470957/

Density-based detection of cell transition states to construct disparate and bifurcating trajectories

Authors: Lan, T Hutvagner, G Zhang, X et al.

Source: urn:ISSN:0305-1048 ; urn:ISSN:1362-4962 ; Nucleic acids research, 50, 21, e122-e122

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, Cancer Genomics, Genetics, Cancer, Human Genome, 1.1 Normal biological development and functioning, Humans, Single-Cell Analysis, SARS-CoV-2, COVID-19, Cell Differentiation, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 05 Environmental Sciences, anzsrc-for: 06 Biological Sciences, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 34 Chemical sciences, anzsrc-for: 41 Environmental sciences

File Description: application/pdf

Relation: http://hdl.handle.net/1959.4/unsworks_82561; https://doi.org/10.1093/nar/gkac785

Availability: http://hdl.handle.net/1959.4/unsworks_82561
https://unsworks.unsw.edu.au/bitstreams/224bfec6-f949-406a-ab98-8a4c36cd4260/download
https://doi.org/10.1093/nar/gkac785

Application of network link prediction in drug discovery

Authors: Abbas, K Abbasi, A Dong, S et al.

Source: urn:ISSN:1471-2105 ; BMC Bioinformatics, 22, 1

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, Data Science, Patient Safety, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering, 8.4 Research design and methodologies (health services), 1.4 Methodologies and measurements, Generic health relevance, 3 Good Health and Well Being, Algorithms, Drug Discovery, Drug Interactions, Machine Learning, Data-driven drug discovery, Drug-target prediction, Network link prediction, Poly-pharmacy, Poly-pharmacy side effects prediction, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 01 Mathematical Sciences, anzsrc-for: 06 Biological Sciences, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 46 Information and computing sciences, anzsrc-for: 49 Mathematical sciences

File Description: application/pdf

Relation: http://hdl.handle.net/1959.4/unsworks_83625

Availability: http://hdl.handle.net/1959.4/unsworks_83625
https://unsworks.unsw.edu.au/bitstreams/f44db202-01dc-496a-ab08-28e7c8d1f8a2/download
https://doi.org/10.1186/s12859-021-04082-y

VIRMOTIF: A user-friendly tool for viral sequence analysis

Authors: Rajaei, P Jahanian, KH Beheshti, A et al.

Source: urn:ISSN:2073-4425 ; Genes, 12, 2, 1-9

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, Bioengineering, Networking and Information Technology R&D (NITRD), Biotechnology, 1.1 Normal biological development and functioning, 1.5 Resources and infrastructure (underpinning), Generic health relevance, Algorithms, Cluster Analysis, Computational Biology, Databases, Genetic, Genetic Variation, Genome, Viral, Nucleotide Motifs, Sequence Analysis, DNA, Software, User-Computer Interface, D-ratio, motif analysis, sequence variation, virus genome, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 0604 Genetics, anzsrc-for: 3105 Genetics

File Description: application/pdf

Relation: http://hdl.handle.net/1959.4/unsworks_74948; https://doi.org/10.3390/genes12020186

Availability: http://hdl.handle.net/1959.4/unsworks_74948
https://unsworks.unsw.edu.au/bitstreams/8a4858e8-873c-452e-bcab-004ab61cbac1/download
https://doi.org/10.3390/genes12020186

CoCoCoNet: Conserved and Comparative Co-expression Across a Diverse Set of Species

Authors: Jesse Gillis Megan Crow Sara Ballouz et al.

Source: Nucleic Acids Res

Subject Terms: 0301 basic medicine, Saccharomyces cerevisiae Proteins, Autism Spectrum Disorder, autism, Gene Expression, yeast, Saccharomyces, 3102 Bioinformatics and Computational Biology, 03 medical and health sciences, computational biology, anzsrc-for: 34 Chemical sciences, Genetics, Animals, Humans, personality disorders, Gene Regulatory Networks, RNA-Seq, anzsrc-for: 31 Biological Sciences, 0303 health sciences, anzsrc-for: 05 Environmental Sciences, mental disorders, anzsrc-for: 41 Environmental sciences, Mental Health, diseases & disorders, Web Server Issue, gene network, anzsrc-for: 06 Biological Sciences, gene expression, Generic health relevance, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 08 Information and Computing Sciences, Software, 31 Biological Sciences, Biotechnology

File Description: application/pdf

Access URL: https://academic.oup.com/nar/article-pdf/48/W1/W566/33433140/gkaa348.pdf
https://pubmed.ncbi.nlm.nih.gov/32392296
https://www.biorxiv.org/content/10.1101/2020.04.21.053900v1
https://biorxiv.org/content/10.1101/2020.04.21.053900v1.full.pdf
https://www.biorxiv.org/content/10.1101/2020.04.21.053900v1.full-text

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Authors: Shamim Ahmad Mohammad Ali Moni M. Babul Islam et al.

Contributors: Shamim Ahmad Mohammad Ali Moni M. Babul Islam et al.

Source: PLoS One
PLoS ONE, Vol 16, Iss 5, p e0250660 (2021)

Subject Terms: FOS: Computer and information sciences, 0301 basic medicine, Aging, Physiology, Comorbidity, Neurodegenerative, Alzheimer's Disease, Computational biology, 3102 Bioinformatics and Computational Biology, Mechanisms of Alzheimer's Disease, Pathology, 2.1 Biological and endogenous factors, Disease, anzsrc-for: 31 Biological Sciences, 10. No inequality, 2. Zero hunger, 0303 health sciences, Systems Biology, Life Sciences, Neurodegenerative Diseases, Diagnosis and Management of Alzheimer's Disease, Analysis of Gene Interaction Networks, Programming language, 3. Good health, Psychiatry and Mental health, Neurological, Medicine, anzsrc-for: 3102 Bioinformatics and Computational Biology, Research Article, Bioinformatics, Science, Neuroimaging, 3105 Genetics, 03 medical and health sciences, Alzheimer Disease, Biochemistry, Genetics and Molecular Biology, Health Sciences, Acquired Cognitive Impairment, Genetics, Humans, Neurodegeneration, Molecular Biology, Biology, Gene Expression Profiling, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Computational Biology, Computer science, Brain Disorders, anzsrc-for: 3105 Genetics, Gene Ontology, FOS: Biological sciences, Pipeline (software), Dementia, 31 Biological Sciences

File Description: application/pdf

Access URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0250660&type=printable
https://pubmed.ncbi.nlm.nih.gov/33956862
https://doaj.org/article/feb240abcbab490f8acd82b57e33dad6
http://www.ncbi.nlm.nih.gov/pubmed/33956862
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250660
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101720
https://europepmc.org/article/MED/33956862
https://dx.plos.org/10.1371/journal.pone.0250660
https://ui.adsabs.harvard.edu/abs/2021PLoSO..1650660C/abstract

Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions

Authors: Peng, H Zheng, Y Zhao, Z et al.

Source: Bioinformatics. 34:i757-i765

Subject Terms: 0301 basic medicine, anzsrc-for: 46 Information and computing sciences, Base Composition, Genome, anzsrc-for: 01 Mathematical Sciences, High-Throughput Nucleotide Sequencing, 3 Good Health and Well Being, anzsrc-for: 49 Mathematical sciences, RNA, Guide, CRISPR-Cas Systems, 3. Good health, Machine Learning, 3102 Bioinformatics and Computational Biology, 03 medical and health sciences, anzsrc-for: 06 Biological Sciences, RNA, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, CRISPR-Cas Systems, anzsrc-for: 08 Information and Computing Sciences, Guide, Software, 31 Biological Sciences, Biotechnology

File Description: application/pdf

Access URL: https://academic.oup.com/bioinformatics/article-pdf/34/17/i757/25702215/bty558.pdf
https://pubmed.ncbi.nlm.nih.gov/30423065
https://www.ncbi.nlm.nih.gov/pubmed/30423065
https://doi.org/10.1093/bioinformatics/bty558
https://pubmed.ncbi.nlm.nih.gov/30423065/
https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics34.html#PengZZLL18
https://europepmc.org/article/MED/30423065
https://academic.oup.com/bioinformatics/article/34/17/i757/5093213

Seave: a comprehensive web platform for storing and interrogating human genomic variation

Authors: Velimir Gayevskiy Tony Roscioli Marcel E. Dinger et al.

Contributors: Velimir Gayevskiy Tony Roscioli Marcel E. Dinger et al.

Source: Bioinformatics

Subject Terms: 0301 basic medicine, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 49 Mathematical sciences, 3105 Genetics, 3102 Bioinformatics and Computational Biology, 03 medical and health sciences, 46 Information and Computing Sciences, Gene Frequency, Genetics, Humans, anzsrc-for: 31 Biological Sciences, Internet, 0303 health sciences, Genome, Genome, Human, Human Genome, anzsrc-for: 01 Mathematical Sciences, Computational Biology, Genetic Variation, Genomics, 1.5 Resources and infrastructure (underpinning), Applications Notes, anzsrc-for: 3105 Genetics, anzsrc-for: 06 Biological Sciences, Generic health relevance, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 08 Information and Computing Sciences, Algorithms, Software, 31 Biological Sciences, Biotechnology, Human

File Description: application/pdf

Access URL: https://academic.oup.com/bioinformatics/article-pdf/35/1/122/27182959/bty540.pdf
https://pubmed.ncbi.nlm.nih.gov/30561546
https://www.biorxiv.org/content/biorxiv/early/2018/01/31/258061.full.pdf
https://www.biorxiv.org/content/early/2018/01/31/258061
https://www.biorxiv.org/content/10.1101/258061v1
https://academic.oup.com/bioinformatics/article/35/1/122/5047758
https://doi.org/10.1093/bioinformatics/bty540
https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics35.html#GayevskiyRDC19
https://www.ncbi.nlm.nih.gov/pubmed/30561546
https://pubmed.ncbi.nlm.nih.gov/30561546/
https://europepmc.org/article/MED/30561546

A generalised approach for high-throughput instance segmentation of stomata in microscope images

Authors: Jayakody, H Petrie, P Boer, HJD et al.

Source: urn:ISSN:1746-4811 ; Plant Methods, 17, 1, 27

Subject Terms: 30 Agricultural, Veterinary and Food Sciences, 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, 3108 Plant Biology, 3001 Agricultural Biotechnology, Bioengineering, Networking and Information Technology R&D (NITRD), Generic health relevance, Automatic stomata detection, High-throughput analysis, Instance segmentation, Machine learning, Mask R-CNN, Microscope imagery, anzsrc-for: 30 Agricultural, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 3108 Plant Biology, anzsrc-for: 3001 Agricultural Biotechnology, anzsrc-for: 0601 Biochemistry and Cell Biology, anzsrc-for: 0607 Plant Biology, anzsrc-for: 1001 Agricultural Biotechnology

File Description: application/pdf

Relation: https://hdl.handle.net/1959.4/unsworks_75089

Availability: https://hdl.handle.net/1959.4/unsworks_75089
https://unsworks.unsw.edu.au/bitstreams/6eae025a-19fe-4b76-8b5b-7e150c819a0d/download
https://doi.org/10.1186/s13007-021-00727-4

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach

Authors: Huberts, LCE Li, S Blake, V et al.

Source: Computers in Biology and Medicine. 174:108321

Subject Terms: Explainable machine learning, Predicting mortality, Male, Adult, Predicting readmission, 8.1 Organisation and delivery of services, anzsrc-for: 4601 Applied computing, Cardiovascular, Patient Readmission, Machine Learning, anzsrc-for: 3102 Bioinformatics and computational biology, Clinical Research, Risk Factors, Machine Learning and Artificial Intelligence, 80 and over, Humans, Aged, Aged, 80 and over, anzsrc-for: 42 Health Sciences, Prevention, Cardiovascular disease (CVD), 42 Health Sciences, 3 Good Health and Well Being, Middle Aged, 4203 Health Services and Systems, anzsrc-for: 4203 Health Services and Systems, anzsrc-for: 11 Medical and Health Sciences, Cardiovascular Diseases, Female, Patient Safety, anzsrc-for: 09 Engineering, New South Wales, anzsrc-for: 08 Information and Computing Sciences, Algorithms

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/38626511

CoCoCoNet: Conserved and comparative co-expression across a diverse set of species

Authors: Lee, J Shah, M Ballouz, S et al.

Source: urn:ISSN:0305-1048 ; urn:ISSN:1362-4962 ; Nucleic Acids Research, 48, W1, W566-W571

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, Genetics, Mental Health, Biotechnology, Generic health relevance, Animals, Autism Spectrum Disorder, Gene Expression, Gene Regulatory Networks, Humans, RNA-Seq, Saccharomyces cerevisiae Proteins, Software, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 05 Environmental Sciences, anzsrc-for: 06 Biological Sciences, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 34 Chemical sciences, anzsrc-for: 41 Environmental sciences

File Description: application/pdf

Relation: http://hdl.handle.net/1959.4/unsworks_70355; https://doi.org/10.1093/NAR/GKAA348

Availability: http://hdl.handle.net/1959.4/unsworks_70355
https://unsworks.unsw.edu.au/bitstreams/4a336961-d282-4b92-b398-21430731a8e3/download
https://doi.org/10.1093/NAR/GKAA348

Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements

Authors: Ruth Pidsley Tim J Peters Wenjia Qu et al.

Contributors: Ruth Pidsley Tim J Peters Wenjia Qu et al.

Source: Bioinformatics

Subject Terms: 0301 basic medicine, Bioengineering, anzsrc-for: 49 Mathematical sciences, 3105 Genetics, 3102 Bioinformatics and Computational Biology, 03 medical and health sciences, Genetics, Humans, anzsrc-for: 31 Biological Sciences, Oligonucleotide Array Sequence Analysis, anzsrc-for: 46 Information and computing sciences, Genome, Genome, Human, Human Genome, anzsrc-for: 01 Mathematical Sciences, Computational Biology, Genomics, DNA Methylation, Original Papers, anzsrc-for: 3105 Genetics, anzsrc-for: 06 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 08 Information and Computing Sciences, Software, 31 Biological Sciences, Biotechnology, Human

File Description: application/pdf

Access URL: https://academic.oup.com/bioinformatics/article-pdf/35/4/560/27838576/bty675.pdf
https://pubmed.ncbi.nlm.nih.gov/30084929
https://minerva-access.unimelb.edu.au/handle/11343/271038
https://europepmc.org/articles/PMC6378945
https://pubmed.ncbi.nlm.nih.gov/30084929/
https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics35.html#PetersFBPSVNQSG19
https://findanexpert.unimelb.edu.au/scholarlywork/1345377-evaluation-of-cross-platform-and-interlaboratory-concordance-via-consensus-modelling-of-genomic-measurements
https://minerva-access.unimelb.edu.au/bitstream/handle/11343/271038/PMC6378945.pdf

Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs

Authors: Devnath, L Luo, S Summons, P et al.

Source: urn:ISSN:0010-4825 ; urn:ISSN:1879-0534 ; Computers in Biology and Medicine, 129, 104125

Subject Terms: 4611 Machine Learning, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, Bioengineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Biomedical Imaging, 4.1 Discovery and preclinical testing of markers and technologies, Humans, Pneumoconiosis, Radiographic Image Interpretation, Computer-Assisted, Radiography, Support Vector Machine, X-Rays, Black lung, Coal workers' pneumoconiosis (CWP), Computer-aided diagnosis, Deep transfer learning, anzsrc-for: 4611 Machine Learning, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 4603 Computer Vision and Multimedia Computation, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 09 Engineering, anzsrc-for: 11 Medical and Health Sciences, anzsrc-for: 3102 Bioinformatics and computational biology, anzsrc-for: 4203 Health services and systems, anzsrc-for: 4601 Applied computing

Relation: https://hdl.handle.net/1959.4/unsworks_75477; https://doi.org/10.1016/j.compbiomed.2020.104125

Availability: https://hdl.handle.net/1959.4/unsworks_75477
https://doi.org/10.1016/j.compbiomed.2020.104125

Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis

Authors: Lan, Tian Hutvagner, Gyorgy Lan, Qing et al.

Source: urn:ISSN:1467-5463 ; urn:ISSN:1477-4054 ; Briefings in Bioinformatics, 22, 4, bbaa248

Subject Terms: 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, Human Genome, Genetics, Generic health relevance, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, RNA, Messenger, Single-Cell Analysis, Software, Transcriptome, batch effect, dropout, normalization tools, performance evaluation, single-cell mRNA sequencing, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, anzsrc-for: 0601 Biochemistry and Cell Biology, anzsrc-for: 0802 Computation Theory and Mathematics, anzsrc-for: 0899 Other Information and Computing Sciences, anzsrc-for: 3101 Biochemistry and cell biology, anzsrc-for: 3105 Genetics

File Description: application/pdf

Relation: http://purl.org/au-research/grants/nhmrc/APP1106790; http://purl.org/au-research/grants/nhmrc/APP1099025; http://purl.org/au-research/grants/nhmrc/APP1120936; https://doi-org.wwwproxy1.library.unsw.edu.au/10.1093/bib/bbaa248; https://hdl.handle.net/1959.4/unsworks_74169; https://doi.org/10.1093/bib/bbaa248

Availability: https://hdl.handle.net/1959.4/unsworks_74169
https://unsworks.unsw.edu.au/bitstreams/5c17f82f-96ce-4ea7-97bd-922be7872cc3/download
https://doi.org/10.1093/bib/bbaa248

Adaptation of Oxford Nanopore technology for hepatitis C whole genome sequencing and identification of within-host viral variants

Authors: Rowena A. Bull Kirston Barton Andrew R. Lloyd et al.

Source: BMC Genomics
BMC Genomics, Vol 22, Iss 1, Pp 1-12 (2021)

Subject Terms: 0301 basic medicine, Technology, Bioengineering, QH426-470, 3105 Genetics, Hepatitis, 3102 Bioinformatics and Computational Biology, Nanopores, 03 medical and health sciences, Hepatitis - C, Genetics, Nanotechnology, 2.1 Biological and endogenous factors, Humans, Nano-Q, anzsrc-for: 31 Biological Sciences, Phylogeny, 0303 health sciences, Whole Genome Sequencing, Hepatitis C virus, Liver Disease, Human Genome, High-Throughput Nucleotide Sequencing, 3 Good Health and Well Being, DNA, Sequence Analysis, DNA, Hepatitis C, 3. Good health, Oxford Nanopore technology, anzsrc-for: 3105 Genetics, anzsrc-for: 32 Biomedical and clinical sciences, Infectious Diseases, Emerging Infectious Diseases, Cost Effectiveness Research, anzsrc-for: 11 Medical and Health Sciences, Haplotypes, anzsrc-for: 06 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology, Third generation sequencing, Digestive Diseases, Infection, anzsrc-for: 08 Information and Computing Sciences, Sequence Analysis, TP248.13-248.65, 31 Biological Sciences, Biotechnology, Research Article

File Description: application/pdf

Access URL: https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-021-07460-1
https://pubmed.ncbi.nlm.nih.gov/33653280
https://doaj.org/article/27ac5a45c50a4b9891f2a9cec58eaa4c
https://link.springer.com/content/pdf/10.1186/s12864-021-07460-1.pdf
https://pubmed.ncbi.nlm.nih.gov/33653280/
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-021-07460-1
https://link.springer.com/article/10.1186/s12864-021-07460-1
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923462
https://europepmc.org/article/MED/33653280

An ensemble of locally reliable cluster solutions

Authors: Niu, H Khozouie, N Parvin, H et al.

Source: urn:ISSN:2076-3417 ; Applied Sciences (Switzerland), 10, 5, 1891

Subject Terms: 46 Information and Computing Sciences, 31 Biological Sciences, 3102 Bioinformatics and Computational Biology, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 31 Biological Sciences, anzsrc-for: 3102 Bioinformatics and Computational Biology

File Description: application/pdf

Relation: http://hdl.handle.net/1959.4/unsworks_69039; https://doi.org/10.3390/app10051891

Availability: http://hdl.handle.net/1959.4/unsworks_69039
https://unsworks.unsw.edu.au/bitstreams/a1c6d964-efad-4dd5-ba67-56264ef92c0b/download
https://doi.org/10.3390/app10051891