research:sean



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<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Jan
2016—</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Aug 2020</span></p>
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<td width=260 valign=top style='width:260.05pt;border:none;border-bottom:
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<p class=MsoNormal><b><span lang=EN-GB style='font-size:10.0pt;font-family:
"Helvetica",sans-serif'>The University of Queensland</span></b></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>School
of Chemistry &amp; Molecular Biosciences PhD</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Brisbane, Australia

</span></p>

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<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Sep
2006—</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Jun
2009</span></p>
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<td width=260 valign=top style='width:260.05pt;border:none;border-bottom:
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<p class=MsoNormal><b><span lang=EN-GB style='font-size:10.0pt;font-family:
"Helvetica",sans-serif'>National Taiwan University</span></b></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Biomedical
Engineering MSs</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Taipei, Taiwan

</span></p>

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<td width=137 valign=top style='width:136.85pt;border:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Sep
2001—</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Jun
2006</span></p>
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<td width=260 valign=top style='width:260.05pt;border:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal><b><span lang=EN-GB style='font-size:10.0pt;font-family:
"Helvetica",sans-serif'>National Chung Cheng University</span></b></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Computer
Science and Information Engineering BSc</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Chiayi, Taiwan

</span></p>

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<html> <head> <meta name=Title content=""> <meta name=Keywords content=""> <meta http-equiv=Content-Type content="text/html; charset=utf-8"> <meta name=Generator content="Microsoft Word 15 (filtered)"> <style> <!– /* Font Definitions */ @font-face

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<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Nov
2009—</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>May
2015</span></p>
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<p class=MsoNormal><b><span lang=EN-GB style='font-size:10.0pt;font-family:
"Helvetica",sans-serif'>Research Assistant</span></b></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Academia
Sinica Institute of Information Science</span></p>
<p class=MsoNormal><span lang=EN-GB style='font-size:10.0pt;font-family:"Helvetica",sans-serif'>Taipei, Taiwan

</span></p>

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<p class=MsoNormal><span lang=EN-US style='font-size:8.0pt;font-family:"Helvetica",sans-serif'>&nbsp;</span></p> </div> </body> </html>


<html> <head> <title>JabRef references</title> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <script type="text/javascript"> <!– QuickSearch script for JabRef HTML export Version: 3.0 Copyright © 2006-2011, Mark Schenk This software is distributed under a Creative Commons Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Features: - intuitive find-as-you-type searching ~ case insensitive ~ ignore diacritics (optional) - search with/without Regular Expressions - match BibTeX key

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Strip Diacritics from text http://stackoverflow.com/questions/990904/javascript-remove-accents-in-strings

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} function toggleInfo(articleid,info) { var entry = document.getElementById(articleid); var abs = document.getElementById('abs_'+articleid); var rev = document.getElementById('rev_'+articleid); var bib = document.getElementById('bib_'+articleid); if (abs && info == 'abstract') { abs.className.indexOf('noshow') == -1?abs.className = 'abstract noshow':abs.className = 'abstract show'; } else if (rev && info == 'review') { rev.className.indexOf('noshow') == -1?rev.className = 'review noshow':rev.className = 'review show'; } else if (bib && info == 'bibtex') { bib.className.indexOf('noshow') == -1?bib.className = 'bibtex noshow':bib.className = 'bibtex show'; } else { return; } check if one or the other is available var revshow; var absshow; var bibshow; (abs && abs.className.indexOf('noshow') == -1)? absshow = true: absshow = false; (rev && rev.className.indexOf('noshow') == -1)? revshow = true: revshow = false; (bib && bib.className.indexOf('noshow') == -1)? bibshow = true: bibshow = false; 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padding: 0px; } @media print { p.infolinks, #qs_settings, #quicksearch, t.bibtex { display: none !important; } tr { page-break-inside: avoid; } } </style> </head> <body> <form action="" id="quicksearch"> <input type="text" id="qs_field" autocomplete="off" placeholder="Type to search…" /> <input type="button" onclick="clearQS()" value="clear" /> <span id="searchstat">Matching entries: <span id="stat">0</span></span> <div id="showsettings" onclick="toggleSettings()">settings…</div> <div id="settings" class="hidden"> <ul> <li><input type="checkbox" class="search_setting" id="opt_searchAbs" onchange="updateSetting(this)"> include abstract</li> <li><input type="checkbox" class="search_setting" id="opt_searchRev" onchange="updateSetting(this)"> include review</li> <li><input type="checkbox" class="search_setting" id="opt_useRegExp" onchange="updateSetting(this)"> use RegExp</li> <li><input type="checkbox" class="search_setting" id="opt_noAccents" onchange="updateSetting(this)"> ignore accents</li> </ul> </div> </form> <table id="qs_table" border="1"> <tbody> <tr id="Lai2020" class="entry"> <td>Lai J-S, Rost B, Kobe B and Bodén M (2020), <i>"Evolutionary model of protein secondary structure capable of revealing new biological relationships"</i>, Proteins., 11, May, 2020. <p class="infolinks">[<a href="javascript:toggleInfo('Lai2020','abstract')">Abstract</a>] [<a href="javascript:toggleInfo('Lai2020','bibtex')">BibTeX</a>] [<a href="https://doi.org/10.1002/prot.25898" target="_blank">DOI</a>] [<a href="https://doi.org/10.1002/prot.25898" target="_blank">URL</a>]</p> </td> </tr> <tr id="abs_Lai2020" class="abstract noshow"> <td><b>Abstract</b>: Abstract Ancestral sequence reconstruction has had recent success in decoding the origins and the determinants of complex protein functions. However, phylogenetic analyses of remote homologues must handle extreme amino-acid sequence diversity resulting from extended periods of evolutionary change. We exploited the wealth of protein structures to develop an evolutionary model based on protein secondary structure. The approach follows the differences between discrete secondary structure states observed in modern proteins and those hypothesised in their immediate ancestors. We implemented maximum likelihood-based phylogenetic inference to reconstruct ancestral secondary structure. The predictive accuracy from the use of the evolutionary model surpasses that of comparative modelling and sequence-based prediction; the reconstruction extracts information not available from modern structures or the ancestral sequences alone. Based on a phylogenetic analysis of a sequence-diverse protein family, we showed that the model can highlight relationships that are evolutionarily rooted in structure and not evident in amino acid-based analysis.</td> </tr> <tr id="bib_Lai2020" class="bibtex noshow"> <td><b>BibTeX</b>: <pre> @article{Lai2020, author = {Lai, Jhih-Siang and Rost, Burkhard and Kobe, Bostjan and Bodén, Mikael}, title = {Evolutionary model of protein secondary structure capable of revealing new biological relationships}, journal = {Proteins}, publisher = {John Wiley &amp; Sons, Ltd}, year = {2020}, volume = {n/a}, number = {n/a}, url = {https://doi.org/10.1002/prot.25898}, doi = {10.1002/prot.25898} } </pre></td> </tr> <tr id="Horsefield2019" class="entry"> <td>Horsefield S, Burdett H, Zhang X, Manik MK, Shi Y, Chen J, Qi T, Gilley J, Lai J-S, Rank MX, Casey LW, Gu W, Ericsson DJ, Foley G, Hughes RO, Bosanac T, von Itzstein M, Rathjen JP, Nanson JD, Boden M, Dry IB, Williams SJ, Staskawicz BJ, Coleman MP, Ve T, Dodds PN and Kobe B (2019), <i>"NAD+ cleavage activity by animal and plant TIR domains in cell death pathways"</i>, Science., August, 2019. Vol. 365(6455), pp. 793. <p class="infolinks">[<a href="javascript:toggleInfo('Horsefield2019','abstract')">Abstract</a>] [<a href="javascript:toggleInfo('Horsefield2019','bibtex')">BibTeX</a>] [<a href="http://science.sciencemag.org/content/365/6455/793.abstract" target="_blank">URL</a>]</p> </td> </tr> <tr id="abs_Horsefield2019" class="abstract noshow"> <td><b>Abstract</b>: One way that plants respond to pathogen infection is by sacrificing the infected cells. The nucleotide-binding leucine-rich repeat immune receptors responsible for this hypersensitive response carry Toll/interleukin-1 receptor (TIR) domains. In two papers, Horsefield et al. and Wan et al. report that these TIR domains cleave the metabolic cofactor nicotinamide adenine dinucleotide (NAD+) as part of their cell-death signaling in response to pathogens. Similar signaling links mammalian TIR-containing proteins to NAD+ depletion during Wallerian degeneration of neurons.Science, this issue p. 793, p. 799SARM1 (sterile alpha and TIR motif containing 1) is responsible for depletion of nicotinamide adenine dinucleotide in its oxidized form (NAD+) during Wallerian degeneration associated with neuropathies. Plant nucleotide-binding leucine-rich repeat (NLR) immune receptors recognize pathogen effector proteins and trigger localized cell death to restrict pathogen infection. Both processes depend on closely related Toll/interleukin-1 receptor (TIR) domains in these proteins, which, as we show, feature self-association-dependent NAD+ cleavage activity associated with cell death signaling. We further show that SARM1 SAM (sterile alpha motif) domains form an octamer essential for axon degeneration that contributes to TIR domain enzymatic activity. The crystal structures of ribose and NADP+ (the oxidized form of nicotinamide adenine dinucleotide phosphate) complexes of SARM1 and plant NLR RUN1 TIR domains, respectively, reveal a conserved substrate binding site. NAD+ cleavage by TIR domains is therefore a conserved feature of animal and plant cell death signaling pathways.</td> </tr> <tr id="bib_Horsefield2019" class="bibtex noshow"> <td><b>BibTeX</b>: <pre> @article{Horsefield2019, author = {Horsefield, Shane and Burdett, Hayden and Zhang, Xiaoxiao and Manik, Mohammad K. and Shi, Yun and Chen, Jian and Qi, Tiancong and Gilley, Jonathan and Lai, Jhih-Siang and Rank, Maxwell X. and Casey, Lachlan W. and Gu, Weixi and Ericsson, Daniel J. and Foley, Gabriel and Hughes, Robert O. and Bosanac, Todd and von Itzstein, Mark and Rathjen, John P. and Nanson, Jeffrey D. and Boden, Mikael and Dry, Ian B. and Williams, Simon J. and Staskawicz, Brian J. and Coleman, Michael P. and Ve, Thomas and Dodds, Peter N. and Kobe, Bostjan}, title = {NAD+ cleavage activity by animal and plant TIR domains in cell death pathways}, journal = {Science}, year = {2019}, volume = {365}, number = {6455}, pages = {793}, url = {http://science.sciencemag.org/content/365/6455/793.abstract} } </pre></td> </tr> <tr id="Lai2013" class="entry"> <td>Lai J-S, Cheng C-W, Lo A, Sung T-Y and Hsu W-L (2013), <i>"Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices"</i>, BMC Bioinformatics., October, 2013. Vol. 14(1), pp. 304. <p class="infolinks">[<a href="javascript:toggleInfo('Lai2013','abstract')">Abstract</a>] [<a href="javascript:toggleInfo('Lai2013','bibtex')">BibTeX</a>] [<a href="https://doi.org/10.1186/1471-2105-14-304" target="_blank">URL</a>]</p> </td> </tr> <tr id="abs_Lai2013" class="abstract noshow"> <td><b>Abstract</b>: Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle.</td> </tr> <tr id="bib_Lai2013" class="bibtex noshow"> <td><b>BibTeX</b>: <pre> @article{Lai2013, author = {Lai, Jhih-Siang and Cheng, Cheng-Wei and Lo, Allan and Sung, Ting-Yi and Hsu, Wen-Lian}, title = {Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices}, journal = {BMC Bioinformatics}, year = {2013}, volume = {14}, number = {1}, pages = {304}, url = {https://doi.org/10.1186/1471-2105-14-304} } </pre></td> </tr> <tr id="Lai2012" class="entry"> <td>Lai J-S, Cheng C-W, Sung T-Y and Hsu W-L (2012), <i>"Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures"</i>, PloS one. Vol. 7(4), pp. e35018-e35018. Public Library of Science. <p class="infolinks">[<a href="javascript:toggleInfo('Lai2012','abstract')">Abstract</a>] [<a href="javascript:toggleInfo('Lai2012','bibtex')">BibTeX</a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/22496884" target="_blank">URL</a>]</p> </td> </tr> <tr id="abs_Lai2012" class="abstract noshow"> <td><b>Abstract</b>: Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.</td> </tr> <tr id="bib_Lai2012" class="bibtex noshow"> <td><b>BibTeX</b>: <pre> @article{Lai2012, author = {Lai, Jhih-Siang and Cheng, Cheng-Wei and Sung, Ting-Yi and Hsu, Wen-Lian}, title = {Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures}, journal = {PloS one}, publisher = {Public Library of Science}, year = {2012}, volume = {7}, number = {4}, pages = {e35018–e35018}, edition = {2012/04/09}, url = {https://pubmed.ncbi.nlm.nih.gov/22496884} } </pre></td> </tr> <tr id="Tsai2009" class="entry"> <td>Tsai K-N, Lin S-H, Shih S-R, Lai J-S and Chen C-M (2009), <i>"Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses"</i>, Computational Biology and Chemistry., April, 2009. Vol. 33(2), pp. 171-175. <p class="infolinks">[<a href="javascript:toggleInfo('Tsai2009','abstract')">Abstract</a>] [<a href="javascript:toggleInfo('Tsai2009','bibtex')">BibTeX</a>] [<a href="http://www.sciencedirect.com/science/article/pii/S1476927108001278" target="_blank">URL</a>]</p> </td> </tr> <tr id="abs_Tsai2009" class="abstract noshow"> <td><b>Abstract</b>: Splice site prediction on an RNA virus has two potential difficulties seriously degrading the performance of most conventional splice site predictors. One is a limited number of strains available for a virus species and the other is the diversified sequence patterns around the splice sites caused by the high mutation frequency. To overcome these two difficulties, a new algorithm called Genomic Splice Site Prediction (GSSP) algorithm, was proposed for splice site prediction of RNA viruses. The key idea of the GSSP algorithm was to characterize the interdependency among the nucleotides and base positions based on the eigen-patterns. Identified by a sequence pattern mining technique, each eigen-pattern specified a unique composition of the base positions and the nucleotides occurring at the positions. To remedy the problem of insufficient training data due to the limited number of strains for an RNA virus, a cross-species strategy was employed in this study. The GSSP algorithm was shown to be effective and superior to two conventional methods in predicting the splice sites of five RNA species in the Orthomyxoviruses family. The sensitivity and specificity achieved by the GSSP algorithm was higher than 99 and 94%, respectively, for the donor sites, and was higher than 96 and 92%, respectively, for the acceptor sites. Supplementary data associated with this work are freely available for academic use at http://homepage.ntu.edu.tw/∼d91548013/.</td> </tr> <tr id="bib_Tsai2009" class="bibtex noshow"> <td><b>BibTeX</b>: <pre> @article{Tsai2009, author = {Tsai, Kun-Nan and Lin, Shu-Hung and Shih, Shin-Ru and Lai, Jhih-Siang and Chen, Chung-Ming}, title = {Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses}, journal = {Computational Biology and Chemistry}, year = {2009}, volume = {33}, number = {2}, pages = {171–175}, url = {http://www.sciencedirect.com/science/article/pii/S1476927108001278} } </pre></td> </tr> </tbody> </table> <footer> <small>Created by <a href="http://jabref.sourceforge.net">JabRef</a> on 24/05/2020.</small> </footer> <!– file generated by JabRef –> </body> </html> —- ==== Projects ==== Evolu-sec package —- ==== Scholarships ==== <html> <head> <meta http-equiv=Content-Type content="text/html; charset=unicode"> <meta name=Generator content="Microsoft Word 15 (filtered)"> <style> <!– /* Font Definitions */ @font-face {font-family:Helvetica; 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height:19.85pt'> <p class=MsoNormal style='text-align:justify'><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>Candidate travel award</span></p> </td> <td width="12%" valign=top style='width:12.58%;border:none;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>2019</span></p> </td> <td width="49%" valign=top style='width:49.68%;border:none;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>Scholarship support for 6-month visiting Technische Universität München, Germany (January-July 2019).</span></p> </td> </tr> <tr style='height:19.85pt'> <td width="37%" valign=top style='width:37.74%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal style='text-align:justify'><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>University of Queensland International Scholarship (UQI)</span></p> </td> <td width="12%" valign=top style='width:12.58%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>2016</span></p> </td> <td width="49%" valign=top style='width:49.68%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>Tuition fee award for three years</span></p> </td> </tr> <tr style='height:19.85pt'> <td width="37%" valign=top style='width:37.74%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal style='text-align:justify'><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>Research Higher Degree Scholarship</span></p> </td> <td width="12%" valign=top style='width:12.58%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>2016</span></p> </td> <td width="49%" valign=top style='width:49.68%;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:Helvetica'>Living cost support for three years</span></p> </td> </tr> </table> <p class=MsoNormal><span style='font-family:Helvetica;color:white'>&nbsp;</span></p> </div> </body> </html> —- ==== Conferences ==== <html> <head> <meta http-equiv=Content-Type content="text/html; charset=utf-8"> <meta name=Generator content="Microsoft Word 15 (filtered)"> <style> <!– /* Font Definitions */ @font-face {font-family:Helvetica; panose-1:0 0 0 0 0 0 0 0 0 0;} @font-face {font-family:PMingLiU; panose-1:2 2 5 0 0 0 0 0 0 0;} @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4;} @font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4;} @font-face {font-family:"Times New Roman \(Body CS\)"; panose-1:2 2 6 3 5 4 5 2 3 4;} @font-face {font-family:"\@PMingLiU"; panose-1:2 1 6 1 0 1 1 1 1 1;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {margin:0cm; margin-bottom:.0001pt; font-size:12.0pt; font-family:"Calibri",sans-serif;} .MsoChpDefault {font-family:Helvetica;} /* Page Definitions */ @page WordSection1 {size:595.0pt 842.0pt; margin:36.0pt 36.0pt 36.0pt 36.0pt;} div.WordSection1 {page:WordSection1;} –> </style> </head> <body lang=EN-AU> <div class=WordSection1> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;border:none'> <tr style='height:19.85pt'> <td width=230 valign=top style='width:172.25pt;border:none;border-bottom: solid windowtext 1.0pt;padding:0cm 5.4pt 0cm 5.4pt;height:19.85pt'> <p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Helvetica'>Conference name</span></b></p> </td> <td width=423 valign=top style='width:317.2pt;border:none;border-bottom:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt;height:19.85pt'> <p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Helvetica'>Presentation</span></b></p> </td> </tr> <tr style='height:19.85pt'> <td width=230 valign=top style='width:172.25pt;border:none;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:10.0pt;font-family:Helvetica'>2019. ISMB<br> (Intelligent Systems for Molecular Biology)</span></p> </td> <td width=423 valign=top style='width:317.2pt;border:none;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal style='text-align:justify;text-justify:inter-ideograph'><span style='font-size:10.0pt;font-family:Helvetica'>Phylogenetic analysis in the predicted secondary structure space (poster)</span></p> </td> </tr> <tr style='height:19.85pt'> <td width=230 valign=top style='width:172.25pt;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:10.0pt;font-family:Helvetica'>2017. ISMB<br> (Intelligent Systems for Molecular Biology)</span></p> </td> <td width=423 valign=top style='width:317.2pt;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal style='text-align:justify;text-justify:inter-ideograph'><span style='font-size:10.0pt;font-family:Helvetica'>Modelling the evolution of protein secondary structure (poster)</span></p> </td> </tr> <tr style='height:19.85pt'> <td width=230 valign=top style='width:172.25pt;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal><span lang=EN-US style='font-size:10.0pt;font-family:Helvetica'>2014. CASP<br> (Critical Assessment of Structure Prediction)</span></p> </td> <td width=423 valign=top style='width:317.2pt;padding:0cm 5.4pt 0cm 5.4pt; height:19.85pt'> <p class=MsoNormal style='text-align:justify;text-justify:inter-ideograph'><span style='font-size:10.0pt;font-family:Helvetica'>Protein residue-residue contact prediction by co-evolution analysis and machine Learning</span><span lang=EN-US style='font-size:10.0pt;font-family:Helvetica'> (</span><span style='font-size:10.0pt;font-family:Helvetica'>poster)</span></p> </td> </tr> </table> </div> </body> </html> —- ==== Programming skills ==== JAVA, C/C++, MATALB, R, PYTHON —- ==== External links ==== Sean in ResearchGate
Sean in ORCID
—- ==== Other skills ==== Certified lifeguard
Trail/Distance running ======Authors======

Created by sean (Jhih-Siang Lai) on 2020/03/20 13:21.

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