This document is a CV for Chien-Wei (Masaki) Lin, a PhD candidate in Biostatistics at the University of Pittsburgh. It summarizes his education, research interests in statistical genetics and neuroscience, publications, teaching experience, and skills. His research focuses on developing statistical methods for analyzing high-throughput omics data, with a particular interest in integrating multi-omics datasets. He has over 15 peer-reviewed publications and has presented his work at several conferences.
Tianpei Xie's research focuses on robust machine learning from multiple data sources. He has developed algorithms for robust classification in the presence of noisy or corrupted training data, including GEM-MED which jointly performs classification and anomaly detection. He has also developed methods for multi-view learning on statistical manifolds, including CMV-MED which co-regularizes multiple models using a robust consensus measure based on information divergence between probability density functions. Current work involves predicting node attributes in networks by combining network topology and node distributions. He has published papers in major machine learning conferences and journals and maintains websites with details of his research activities.
International Journal of Data Mining & Knowledge Management Process - novemb...IJDKP
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Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
This document is a research statement by Chien-Wei (Masaki) Lin that summarizes his past and ongoing methodology and collaborative research projects. It discusses his interests in developing statistical methods for analyzing multi-omics data, including power calculation tools, meta-analysis and integrative analysis methods. It also summarizes some of Lin's collaboration projects applying these statistical methods to study topics like brain aging, major depressive disorder, and cardiovascular epidemiology. The document references 18 of Lin's publications and provides an overview of his diverse experience and future research plans developing statistical tools and methods and applying them to biological problems.
This document summarizes Saige Rutherford's presentation on bridging the technical and clinical gaps in normative modeling. It discusses prior work developing predictive models at UMich, but notes a lack of clinical results. The presentation then outlines efforts to build normative models from a large open dataset of over 58,000 brain scans, and develop associated software tools. Evaluation shows the models generalize across sites and resampled data, and can predict outcomes like cognitive ability scores and classify clinical populations like schizophrenia patients from controls. The work provides evidence that embracing normative modeling approaches can help translate technical advances into clinically useful applications.
Effects of Network Structure, Competition and Memory Time on Social Spreading...James Gleeson
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Talk at the Computational Social Science workshop at the Conference on Complex Systems #CCS16, Amsterdam, 21 Sep 2016, http://cssworkshop.oii.ox.ac.uk/
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speakers variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria.
TOP READ NATURAL LANGUAGE COMPUTING ARTICLE 2020kevig
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Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
ICCSS2015 talk: Null model for meme popularityJames Gleeson
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The document presents a model of how memes compete for popularity when spreading over social networks. The model accounts for the effects of human memory timescales and network structure on meme diffusion. It shows that competition between memes for limited user attention can induce critical behavior, producing power law popularity distributions and linear popularity growth over time. Comparison to empirical data demonstrates the model provides a useful null model for understanding how memory, networks and competition influence meme popularity.
Multi-target prediction (MTP) involves simultaneously predicting multiple target outputs from diverse data types using input features. In contrast to single-target prediction which predicts a single output, MTP can handle tasks like multivariate regression, multi-task learning, and multi-label classification. MTP can be improved by incorporating side information, such as representations of target molecules or category tags of documents. Side information is important for generalizing to new, unobserved targets. MTP shows potential for diagnosing depression, anxiety, and stress using the DASS-21 scale from both structured and unstructured data sources.
Kernel-based machine learning methods are for predicting the structured, non-tabular data arising in biomedicine and digital health, especially applications on small molecules such as drugs and metabolites.
How to use science maps to navigate large information spaces? What is the lin...Andrea Scharnhorst
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A. Scharnhorst (2016) Wie k旦nnen Wissenschaftskarten zur Suche in grossen Informationsr辰umen eingesetzt werden? How to use science maps to navigate large information spaces? What is the link between science maps and predictive models of science? Invited lecture Fraunhofer-Institut f端r Naturwissenschaftlich-Technische Trendanalysen, Euskirchen, Germany, December 7, 2016
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...Hakka Labs
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Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occams razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
This curriculum vitae summarizes Nicola Amoroso's education and professional experience. He holds a PhD in Physics from 2014 with a thesis on quantitative MRI analysis in Alzheimer's disease. His postdoctoral research has focused on developing cloud computing solutions to support neuroimaging data analysis. He has published over 10 papers in peer-reviewed journals on topics including hippocampal segmentation, machine learning applications for brain disease detection, and complex network analysis of neuroimaging data.
Benjamin E. Deonovic is a Ph.D. candidate in Biostatistics at the University of Iowa, expected to graduate in May 2017. His research interests include Bayesian modeling, MCMC, and statistical methods applied to genomics and bioinformatics. He has published papers on haplotype phasing and allele-specific expression using hybrid sequencing data, and has presented his research at several conferences. Deonovic has worked as a research assistant at the University of Iowa on projects involving genetic association studies, pathway analysis, and joint modeling of sequencing data.
Interactive Visualization Systems and Data Integration Methods for Supporting...Don Pellegrino
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This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
This document provides an introduction to multivariate statistics. It begins with background on the Indian Statistical Institute where the author is located. It then discusses some common myths about multivariate statistics, defining it as the analysis of relationships between sets of variables. The document lists several multivariate statistical tools and provides examples of research questions they could address related to women and child development. It also summarizes some published studies utilizing multivariate techniques like principal component analysis, correspondence analysis, cluster analysis, and MANOVA.
Roland Matsouaka is an Assistant Professor of Biostatistics and Bioinformatics at Duke University Medical Center. He received his PhD in Biostatistics from Harvard School of Public Health in 2012. His research interests include model selection and validation, risk adjustment, biomarker evaluation, and causal inference methods. He has over 10 publications in peer-reviewed journals and has served as a co-investigator on several grants related to cardiovascular disease.
Understanding medical concepts and codes through NLP methodsAshis Chanda
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This document summarizes two projects aimed at improving representations of medical concepts and codes through natural language processing of clinical notes. The first project uses an external knowledge graph to enhance skip-gram embeddings of medical concepts. The second project jointly learns representations of medical codes and words from clinical notes using a modified skip-gram model that considers relationships between codes, words, and codes and words. The document concludes by discussing future directions, including applying these methods to other domains and using more recent pre-trained language models like clinical BERT.
The document discusses learning analytics and cognitive automation, and their implications for education. It begins by outlining how cognitive automation is automating routine cognitive work. This will impact learning analytics, as analytics aggregate lower-level data and AI automates routine cognitive tasks. As a result, humans must focus on higher-order skills like creativity, ethics, resilience and curiosity. The document then provides examples of learning analytics research focusing on dispositions, teamwork and learning beyond the classroom. It argues analytics could assess holistic development if they evaluate integration of knowledge, skills and dispositions over time.
Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics
Innovating Pedagogical Practices through Professional Development in Computer Science Education
Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data
Enhancing Human-Machine Interaction: Real-Time Emotion Recognition through Speech Analysis
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This doctoral dissertation defense presentation summarizes Yang Yang's dissertation work on developing data-adaptive methods for analyzing genome-wide association studies (GWAS) using longitudinal data. The presentation includes background on GWAS and longitudinal data analysis, the overall study design involving simulation studies and application to a real dataset, and three proposed journal articles describing novel methods for SNP-set and pathway-based association tests. The goal of the work is to develop more powerful statistical approaches for detecting genetic associations using the additional information from longitudinal phenotypes in GWAS.
Peter Yun-Shao Sung is a computer science professional with a MS in computer science from NYU and a ME in biomedical engineering from Cornell University. He has over 10 years of experience in bioinformatics analysis at Memorial Sloan-Kettering Cancer Center and has published over 30 papers identifying genomic signatures related to sarcoma development. His technical skills include Python, C++, JavaScript, and machine learning algorithms like CNNs, RNNs, and LSTMs.
The document is a curriculum vitae for Dr. Dharmesh P. Raykundaliya, an Assistant Professor in the Department of Statistics at Sardar Patel University in India. It summarizes his educational background, which includes a Ph.D. in Reliability-Oriented Designs from Sardar Patel University, as well as his work experience teaching statistics and conducting research projects. It also lists his publications, research interests, awards, and involvement in conferences related to statistics.
The Inquisitive Data Scientist: Facilitating Well-Informed Data Science throu...Cagatay Turkay
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際際滷s for my talk at the VRVis Research Centre in Vienna as part of their VRVIS Forum talk series on November 8th 2018 -- https://www.vrvis.at/newsroom/events/forum/148-invited-talk-by-cagatay-turkay-the-inquisitive-data-scientist/
The talk argues the importance of being "inquisitive" as a data scientist and discusses techniques from visualisation that support this.
This document summarizes a lecture on research methodology given by Dr. Said Mirza Pahlevi. The lecture covered four main topics: 1) computer science as a discipline, 2) the nature of research, 3) types of research methodology, and 4) characteristics and roles of research. The lecture defined computer science, discussed what constitutes research, described quantitative, qualitative and design research methods, and outlined the roles of different types of researchers.
Affect- and Personality-based Recommender Systems Part II: Acquisition, Usage...Marko Tkali
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This document summarizes a presentation on acquiring and using emotions and personality in recommender systems. It discusses how emotions and personality can be measured from online behaviors like social media usage. Personality detection methods are presented that use features from Twitter, Facebook, and Instagram. The document also outlines how acquired personality and emotion data can be used in recommender systems, such as for mood regulation, matching browsing styles, and addressing the new user problem through similarity measures. Measurement of emotions from modalities like language, visual cues, and physiology is also summarized.
ICCSS2015 talk: Null model for meme popularityJames Gleeson
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The document presents a model of how memes compete for popularity when spreading over social networks. The model accounts for the effects of human memory timescales and network structure on meme diffusion. It shows that competition between memes for limited user attention can induce critical behavior, producing power law popularity distributions and linear popularity growth over time. Comparison to empirical data demonstrates the model provides a useful null model for understanding how memory, networks and competition influence meme popularity.
Multi-target prediction (MTP) involves simultaneously predicting multiple target outputs from diverse data types using input features. In contrast to single-target prediction which predicts a single output, MTP can handle tasks like multivariate regression, multi-task learning, and multi-label classification. MTP can be improved by incorporating side information, such as representations of target molecules or category tags of documents. Side information is important for generalizing to new, unobserved targets. MTP shows potential for diagnosing depression, anxiety, and stress using the DASS-21 scale from both structured and unstructured data sources.
Kernel-based machine learning methods are for predicting the structured, non-tabular data arising in biomedicine and digital health, especially applications on small molecules such as drugs and metabolites.
How to use science maps to navigate large information spaces? What is the lin...Andrea Scharnhorst
油
A. Scharnhorst (2016) Wie k旦nnen Wissenschaftskarten zur Suche in grossen Informationsr辰umen eingesetzt werden? How to use science maps to navigate large information spaces? What is the link between science maps and predictive models of science? Invited lecture Fraunhofer-Institut f端r Naturwissenschaftlich-Technische Trendanalysen, Euskirchen, Germany, December 7, 2016
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...Hakka Labs
油
Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occams razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
This curriculum vitae summarizes Nicola Amoroso's education and professional experience. He holds a PhD in Physics from 2014 with a thesis on quantitative MRI analysis in Alzheimer's disease. His postdoctoral research has focused on developing cloud computing solutions to support neuroimaging data analysis. He has published over 10 papers in peer-reviewed journals on topics including hippocampal segmentation, machine learning applications for brain disease detection, and complex network analysis of neuroimaging data.
Benjamin E. Deonovic is a Ph.D. candidate in Biostatistics at the University of Iowa, expected to graduate in May 2017. His research interests include Bayesian modeling, MCMC, and statistical methods applied to genomics and bioinformatics. He has published papers on haplotype phasing and allele-specific expression using hybrid sequencing data, and has presented his research at several conferences. Deonovic has worked as a research assistant at the University of Iowa on projects involving genetic association studies, pathway analysis, and joint modeling of sequencing data.
Interactive Visualization Systems and Data Integration Methods for Supporting...Don Pellegrino
油
This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
This document provides an introduction to multivariate statistics. It begins with background on the Indian Statistical Institute where the author is located. It then discusses some common myths about multivariate statistics, defining it as the analysis of relationships between sets of variables. The document lists several multivariate statistical tools and provides examples of research questions they could address related to women and child development. It also summarizes some published studies utilizing multivariate techniques like principal component analysis, correspondence analysis, cluster analysis, and MANOVA.
Roland Matsouaka is an Assistant Professor of Biostatistics and Bioinformatics at Duke University Medical Center. He received his PhD in Biostatistics from Harvard School of Public Health in 2012. His research interests include model selection and validation, risk adjustment, biomarker evaluation, and causal inference methods. He has over 10 publications in peer-reviewed journals and has served as a co-investigator on several grants related to cardiovascular disease.
Understanding medical concepts and codes through NLP methodsAshis Chanda
油
This document summarizes two projects aimed at improving representations of medical concepts and codes through natural language processing of clinical notes. The first project uses an external knowledge graph to enhance skip-gram embeddings of medical concepts. The second project jointly learns representations of medical codes and words from clinical notes using a modified skip-gram model that considers relationships between codes, words, and codes and words. The document concludes by discussing future directions, including applying these methods to other domains and using more recent pre-trained language models like clinical BERT.
The document discusses learning analytics and cognitive automation, and their implications for education. It begins by outlining how cognitive automation is automating routine cognitive work. This will impact learning analytics, as analytics aggregate lower-level data and AI automates routine cognitive tasks. As a result, humans must focus on higher-order skills like creativity, ethics, resilience and curiosity. The document then provides examples of learning analytics research focusing on dispositions, teamwork and learning beyond the classroom. It argues analytics could assess holistic development if they evaluate integration of knowledge, skills and dispositions over time.
Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics
Innovating Pedagogical Practices through Professional Development in Computer Science Education
Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data
Enhancing Human-Machine Interaction: Real-Time Emotion Recognition through Speech Analysis
Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework
This doctoral dissertation defense presentation summarizes Yang Yang's dissertation work on developing data-adaptive methods for analyzing genome-wide association studies (GWAS) using longitudinal data. The presentation includes background on GWAS and longitudinal data analysis, the overall study design involving simulation studies and application to a real dataset, and three proposed journal articles describing novel methods for SNP-set and pathway-based association tests. The goal of the work is to develop more powerful statistical approaches for detecting genetic associations using the additional information from longitudinal phenotypes in GWAS.
Peter Yun-Shao Sung is a computer science professional with a MS in computer science from NYU and a ME in biomedical engineering from Cornell University. He has over 10 years of experience in bioinformatics analysis at Memorial Sloan-Kettering Cancer Center and has published over 30 papers identifying genomic signatures related to sarcoma development. His technical skills include Python, C++, JavaScript, and machine learning algorithms like CNNs, RNNs, and LSTMs.
The document is a curriculum vitae for Dr. Dharmesh P. Raykundaliya, an Assistant Professor in the Department of Statistics at Sardar Patel University in India. It summarizes his educational background, which includes a Ph.D. in Reliability-Oriented Designs from Sardar Patel University, as well as his work experience teaching statistics and conducting research projects. It also lists his publications, research interests, awards, and involvement in conferences related to statistics.
The Inquisitive Data Scientist: Facilitating Well-Informed Data Science throu...Cagatay Turkay
油
際際滷s for my talk at the VRVis Research Centre in Vienna as part of their VRVIS Forum talk series on November 8th 2018 -- https://www.vrvis.at/newsroom/events/forum/148-invited-talk-by-cagatay-turkay-the-inquisitive-data-scientist/
The talk argues the importance of being "inquisitive" as a data scientist and discusses techniques from visualisation that support this.
This document summarizes a lecture on research methodology given by Dr. Said Mirza Pahlevi. The lecture covered four main topics: 1) computer science as a discipline, 2) the nature of research, 3) types of research methodology, and 4) characteristics and roles of research. The lecture defined computer science, discussed what constitutes research, described quantitative, qualitative and design research methods, and outlined the roles of different types of researchers.
Affect- and Personality-based Recommender Systems Part II: Acquisition, Usage...Marko Tkali
油
This document summarizes a presentation on acquiring and using emotions and personality in recommender systems. It discusses how emotions and personality can be measured from online behaviors like social media usage. Personality detection methods are presented that use features from Twitter, Facebook, and Instagram. The document also outlines how acquired personality and emotion data can be used in recommender systems, such as for mood regulation, matching browsing styles, and addressing the new user problem through similarity measures. Measurement of emotions from modalities like language, visual cues, and physiology is also summarized.
Affect- and Personality-based Recommender Systems Part II: Acquisition, Usage...Marko Tkali
油
CV Chien-Wei Lin
1. Chien-Wei (Masaki) Lin
1
CHIEN-WEI (MASAKI) LIN
2715 MURRAY AVE., APT 607, PITTSBURGH, PA 15217
: (412)-944-7488 : masaki396@gmail.com
http://www.pitt.edu/~chl169/
Education
University of Pittsburgh Pittsburgh, PA, US
Ph.D. in Biostatistics (GPA: 3.96/4.00) Sep 2012 Expected in Jun 2017
- Dissertation: Power calculation and Experimental design in
RNA-Seq and Methyl-Seq
- Advisor: George C. Tseng
National Chiao Tung University Hsinchu, Taiwan
M.S. in Statistics (GPA: 4.00/4.00) Sep 2005 Jun 2007
- Dissertation: A goodness-of-fit test for Archimedean copula
models in the presence of right censoring
- Advisor: Weijing Wang
National Tsing Hua University Hsinchu, Taiwan
B.S. in Applied Mathematics Sep 2000 Jun 2005
Research Interest
My research interest is in both methodology and collaborative research. I have many collaboration
experiences with researchers in psychiatric field, specifically focus on brain disorders. I have been inspired by
many of those interesting biological questions and developed statistical methods to solve real-world
problems. I am particularly interested in working on high-throughput multi-omics data and developing
integrative methods to identify disease-related biomarkers and to solve supervised/unsupervised statistical
machine learning problems. I am also interested in techniques about how to handle Big Data (e.g., Apache
Spark) and topics in integrating multi-omics data and brain imaging data.
Scholarship and Award
International Biometric Society Eastern North American Region (ENAR)
Distinguished Student Paper Award 2016
- SeqDesign: A framework for RNA-Seq genome-wide power
calculation and experimental design issues.
University of Pittsburgh
Best ENAR Presentation Award 2014/2016
Health Disparities Poster Competition Award 2015
Statistical and Applied Mathematical Sciences Institute (SAMSI)
Challenges in Computational Neuroscience (CCNS) Transition Workshop Travel Award 2016
- Exchange and connect results spanned by the CCNS program
CCNS Opening Workshop Travel Award 2015
- Overview of methodology and applications in computational neuroscience
CCNS Summer School Travel Award 2015
- An in-depth course training in computation neuroscience
Professor Chen Wen-Chens Memorial Foundation
Chen Wen-Chen Memorial Scholarship in Statistical Sciences 2007
2. Chien-Wei (Masaki) Lin
2
The National Tsing Hua University/The National Chiao Tung University
Academic Achievement Award 2003/2006
Publication
Methodology........
Power and Sample Size Calculation in NGS Data
[1] Lin, C.-W.*
, Liao, G.*
, Lee, M. L. T., Park, Y., & Tseng, G. C. (2017). SeqDesign: A framework for RNA-Seq
genome-wide power calculation and experimental design issues. Submitted to JASA.
Meta-analysis and Integrative Analysis
[2] Kim, S., Lin, C.-W., & Tseng, G. C. (2016). MetaKTSP: A Meta-Analytic Top Scoring Pair Method for
Robust Cross-Study Validation of Omics Prediction Analysis. Bioinformatics, 32(March), btw115.
[3] Yang, H.-C., Lin, C.-W., Chen, C.-W., & Chen, J. (2014). Applying genome-wide gene-based expression
quantitative trait locus mapping to study population ancestry and pharmacogenetics. BMC Genomics,
15(1), 319.
[4] Yang, H.-C., Wang, P.-L., Lin, C.-W., Chen, C.-H., & Chen, C.-H. (2012). Integrative analysis of single
nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related
ethnic populations. BMC Genomics, 13(1), 346.
Statistical Metabolomics
[5] Liang, Y. J., Lin, Y. T., Chen, C. W., Lin, C. W., Chao, K. M., Pan, W. H., & Yang, H. C. (2016). SMART:
Statistical Metabolomics Analysis - An R Tool. Analytical Chemistry, 88(12), 63346341.
Bioinformatics
[6] Yang, H.-C., Lin, H.-C., Kang, M., Chen, C.-H., Lin, C.-W., Li, L.-H., Pan, W.-H. (2011). SAQC: SNP array
quality control. BMC Bioinformatics, 12, 100.
Survival Analysis
[7] Emura, T., Lin, C. W., & Wang, W. (2010). A goodness-of-fit test for Archimedean copula models in the
presence of right censoring. Computational Statistics and Data Analysis, 54(12), 30333043.
Game Theory
[8] Wang, B. C.*
, Lin, C. W.*
, Chen, K. T., & Chen, L. J. (2012). An analytical model for generalized ESP
games. Knowledge-Based Systems, 34, 114127.
Collaboration/Application..................................................................................................................
[9] McKinney, B. C.*
, Lin, C.-W.*
, Oh, H., Tseng, G. C., Lewis, D. A., & Sibille, E. (2017). DNA Methylation in
the Human Frontal Cortex Reveals a Putative Mechanism for Age-by-Disease Interactions. Submitted to
Biological Psychiatry.
[10] Belzeaux, R., Lin, C.-W., Ding, Y., Bergon, A., Ibrahim, E. C., Turecki, G., Sibille, E. (2016).
Predisposition to treatment response in major depressive episode: A peripheral blood gene
coexpression network analysis. Journal of Psychiatric Research, 81, 119126.
[11] Diniz, B. S., Reynolds, C. F., Sibille, E., Lin, C.-W., Tseng, G., Lotrich, F., Butters, M. A. (2016).
Enhanced Molecular Aging in Late-Life Depression: the Senescent Associated Secretory Phenotype. The
American Journal of Geriatric Psychiatry.
[12] Grubisha, M. J.*
, Lin, C.-W.*
, Tseng, G. C., Penzes, P., Sibille, E., & Sweet, R. A. (2016). Age-dependent
increase in Kalirin-9 and Kalirin-12 transcripts in human orbitofrontal cortex. European Journal of
Neuroscience, 44(7), 24832492.
[13] Diniz, B. S., Lin, C. W., Sibille, E., Tseng, G., Lotrich, F., Aizenstein, H. J., Butters, M. A. (2016).
Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach
to understanding LLD pathophysiology. Journal of Psychiatric Research, 82, 17.
[14] McKinney, B. C., Lin, C.-W., Oh, H., Tseng, G. C., Lewis, D. A., & Sibille, E. (2015). Hypermethylation of
BDNF and SST Genes in the Orbital Frontal Cortex of Older Individuals: A Putative Mechanism for
Declining Gene Expression with Age. Neuropsychopharmacology: Official Publication of the American
College of Neuropsychopharmacology, 40(11), 260413.
3. Chien-Wei (Masaki) Lin
3
[15] Lin, C. W., Chang, L. C., Tseng, G. C., Kirkwood, C. M., Sibille, E. L., & Sweet, R. A. (2015). VSNL1 co-
expression networks in aging include calcium signaling, synaptic plasticity, and Alzheimers disease
pathways. Frontiers in Psychiatry, 6(MAR), 30.
[16] Nikolova, Y. S., Iruku, S. P., Lin, C.-W., Conley, E. D., Puralewski, R., French, B., Sibille, E. (2015).
FRAS1-related extracellular matrix 3 (FREM3) single-nucleotide polymorphism effects on gene
expression, amygdala reactivity and perceptual processing speed: An accelerated aging pathway of
depression risk. Frontiers in Psychology, 6(September), 1377.
[17] Chang, L. C., Jamain, S., Lin, C. W., Rujescu, D., Tseng, G. C., & Sibille, E. (2014). A conserved BDNF,
glutamate- and GABA-enriched gene module related to human depression identified by coexpression
meta-analysis and DNA variant genome-wide association studies. PLoS ONE, 9(3), e90980.
Methodology in Preparation.......................................................................................................................................................................
[18] Lin, C.-W.*
, Liu, P.*
, Park, Y., & Tseng, G. C. (2017). MethylSeqDesign: A framework for Methyl-Seq
genome-wide power calculation and experimental design issues.
[19] Lin, C. W.*
, Cahill, K. M.*
, & Tseng, G. C. (2017) Meta analytic framework of tree methods.
[20] Lin, C. W.*
, Zeng, X.*
, & Tseng, G. C. (2017) Parameter estimation scheme in clustering algorithms via
stability analysis.
[21] Ma, T., Huo, Z., Kuo, A., Zeng, X., Zhu, L., Fang, A., Wang, L., Lin, C. W., Rahman, T., Liu, S., Park, Y., Kim,
S., Li, J., Chang, L. C., Song, C., & Tseng, G. C. (2017) MetaOmics - a Comprehensive Software Suite with
Interactive Visualization for Transcriptomic Meta-Analysis.
[22] Zeng, X., Fang, A., Lin, C. W., Ma, T., & Tseng, G. C. (2017) Comparative Pathway Integrator: a
framework of meta-analytic integration of multiple transcriptomic studies for consensual and
differential pathway analysis.
Collaboration/Application in Preparation...........................................................
[23] Lin, C.-W., Chang, L. C., Ma, T., Oh, H., Tseng, G. C., Lewis, D. A., & Sibille, E. (2017). Genetic Modulation
of Brain Molecular Aging.
Conference Proceedings
[24] Lin, C. W., Chen, K. T., Chen, L. J., King, I., & Lee, J. H. M. (2008). An analytical approach to optimizing
the utility of ESP games. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web
Intelligence, WI 2008 (pp. 184187).
Working Experience
Academia Sinica Taipei, Taiwan
Research Assistant in Institute of Statistical Science Mar 2009 July 2012
- Advisor: Hsin-Chou Yang
Research Assistant in Institute of Information Science Oct 2007 Mar 2009
- Advisor: Sheng-Wei Chen (a.k.a. Kuan-Ta Chen)
*Remark: The working period from Oct 2007 to Oct 2011 was a replacement of required military service.
Teaching Experience
Guest Lecturer..
BIOST 2094 (Biostatistics, University of Pittsburgh)
Statistical Computing in R/Advanced R Computing Mar 2015/Spring 2017
- Numerical methods in optimization problems, ggplot2,
Graphical interface: tcltk, shiny.
BIOST 2055/HUGEN 2070 (University of Pittsburgh)
Introductory High-throughput Genomic Data Analysis I: Oct 2014/Mar 2015
Data Mining and Applications/Bioinformatics in Human Genetics
4. Chien-Wei (Masaki) Lin
4
- Differential and isoform analysis of RNA-Seq data.
- Served as Teaching Assistant in BIOST 2055.
Oral and Poster Presentations
Oral Presentation...
ENAR Baltimore, MD/Austin, TX
SeqDesign: A framework for RNA-Seq genome-wide power Mar 2014/Mar 2016
calculation and experimental design issues. Contributed.
Poster Presentation.
Deans Days Competition, GSPH, University of Pittsburgh Pittsburgh, PA
DNA Methylation in the Human Frontal Cortex Reveals a Apr 2016
Putative Mechanism for Age-by-Disease Interactions.
Pittsburgh ASA banquet Pittsburgh, PA
SeqDesign: A framework for RNA-Seq genome-wide power Apr 2015/Mar 2016
calculation and experimental design issues.
Joint Statistical Meetings (JSM) Seattle, WA
Comparative Pathway Integrator: a framework of meta- Aug 2015
of multiple transcriptomic studies for
consensual and differential pathway analysis.
Health Disparities Poster Competition, University of Pittsburgh Pittsburgh, PA
Genetic Modulation of Brain Molecular Aging. Apr 2015
Deans Days Competition, GSPH, University of Pittsburgh Pittsburgh, PA
Genetic Modulation of Brain Molecular Aging. Apr 2013/Apr 2014/Apr 2015
ICHG and ASHG Annual Meeting Montreal, Canada
An integrative pathway analysis using gene expression, Oct 2011
single-nucleotide polymorphism and environmental
factor successfully predicts disease status of hypertension.
(Presented by Dr. Hsin-Chou Yang)
Skills
Statistical Software
Proficient in R, SAS, Stata
Bioinformatics Tools
NCBI database, FASTSNP, Bioconductor, UCSC Genome Browser, Ensemble
Database, HELIXTREE, PARTEK, SAS GENETICS
Operating System
Windows, Mac OS, Linux
Languages
Proficient in Chinese, Japanese, English