謝立青
Li-Ching Hsieh

生活照

Name
謝立青
Li-Ching Hsieh
Title
助理教授
Assistant Professor
Department
基因體暨生物資訊所
Inst. of Genomics & Bioinformatics
Tel
Tel: 886-4-22840338 Ext. 7091
Fax: 886-4-22859329
Email
liching@dragon.nchu.edu.tw
FAX
防檢疫大樓709室
Office address
Health Insp.& Quar. Bldg. R709
個人資料 Personal Information
學歷 Education
  • 最高學歷:國立中央大學  物理  博士
  • --------- EDUCATION ---------
  • National Central University, Ph.D. in Physics (1999-2003)
  • National Chung Hsing University, M.Sc. in Physics (1995)
  • National Chung Hsing University, B.Sc. in Physics (1993)

經歷 Experience
  • 現職
  • --------- CURRENT POSITION ---------
  • Assistant Professor, Inst. of Genomics and Bioinformatics, National Chung Hsing Univ. (2011/02-present)
  • --------- WORK EXPERIENCE ---------
  • Visiting Scholar, Dept. of Ecology and Evolution, Univ. of Chicago, USA (2004/12-2005/11)
  • Postdoctoral Researcher, Genomics Research Center and Inst. of Information Science, Academia Sinica (2004/08-2011/01)
  • Postdoctoral Researcher, Dept. of Physics, National Central Univ. (2003/08-2004/07)

研究領域 Research and Intersets
  • Evolution of upstream regulatory networks of microRNAs
  • Evolution of upstream regulatory networks of microRNAs MicroRNAs (miRNAs) regulate the expression of target genes and are involved in the control of a variety of physiological and developmental processes in multicellular organisms. Although intensive studies have been focused on miRNA downstream gene regulation, relatively little is known regarding the upstream regulation of miRNA genes. More and more high-throughput transcriptome sequencing data and transcription-factor (TF) associated ChIP-chip and ChIP-seq data sets are available for diverse organisms. We are developing a new approach to utilize the various types of data in the public domain and artificial intelligence techniques such as machine learning to identify a reliable set of the upstream regulators of miRNAs in each model organism of interest. We then can employ the set of miRNA TFs to study the evolution of the upstream regulatory networks of miRNAs.
  • MicroRNA target gene prediction
  • Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA–target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we are developing an online application to predict a reliable relationship between miRNAs and their targets using a supervised statistical model trained on numerous features used in these prediction tools.
  • Evolution of hemagglutinin (HA) in influenza A
  • For understanding influenza A/H3N2 virus evolution and facilitating vaccine strain prediction, several studies have been conducted to determine which sites on the HA1 domain of the hemagglutinin (HA) are under positive selection pressure. A number of positive selection sites have been identified by using phylogenetic-tree-based methods. However, many substitutions fixed in the population did not occur on these sites and most of the substitutions lead to simultaneous (parallel) multiple amino acid fixations. Theoretically, evolution may occur without positive selection, called hitchhiking. Therefore, whether or not the majority of these parallel substitutions were due to hitchhiking is still controversial. We are developing a frequency-based method without utilizing a phylogenetic tree to estimate the selection pressure acting at each single amino acid site of the H3HA1 and are studying what mechanism plays a major role in influenza A hemagglutinin evolution.
  • --------- 研究領域 --------
  • 微RNA (microRNAs) 上游調控網路的演化
  • .微RNA出現在多細胞生物中,它們調控目標基因的表現,也牽涉了各類的生物生理與發育的過程。有大量的研究聚焦在微RNA對於下游基因的調控,但對於微RNA本身的調控所知相對很少。有越來越多不同物種的高通量轉錄物組 (transcriptome) 的解序資料與轉錄因子相關的ChIP-chip 和 ChIP-seq 資料可被利用。我們正在發展一個新的方法,利用這些公開的資料與人工智慧如機械學習的方法在每個感興趣的模式物種中鑑別出一組可信的微RNA上游調控因子。然後我們將利用這些資料來研究微RNA的上游調控網路的演化。
  • 微RNA (microRNAs) 目標基因的預測
  • 預測微RNA及其目標基因之間的關係對於研究和臨床應用至關重要。已經有許多預測微RNA及其目標基因的相互作用被開發出來,但是不同的預測工具之間的常有不一致的結果導致使用者感到困惑。為了解決這個問題,我們正在開發一個網站整合了這些預測工具不同結果所得到的許多性質,再利用機器學習統計模型進行訓練以預測更可靠的微RNA及其目標基因之間的關係。
  • A 型流行性感冒病毒血凝素 (hemagglutinin) 的演化
  • 為了瞭解A型流感H3N2亞型病毒的演化與預測疫苗株,若干研究已經預測了病毒血凝素HA1 結構域 (domain) 上受到正向天擇 (positive selection) 的位點。這些研究利用以親緣關係樹為基礎的方法 (phylogenetic-tree-based methods) 鑑別出了不少正向天擇的位點。然而另外有許多發生序列置換 (substitutions) 而且在病毒族群中已經固定 (fixation) 的位點卻不包括在這些研究的結果中,而且這些置換大多是同時(平行)到達固定。理論上,演化也有可能不經由正向天擇而發生,稱為牽連效應 (hitchhiking)。因此這些平行置換位點的形成原因是否主要為牽連效應仍然存在著爭論。我們正發展一套基於位點的氨基酸頻率的方法並且不引用親緣關係樹的資訊來估量H3HA1 每一個氨基酸位點的天擇壓力,而且也研究何種機制在A型流感血凝素的演化扮演了主要的角色。