A5028190064- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Acute Myeloid Leukemia Research
- Computational Drug Discovery Methods
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- Single-cell and spatial transcriptomics
- RNA modifications and cancer
- RNA Research and Splicing
- MicroRNA in disease regulation
- Epigenetics and DNA Methylation
- Ferroptosis and cancer prognosis
- Genetics, Bioinformatics, and Biomedical Research
- Extracellular vesicles in disease
- Molecular Biology Techniques and Applications
- Cancer Immunotherapy and Biomarkers
- Cell Image Analysis Techniques
- Immune responses and vaccinations
- Radiomics and Machine Learning in Medical Imaging
- Myeloproliferative Neoplasms: Diagnosis and Treatment
- Circular RNAs in diseases
- Genetic Associations and Epidemiology
- Histone Deacetylase Inhibitors Research
- Immune cells in cancer
- RNA and protein synthesis mechanisms
UPMC Hillman Cancer Center
2022-2025
University of Pittsburgh
2022-2025
The University of Texas Health Science Center at San Antonio
2015-2024
University of Pittsburgh Medical Center
2024
Children's Cancer Center
2021
The University of Texas Health Science Center at Houston
2017-2021
Taichung Veterans General Hospital
2017-2019
National Taiwan University
2012-2017
National Taiwan University Hospital
2014
Background Many prediction tools for microRNA (miRNA) targets have been developed, but inconsistent predictions were observed across multiple algorithms, which can make further analysis difficult. Moreover, the nomenclature of human miRNAs changes rapidly. To address these issues, we developed a web-based system, miRSystem, converting queried to latest annotation and predicting function miRNA by integrating target gene function/pathway analyses. Results First, IDs converted annotated version...
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screened for response thousand human cancer cell lines to wide collection anti-cancer drugs and illuminated link between cellular genotypes vulnerability. However, due essential differences tumors, date translation predicting in tumors remains challenging. Recently, advances deep learning have revolutionized...
Abstract Background Precise prediction of cancer types is vital for diagnosis and therapy. Through a predictive model, important marker genes can be inferred. Several studies have attempted to build machine learning models this task however none has taken into consideration the effects tissue origin that potentially bias identification markers. Results In paper, we introduced several Convolutional Neural Network (CNN) take unstructured gene expression inputs classify tumor non-tumor samples...
Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction cancers at an early stage and understanding genomic mechanisms that drive cancer development are vital to improvement treatment outcomes survival rates, thus resulting social economic impacts. Attempts have made classify types machine learning techniques during past two decades deep approaches more recently. In this paper, we established four models graph convolutional neural...
Abstract CAMILLA is a basket trial (NCT03539822) evaluating cabozantinib plus the ICI durvalumab in chemorefractory gastrointestinal cancer. Herein, are phase II colorectal cohort results. 29 patients were evaluable. 100% had confirmed pMMR/MSS tumors. Primary endpoint was met with ORR of 27.6% (95% CI 12.7-47.2%). Secondary endpoints 4-month PFS rate 44.83% 26.5-64.3%); and median OS 9.1 months 5.8-20.2). Grade≥3 TRAE occurred 39%. In post-hoc analysis RAS wild type tumors, 50% 6.3 21.5...
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's characteristics can affect its responses drugs. This field, called pharmacogenomics, is key area in the development precision oncology. Deep learning (DL) methodology has emerged as powerful technique characterize and learn from rapidly accumulating pharmacogenomics data. We introduce fundamentals typical model architectures DL. review use DL classification cancers subtypes (diagnosis treatment...
A novel deep learning method links in vitro CRISPR screens to the context of cancer genomics and translates individual tumors.
Since its selection as the method of year in 2013, single-cell technologies have become mature enough to provide answers complex research questions. With growth profiling technologies, there has also been a significant increase data collected from profilings, resulting computational challenges process these massive and complicated datasets. To address challenges, deep learning (DL) is positioned competitive alternative for analyses besides traditional machine approaches. Here, we survey...
// Ming-Kai Chuang 1, * , Yu-Chiao Chiu 2, 5, Wen-Chien Chou 3 Hsin-An Hou Mei-Hsuan Tseng Yi-Yi Kuo Yidong Chen 6 Eric Y. 4 Hwei-Fang Tien 1 Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, 2 Graduate Institute Biomedical Electronics and Bioinformatics, University, Internal Bioinformatics Biostatistics Core, Center Genomic 5 Greehey Children's Cancer Research Institute, Texas Health Science at San Antonio, Texas, United States America Epidemiology...
Bioinformatics tools have been developed to interpret gene expression data at the set level, and these based analyses improve biologists' capability discover functional relevance of their experiment design. While elucidating individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used generate unbiased combination set, determine biological analysis consistency combining by leveraging...
Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, expression data's unique characteristics challenge the computer vision-inspired design popular Learning (DL) models such as Convolutional Neural Network (CNN) and ask for need develop interpretable DL tailored transcriptomics study. To current challenges developing an model modeling data, we propose novel deep architecture called T-GEM, or Transformer Gene...
Abstract Motivation Molecular Regulatory Pathways (MRPs) are crucial for understanding biological functions. Knowledge Graphs (KGs) have become vital in organizing and analyzing MRPs, providing structured representations of complex interactions. Current tools mining KGs from biomedical literature inadequate capturing complex, hierarchical relationships contextual information about MRPs. Large Language Models (LLMs) like GPT-4 offer a promising solution, with advanced capabilities to decipher...
Advancing precision oncology requires accurate prediction of treatment response and accessible models. To this end, we present shinyDeepDR, a user-friendly implementation our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more to researchers without extensive programming experience. Using users can upload mutation and/or gene expression data from cancer sample (cell line or tumor) perform two main functions: "Find Drug," which...
Therapy resistance in breast cancer is increasingly attributed to polyploid giant cells (PGCCs), which arise through whole genome doubling and exhibit heightened resilience standard treatments. Characterized by enlarged nuclei increased DNA content, these tend be dormant under therapeutic stress, driving disease relapse. Despite their critical role resistance, strategies effectively target PGCCs are limited, largely due the lack of high-throughput methods for assessing viability. Traditional...
The ability of adult stem cells to reside in a quiescent state is crucial for preventing premature exhaustion the cell pool. However, intrinsic epigenetic factors that regulate spermatogonial quiescence are largely unknown. Here, we investigate mice how DNA methyltransferase 3-like (DNMT3L), an regulator important interpreting chromatin context and facilitating de novo methylation, sustains long-term male germ We demonstrated cell-enriched THY1+ stem/progenitor (SPCs) constituted...
Additional sex combs-like 1 (ASXL1) is frequently mutated in myeloid malignancies. Recent studies showed that hematopoietic-specific deletion of Asxl1 or overexpression mutant ASXL1 resulted myelodysplasia-like disease mice. However, actual effects a "physiological" dose remain unexplored. We established knock-in mouse model bearing the most frequent mutation and studied its pathophysiological on hematopoietic system. Heterozygotes (Asxl1 tm/+ ) marrow cells had higher vitro proliferation...
In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) establish coexpression through competition for a limited pool miRNA program. While dynamics ceRNA among cellular conditions have been verified...
Homeodomain-only protein homeobox (HOPX) is the smallest homeodomain protein. It was regarded as a stem cell marker in several non-hematopoietic systems. While prototypic genes such HOX family have been well characterized acute myeloid leukemia (AML), clinical and biological implications of HOPX disease remain unknown. Thus we analyzed global gene expression patterns 347 newly diagnosed de novo AML patients our institute. We found that higher closely associated with older age, platelet...
Abstract Mutations of the GATA binding protein 2 ( GATA2 ) gene in myeloid malignancies usually cluster zinc finger 1 (ZF1) and ZF2 domains. different locations may have distinct impact on clinico-biological features outcomes AML patients, but little is known this aspect. In study, we explored mutations 693 de novo non-M3 patients identified 44 43 (6.2%) including 31 ZF1, 10 ZF2, three outside two Different from mutations, ZF1 were closely associated with French-American-British (FAB) M1...
Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due intensive computation, however, these methods rely heavily on prior knowledge are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis systematically modulation of networks. Based a novel statistical test employing conjugate Fisher transformations...
Despite intensive studies during the last 3 years, pathology and underlying molecular mechanism of coronavirus disease 2019 (COVID-19) remain poorly defined. In this study, we investigated spatial single-cell cellular features postmortem COVID-19 lung tissues using in situ sequencing (ISS). We detected 10 414 863 transcripts 221 genes whole-slide segmented them into 1 719 459 cells that were mapped to 18 major parenchymal immune cell types, all which infected by SARS-CoV-2. Compared with...