Adaptive resistance to PI3Kα-selective inhibitor CYH33 is mediated by genomic and transcriptomic alterations in ESCC cells.

Phosphoinositide-3 kinase alpha-specific inhibitors (PI3Kαi) displayed promising potential for the treatment of esophageal squamous cell carcinoma (ESCC) with frequent activation in PI3K signaling. However, acquired resistance is likely to develop and limit the efficacy of PI3Kαi like other targeted therapies. To identify genomic adaptation to PI3Kαi, we applied whole-genome sequencing and detected gene mutation and amplification in four lines of ESCC cells established with adapted resistance to a novel PI3Kαi CYH33.

Identifying circulating miRNA biomarkers for early diagnosis and monitoring of lung cancer.

Liquid biopsy refers to the sampling, screening, and detecting potential biomarkers in unique liquid samples for clinical use. Lung cancer is one of the most highly frequent cancer subtypes, which is hard to be early diagnosed and monitored by radiological and histopathological evaluation that are the most general and accurate methods. Circulating miRNA is a potential clinical examination index for tumor detection and monitoring tumorigenesis progression using liquid biopsy.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC and LSC cells.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes.

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels.

Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects.

A computational method using the random walk with restart algorithm for identifying novel epigenetic factors.

Epigenetic regulation has long been recognized as a significant factor in various biological processes, such as development, transcriptional regulation, spermatogenesis, and chromosome stabilization. Epigenetic alterations lead to many human diseases, including cancer, depression, autism, and immune system defects. Although efforts have been made to identify epigenetic regulators, it remains a challenge to systematically uncover all the components of the epigenetic regulation in the genome level using experimental approaches.

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease.

As a chronic illness derived from hair cells of the inner ear, Menière's disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes.

An integrated method for the identification of novel genes related to oral cancer.

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes.

Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach.

Identification of disease genes is a hot topic in biomedicine and genomics. However, it is a challenging problem because of the complexity of diseases. Inflammatory bowel disease (IBD) is an idiopathic disease caused by a dysregulated immune response to host intestinal microflora.

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life.

Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach.

Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC.

Determination of Trace Elements in Edible Nuts in the Beijing Market by ICP-M.

Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of the elemental distributions in nuts is not only necessary in evaluating the total dietary intake of the essential elements, but also useful in detecting heavy metal contamination in food. The aim of this study was to determine the mineral contents in edible nuts, and to assess the food safety of nuts in the Beijing market.

Classifying ten types of major cancers based on reverse phase protein array profiles.

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method.

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues.

Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein-protein interaction network.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and one of the deadliest cancers in Asia. But at present, effective targets for HCC clinical therapy are still limited. The "guilt by association" rule suggests that interacting proteins share the same or similar functions and hence may be involved in the same pathway.

Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network.

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network.

Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network.

Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention.

Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches.

Longevity is one of the most basic and one of the most essential properties of all living organisms. Identification of genes that regulate longevity would increase understanding of the mechanisms of aging, so as to help facilitate anti-aging intervention and extend the life span. In this study, based on the network features and the biochemical/physicochemical features of the deletion network and deletion genes, as well as their functional features, a two-layer model was developed for predicting the deletion effects on yeast longevity.

Nucleosome positioning based on the sequence word composition.

The DNA of all eukaryotic organisms is packaged into nucleosomes (a basic repeating unit of chromatin). A nucleosome consists of histone octamer wrapped by core DNA and linker histone H1 associated with linker DNA. It has profound effects on all DNA-dependent processes by affecting sequence accessibility.

Selection of reprogramming factors of induced pluripotent stem cells based on the protein interaction network and functional profiles.

Induced pluripotent stem cells have displayed great potential in disease investigation and drug development applications. However, selection of reprogramming factors in each cell type or disease state is both expensive and time consuming. To deal with this kind of situation, a fast computational framework was developed by optimize the reprogramming factors via the protein interaction network and gene functional profiles.

Classification of transcription factors using protein primary structure.

The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures.

Prediction of interactiveness of proteins and nucleic acids based on feature selections.

It is important to identify which proteins can interact with nucleic acids for the purpose of protein annotation, since interactions between nucleic acids and proteins involve in numerous cellular processes such as replication, transcription, splicing, and DNA repair. This research tries to identify proteins that can interact with DNA, RNA, and rRNA, respectively. mRMR (Minimum redundancy and maximum relevance), with its elegant mathematical formulation, has been applied widely in processing biological data and feature analysis since its introduction in 2005.

Temporal and parental-specific expression of imprinted genes in a newly derived Chinese human embryonic stem cell line and embryoid bodies.

Although the study of imprinted genes in human development is very important, little is known about their expression and regulation in the early differentiation of human tissues due to lack of an appropriate model. In this study, a Chinese human embryonic stem (hES) cell line, SHhES1, was derived and fully characterized. Expression profiles of human imprinted genes were determined by Affymetrix Oligo micro-array in undifferentiated SHhES1 cells and SHhES1-derived embryoid bodies (EBs) at day 3, 8, 13 and 18.