BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of "integrative genetics". Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

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Multi-omics approaches to disease.

The biological rationale for targeting protein–protein interactions as a therapeutic strategy is strong, but identifying viable small-molecule drugs to achieve this has proved highly challenging. This article uses examples of successful discovery efforts to illustrate the research strategies that have proved most useful for different classes of protein–protein interactions.

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Small molecules, big targets: drug discovery faces the protein–protein interaction challenge

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

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Deciphering cell-cell interactions and communication from gene expression.

Super-Resolution Microscopy: From Single Molecules to Supramolecular Assemblies

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

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Protein-Protein Interaction Detection: Methods and Analysis

Drug discovery process is a critical issue in the pharmaceutical industry since it is a very cost-effective and time consuming process to produce new drug potentials and enlarge the scope of diseases incurred. Drug target identification, being the first phase in drug discovery is becoming an overly time consuming process. In many cases, such produces inefficient results due to failure of conventional approaches like in vivo and in vitro to investigate large scale data. Sophisticated in silico approaches has given a tremendous opportunity to pharmaceutical companies to identify new potential drug targets which in turn affect the success and time of performing clinical trials for discovering new drug targets. The main goal of this work is to review in silico methods for drug discovery process with emphasis on identifying drug targets, where there are genes or proteins associated with specific diseases. This review provides a succinct overview of several recent approaches that employ bioinformatics for the systematic characterization of the targets of bioactive compounds.

	 

	Key words: In silico, infectious vector, drug, pharmaceutical industry.

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Modern drug discovery process: An in silico approach

Alzheimer's disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer's disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.

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Protein interaction network for Alzheimer's disease using computational approach

Noises are the unwanted information in an image, so they should be removed before further processing. Existing methods consider histogram based analysis which is globally varied one. A modified statistical measured based automatic noise type recognition technique is proposed in this paper. This has 2 phases including training phase and testing phase. The key role involves deduction of noise samples using filters like wiener, lee, median and extracts the statistical measures like kurtosis and skewness from samples. Kurtosis and skewness values exhibit behavior based on noise type. By using the statistical information and trained data we can classify the type of noise. Finally the noise type is identified and corresponding filter is applied. Thus noise eliminated image would give the desirable results during further processing. Experimental results show that the method is capable of accurately classifying the types of noise.

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V. Srinivasa Rao

Papers

Protein-Protein Interaction Detection: Methods and Analysis

Modern drug discovery process: An in silico approach

Protein interaction network for Alzheimer's disease using computational approach

Enhanced Noise Type Recognition Using Statistical Measures