Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.
Amino acid mutations may have diverse effects on protein structure and function. Thus reliable information about the protein sequence variations is essential to gain insights into disease genotype-phenotype correlations. With the recent availability of the complete genome sequence and the accumulation of variation data, determining the effects of amino acid substitution will be the next challenge in mutation research. The molecular consequences of amino acid mutations can readily be predicted by numerous bioinformatic methods, which analyze the mutation effects from different points of view. In this review, these approaches are categorized according to their analysis principles. The applicability of these tools for inference of mutation-structure-function relationship is also recapitulated. When the human diseases are likely to involve defects in multiple genes, most of the current mutation analysis focuses on single point mutation and lacks an expansive proteome-wide perspective. We propose in this review the application of the existing computational tools in the analysis of correlated mutations at a system level. Directions for future developments and implications are discussed, which will help to understand the networks underlying human disease.
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
In this paper, a maneuver decision making method for autonomous vehicle in complex urban environment is studied. We decompose the decision making problem into three steps. The first step is for selecting the logical maneuvers, in the second step we remove the maneuvers which break the traffic rules. In the third step, Multiple Attribute Decision Making (MADM) methods such as Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used in the process of selecting the optimum driving maneuver in the scenario considering safety and efficiency. AHP is used for obtaining the weights of attributes, TOPSIS is responsible for calculating the ratings and ranking the alternatives. Road test indicates that the proposed method helps the autonomous vehicle to make reasonable decisions in complex environment. In general, the experiment results show that this method is efficient and reliable.
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.
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