Human body-fluid proteome: quantitative profiling and computational prediction.

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

DeepSec: a deep learning framework for secreted protein discovery in human body fluids.

MOTIVATION: Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great progress in protein discovery in various human fluids. However, due to the large number of proteins and diverse modifications present in the fluids, as well as the existing technical limits of major proteomics platforms (e.g. mass spectrometry), large discrepancies are often generated from different experimental studies. As a result, a comprehensive proteomics landscape across major human fluids are not well determined. RESULTS: To bridge this gap, we have developed a deep learning framework, named DeepSec, to identify secreted proteins in 12 types of human body fluids. DeepSec adopts an end-to-end sequence-based approach, where a Convolutional Neural Network is built to learn the abstract sequence features followed by a Bidirectional Gated Recurrent Unit with fully connected layer for protein classification. DeepSec has demonstrated promising performances with average area under the ROC curves of 0.85-0.94 on testing datasets in each type of fluids, which outperforms existing state-of-the-art methods available mostly on blood proteins. As an illustration of how to apply DeepSec in biomarker discovery research, we conducted a case study on kidney cancer by using genomics data from the cancer genome atlas and have identified 104 possible marker proteins. AVAILABILITY: DeepSec is available at https://bmbl.bmi.osumc.edu/deepsec/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest.

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.

HBFP: a new repository for human body fluid proteome.

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery. Database URL: https://bmbl.bmi.osumc.edu/HBFP/.