Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart

Functional analysis of novel SNPs and mutations in human and mouse genomes

<p>Abstract</p> <p>Background</p> <p>With the flood of information generated by the new generation of sequencing technologies, more efficient bioinformatics tools are needed for in-depth impact analysis of novel genomic variations. FANS (Functional Analysis of Novel SNPs) was developed to streamline comprehensive but tedious functional analysis steps into a few clicks and to offer a carefully designed presentation of results so researchers can focus more on thinking instead of typing and calculating.</p> <p>Results</p> <p>FANS <url>http://fans.ngc.sinica.edu.tw/</url> harnesses the power of public information databases and powerful tools from six well established websites to enhance the efficiency of analysis of novel variations. FANS can process any point change in any coding region or GT-AG splice site to provide a clear picture of the disease risk of a prioritized variation by classifying splicing and functional alterations into one of nine risk subtypes with five risk levels.</p> <p>Conclusion</p> <p>FANS significantly simplifies the analysis operations to a four-step procedure while still covering all major areas of interest to researchers. FANS offers a convenient way to prioritize the variations and select the ones with most functional impact for validation. Additionally, the program offers a distinct improvement in efficiency over manual operations in our benchmark test.</p

Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes and test it on a Level IV-like monitoring system.Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV-like wearable device measuring the thoracic and abdominal movements and the SpO2.Results: With the leave-one-subject-out cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03.Conclusion: The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS

Assessment of Renal Function by the Stable Oxygen and Hydrogen Isotopes in Human Blood Plasma

Water (H2O) is the most abundant and important molecule of life. Natural water contains small amount of heavy isotopes. Previously, few animal model studies have shown that the isotopic composition of body water could play important roles in physiology and pathophysiology. Here we study the stable isotopic ratios of hydrogen (δ2H) and oxygen (δ18O) in human blood plasma. The stable isotopic ratio is defined and determined by δsample = [(Rsample/RSTD)−1] * 1000, where R is the molar ratio of rare to abundant, for example, 18O/16O. We observe that the δ2H and the δ18O in human blood plasma are associated with the human renal functions. The water isotope ratios of the δ2H and δ18O in human blood plasma of the control subjects are comparable to those of the diabetes subjects (with healthy kidney), but are statistically higher than those of the end stage renal disease subjects (p<0.001 for both ANOVA and Student's t-test). In addition, our data indicate the existence of the biological homeostasis of water isotopes in all subjects, except the end stage renal disease subjects under the haemodialysis treatment. Furthermore, the unexpected water contents (δ2H and δ18O) in blood plasma of body water may shed light on a novel assessment of renal functions