PubMed Health⌕ Search

Biomedical subjects

Hsin-Nan Lin

Publications and source records attributed to Hsin-Nan Lin.

8 recordsLinked to original sources
PubMed2005

GANA--a genetic algorithm for NMR backbone resonance assignment.

NMR data from different experiments often contain errors; thus, automated backbone resonance assignment is a very challenging issue. In this paper, we present a method called GANA that uses a genetic algorithm to automatically perform backbone resonance assignment with a high degree of precision and recall. Precision is the number of correctly assigned residues divided by the number of assigned residues, and recall is the number of correctly assigned residues divided by the number of residues with known human curated answers. GANA takes spin systems as input data and uses two data structures, candidate lists and adjacency lists, to assign the spin systems to each amino acid of a target protein. Using GANA, almost all spin systems can be mapped correctly onto a target protein, even if the data are noisy. We use the BioMagResBank (BMRB) dataset (901 proteins) to test the performance of GANA. To evaluate the robustness of GANA, we generate four additional datasets from the BMRB dataset to simulate data errors of false positives, false negatives and linking errors. We also use a combination of these three error types to examine the fault tolerance of our method. The average precision rates of GANA on BMRB and the four simulated test cases are 99.61, 99.55, 99.34, 99.35 and 98.60%, respectively. The average recall rates of GANA on BMRB and the four simulated test cases are 99.26, 99.19, 98.85, 98.87 and 97.78%, respectively. We also test GANA on two real wet-lab datasets, hbSBD and hbLBD. The precision and recall rates of GANA on hbSBD are 95.12 and 92.86%, respectively, and those of hbLBD are 100 and 97.40%, respectively.

Algorithms↗
PubMed2005

HYPROSP II--a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence.

MOTIVATION: In our previous approach, we proposed a hybrid method for protein secondary structure prediction called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate, to determine whether PROSP or PSIPRED is to be used for the prediction of a target protein. HYPROSP made slight improvement of Q(3) over PSIPRED because PROSP predicted well for proteins with match rate >80%. As the portion of proteins with match rate >80% is quite small and as the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II that is based on a new quantitative measure called local match rate. RESULTS: Local match rate indicates the amount of structural information that each amino acid can extract from the knowledge base. With the local match rate, we are able to define a confidence level of the PROSP prediction results for each amino acid. Our new hybrid approach, HYPROSP II, is proposed as follows: for each amino acid in a target protein, we combine the prediction results of PROSP and PSIPRED using a hybrid function defined on their respective confidence levels. Two datasets in nrDSSP and EVA are used to perform a 10-fold cross validation. The average Q(3) of HYPROSP II is 81.8% and 80.7% on nrDSSP and EVA datasets, respectively, which is 2.0% and 1.1% better than that of PSIPRED. For local structures with match rate >80%, the average Q(3) improvement is 4.4% on the nrDSSP dataset. The use of local match rate improves the accuracy better than global match rate. There has been a long history of attempts to improve secondary structure prediction. We believe that HYPROSP II has greatly utilized the power of peptide knowledge base and raised the prediction accuracy to a new high. The method we developed in this paper could have a profound effect on the general use of knowledge base techniques for various predictionalgorithms. AVAILABILITY: The Linux executable file of HYPROSP II, as well as both nrDSSP and EVA datasets can be downloaded from http://bioinformatics.iis.sinica.edu.tw/HYPROSPII/.

Algorithms↗
PubMed2004

HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach.

We develop a knowledge-based approach (called PROSP) for protein secondary structure prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can extract from the knowledge base. Our experimental results show that proteins with a higher match rate will likely be predicted more accurately based on PROSP. That is, there is roughly a monotone correlation between the prediction accuracy and the amount of structure matching with the knowledge base. To fully utilize the strength of our knowledge base, a hybrid prediction method is proposed as follows: if the match rate of a target protein is at least 80%, we use the extracted information to make the prediction; otherwise, we adopt a popular machine-learning approach. This comprises our hybrid protein structure prediction (HYPROSP) approach. We use the DSSP and EVA data as our datasets and PSIPRED as our underlying machine-learning algorithm. For target proteins with match rate at least 80%, the average Q3 of PROSP is 3.96 and 7.2 better than that of PSIPRED on DSSP and EVA data, respectively.

Algorithms↗
PubMed2003

Amisulpride versus risperidone in the treatment of schizophrenic patients: a double-blind pilot study in Taiwan.

BACKGROUND AND PURPOSE: The atypical antipsychotics, amisulpride and risperidone, have different receptor affinity characteristics. Although the relative efficacy of both drugs compared to conventional antipsychotics is well established, it remains unclear how the efficacy of amisulpride compares with risperidone. There have been no controlled studies comparing amisulpride to risperidone in Asian patients. The purpose of this study was to compare the efficacy and safety of amisulpride with that of risperidone in Taiwanese schizophrenic patients. METHODS: Patients with productive positive symptoms (n = 48) were enrolled into this double-blind, randomized pilot study for 6 weeks. Patients received either amisulpride (400-800 mg/day) or risperidone (4-8 mg/day). Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression (CGI), Social and Occupational Functioning Assessment Scale (SOFAS), and patients' subjective responses to treatment were assessed during the trial period. Adverse events were recorded at each follow-up visit. RESULTS: At the end of the trial, the mean dosage was 630 +/- 134 mg/day and 6.88 +/- 1.54 mg/day for amisulpride and risperidone, respectively. There was no significant difference in the reduction of the PANSS total score (amisulpride -24.1 versus risperidone -28.4, p = 0.999), the PANSS positive subscale score (amisulpride -6.8 versus risperidone -8.3, p = 0.467), the PANSS negative subscale score (amisulpride -5.6 versus risperidone -6.4, p = 0.999), or the CGI score between the two groups. The extrapyramidal symptom ratings, the improvement in the SOFAS (amisulpride 11.1 versus risperidone 10.0) and the subjective response (amisulpride 82% versus risperidone 83%) were comparable. No serious adverse events were recorded in either treatment group. There was a statistically significant body weight gain in the risperidone group. In contrast, there was a statistically, though not clinically, significant reduction of blood pressure and heart rate in the amisulpride group. CONCLUSIONS: This study suggests that amisulpride is as effective as risperidone in the treatment of patients with schizophrenia. Both drugs were well tolerated, but had different side effect profiles.

Amisulpride↗
PubMed2003

A new similarity measure among protein sequences.

Protein sequence analysis is an important tool to decode the logic of life. One of the most important similarity measures in this area is the edit distance between amino acids of two sequences. We believe this criterion should be reconsidered because protein features are probably associated more with small peptide fragments than with individual amino acids. In this paper, we design small patterns that are associated with highly conversed regions among a set of protein sequences. These patterns are used analogous to the index terms in information retrieval. Therefore, we do not consider gaps within patterns. This new similarity measure has been applied to phylogenetic tree construction, protein clustering and protein secondary structure prediction and has produced promising results.

Algorithms↗
PubMed2002

Symptom patterns and subgrouping of schizophrenic patients: significance of negative symptoms assessed on admission.

This study subgroups schizophrenic patients based on symptoms assessed on admission and examines the validity of the subgrouping using follow-up data and other clinical outcome variables. Schizophrenic patients (n=163) from consecutive admission received ratings on the positive and negative syndrome scale (PANSS) on admission and during a 1-year follow-up course. An exploratory graphic analysis on the admission PANSS derived four symptom dimensions: negative symptoms, disorganized thought, hostility/excitement and delusions/hallucinations. This yielded two subgroups of patients on admission, a group with marked negative (GWNEG) and a group without marked negative (GONEG) symptoms. Compared with the GONEG, the GWNEG had a poorer recovery rate, more impairment in attention, a slower response of the delusion/hallucination symptoms to neuroleptic treatment and a longer duration of index hospitalization. At a one-year follow-up, the GWNEG assessed on admission had persistently higher scores on the negative symptom and disorganized thought syndromes, less relapse rate, a shorter duration on job, as well as worse social functioning than the GONEG. Thus, the GONEG might comprise patients having a pure paranoid syndrome with quick and better treatment response, while the GWNEG comprises patients with the blunt-disorganization syndrome having a poorer outcome.

Adult↗
PubMed2002

Detecting drug-drug interactions in medication profiles of psychiatric inpatients: a two-stage approach.

A two-stage evaluation methodology was developed to detect drug-drug interactions (D-DIs) of moderate and major severity in a psychiatric inpatient population. During a 3-month study period, the medication orders prescribed for 152 psychiatric inpatients in two psychiatric wards of a medical center were examined. A primary assessment of patients' reactions due to drug interactions was carried out by physicians who were provided with pertinent drug interaction monographs. A secondary assessment was performed by a three-member committee using explicit criteria to decide which reactions were actual D-DIs. Potential moderate or major severity D-DIs were identified in 79 (52.0%) of the 152 patients. A total of 130 potential interactions were detected in 339 (63.1%) of the 537 medication profiles. Actual D-DIs were seen in two patients (1.3%) who were prescribed trazodone with chlorpromazine and doxepin with paroxetine, respectively. The discrepancy between incidences of potential and actual D-DIs suggests that the method was sensitive in the detection and prudent in the assessment of D-DIs.

Drug Interactions↗