Biomedical subjects

Jianhua Yao

Publications and source records attributed to Jianhua Yao.

11 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2006

CT colonography with computer-aided polyp detection: volume and attenuation thresholds to reduce false-positive findings owing to the ileocecal valve.

PURPOSE: To retrospectively identify volume and average attenuation thresholds for differentiating between ileocecal valve (ICV) and polyp at computed tomographic (CT) colonography with computer-aided detection (CAD). MATERIALS AND METHODS: Informed consent (with consent for future retrospective research) and institutional review board (IRB) approval were obtained for the original prospective study. This retrospective study had IRB approval, as well, and was HIPAA-compliant. A total of 496 patients were selected from a larger screening population. CT colonographic images from 394 patients (227 men, 167 women; mean age, 58.0 years; range, 40-79 years) were used as a training set, and images from 102 patients (76 men, 26 women; mean age, 59.8 years; range, 46-79 years) were used as a test set. A series of 2742 volume and attenuation thresholds, for which segmented findings both larger in volume and lower in average attenuation were labeled as ICVs and remaining findings were labeled polyps, were applied to the training set to determine settings with 100% sensitivity for polyp detection and the highest specificity for ICV detection. The optimal settings were then applied to the test set. Significance was assessed with the Fisher exact test, and 95% confidence intervals (CIs) were computed for sensitivity and specificity. RESULTS: A total of 386 ICVs and 67 adenomatous polyps from the training set and 102 ICVs and 138 adenomatous polyps from the test set could be segmented with a three-dimensional segmentation algorithm. When supine and prone images were counted individually, 746 nonunique ICVs from the training set and 191 from the test set were segmentable. In the training set, a volume of 600 mm(3) and an attenuation of 36 HU provided 100% sensitivity (67 polyps; 95% CI: 93%, 100%) and the optimal 83% specificity (618 of 746 ICVs; 95% CI: 80%, 85%). When applied to the test set, this combination provided 97% sensitivity (134 of 138 polyps; 95% CI: 92%, 99%) and 84% specificity (160 of 191 ICVs; 95% CI: 78%, 89%). Differences in sensitivity and specificity in the detection of polyps between the sets were not significant. CONCLUSION: Volume and average CT attenuation thresholds can help differentiate most ICVs from true polyps.

Adult↗

PubMed2006

SVM approach for predicting LogP.

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

Artificial Intelligence↗

PubMed2006

Discovery of bitriazolyl compounds as novel antiviral candidates for combating the tobacco mosaic virus.

Bitriazolyl compounds were synthesized and their activity against tobacco mosaic virus was assessed. Two of them showed promising antiviral activity and were more potent than the reference compounds. Moreover, these compounds are predicted not to be carcinogenic or mutagenic based on the prediction systems. Therefore, the bitriazolyl compounds may provide interesting new leads or scaffolds for use in further attempts to screen novel antiviral candidates.

Antiviral Agents↗

PubMed2006

Assessment of polyp and mass histopathology by intravenous contrast-enhanced CT colonography.

RATIONALE AND OBJECTIVES: We sought to demonstrate that intravenous contrast-enhanced CT colonography (CTC) can distinguish colonic adenomas from carcinomas. METHODS: Supine intravenous contrast-enhanced CTC with colonoscopic and/or surgical correlation was performed on 25 patients with colonic adenomas or carcinomas. Standard deviation of mean polyp CT attenuation was computed and assessed using ANOVA and receiver-operating characteristic analyses. RESULTS: Colonoscopy confirmed 32 polyps or masses 1 to 8 cm in size. The standard deviations of CT attenuation were carcinomas (n = 13; 36 +/- 6 HU; range 28-48 HU) and adenomas (n = 19; 49 +/- 14 HU; range 31-100 HU) (P = 0.005). At a standard deviation threshold of 42 HU, the sensitivity and specificity for classifying a polyp or mass as a carcinoma were 92% and 79%, respectively. The area under the receiver-operating characteristic curve was 0.89 +/- 0.06 (95% confidence interval 0.73-0.96). CONCLUSIONS: Measurement of the standard deviation of CT attenuation on intravenous contrast-enhanced CTC permits histopathologic classification of polyps 1 cm or larger as carcinomas versus adenomas. The presence of ulceration or absence of muscular invasion in carcinomas creates overlap with adenomas, reducing the specificity of carcinoma classification.

Adenoma↗

PubMed2006

Polyps: linear and volumetric measurement at CT colonography.

PURPOSE: To retrospectively determine which of several computed tomographic (CT) colonography-based polyp measurements is most compatible with the linear measurement at optical colonoscopy and which is best for assessing change in polyp size. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval; informed consent was obtained. Prone and supine CT colonography with same-day optical colonoscopy was performed in 216 patients (147 men and 69 women; age range, 46-79 years; mean age, 59.2 years) with 338 polyps detected at CT colonography. Polyp size was measured with three linear measurements and two volume measurements. One linear measurement and one volume measurement were performed by using automated segmentation; remaining measurements were performed manually. Compatibility with linear size at optical colonoscopy and measurement reproducibility were assessed three ways: variation from size measurement at optical colonoscopy, change between prone and supine scans, and variability between observers. Confidence analysis assessed the ability of each measurement to identify polyps with an optical colonoscopy measurement of 1 cm or greater. RESULTS: Two hundred fifty-one segmentable polyps were present on both supine and prone scans. Linear polyp diameter manually measured on a three-dimensional endoluminally viewed surface (L(M3D)) indicated with 95% confidence that a polyp measured as 0.8 cm or smaller was less than 1.0 cm at optical colonoscopy. Prone and supine polyp size difference was smallest for L(M3D) and the linear diameter computed from manual and automated volume measurements, with interquartile ranges smaller than or equal to 0.3, 0.2, and 0.5 cm, respectively. Interobserver and intraobserver variability was smallest for linear polyp diameter measurements on a two-dimensional display, with a mean percentage difference of 2.8% (95% Bland-Altman limits of agreement: -17.8%, 23.4%) and 5.0% (95% Bland-Altman limits of agreement: -28.3%, 38.3%), respectively. CONCLUSION: L(M3D) best approximated polyp size measurements at optical colonoscopy. Linear diameter calculated from automated volume measurements showed the smallest variation between supine and prone scans while avoiding observer variability and may be best for assessing polyp size changes with serial examinations.

Adult↗

PubMed2005

Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

BACKGROUND & AIMS: The sensitivity of computed tomographic (CT) virtual colonoscopy (CT colonography) for detecting polyps varies widely in recently reported large clinical trials. Our objective was to determine whether a computer program is as sensitive as optical colonoscopy for the detection of adenomatous colonic polyps on CT virtual colonoscopy. METHODS: The data set was a cohort of 1186 screening patients at 3 medical centers. All patients underwent same-day virtual and optical colonoscopy. Our enhanced gold standard combined segmental unblinded optical colonoscopy and retrospective identification of precise polyp locations. The data were randomized into separate training (n = 394) and test (n = 792) sets for analysis by a computer-aided polyp detection (CAD) program. RESULTS: For the test set, per-polyp and per-patient sensitivities for CAD were both 89.3% (25/28; 95% confidence interval, 71.8%-97.7%) for detecting retrospectively identifiable adenomatous polyps at least 1 cm in size. The false-positive rate was 2.1 (95% confidence interval, 2.0-2.2) false polyps per patient. Both carcinomas were detected by CAD at a false-positive rate of 0.7 per patient; only 1 of 2 was detected by optical colonoscopy before segmental unblinding. At both 8-mm and 10-mm adenoma size thresholds, the per-patient sensitivities of CAD were not significantly different from those of optical colonoscopy before segmental unblinding. CONCLUSIONS: The per-patient sensitivity of CT virtual colonoscopy CAD in an asymptomatic screening population is comparable to that of optical colonoscopy for adenomas > or = 8 mm and is generalizable to new CT virtual colonoscopy data.

Adenomatous Polyps↗

PubMed2004

Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models.

An automatic method to segment colonic polyps in computed tomography (CT) colonography is presented in this paper. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The computer segmentations were compared with manual segmentations to validate the accuracy of our method. An average 76.3% volume overlap percentage among 105 polyp detections was reported in the validation, which was very good considering the small polyp size. Several experiments were performed to investigate the intraoperator and interoperator repeatability of manual colonic polyp segmentation. The investigation demonstrated that the computer-human repeatability was as good as the interoperator repeatability. The polyp segmentation was also applied in computer-aided detection (CAD) to reduce the number of false positive (FP) detections and provide volumetric features for polyp classification. Our segmentation method was able to eliminate 30% of FP detections. The volumetric features computed from the segmentation can further reduce FP detections by 50% at 80% sensitivity.

Algorithms↗

PubMed2004

CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections.

The ileocecal valve (ICV) is a common cause of false-positive detections of polyps at computed tomographic (CT) colonography with computer-aided detection (CAD). The authors developed a CAD algorithm for differentiating the ICV from a true polyp and evaluated this algorithm by using two colonoscopy-confirmed CT colonography data sets. Data sets 1 and 2 consisted of the data obtained at CT colonographic examinations performed in 20 and 40 patients, respectively. Forty of these patients had at least one polyp 1 cm or larger. For data set 1, the proposed ICV recognition algorithm eliminated three of nine (33%; 95% confidence interval [CI]: 8%, 70%) false-positive CAD detections that were attributable to the ICV and none of the true-positive polyp detections. For data set 2, with use of identical parameters, the algorithm eliminated 11 of 18 (61%; 95% CI: 36%, 83%) false-positive detections that were attributable to the ICV and none of the true-positive detections. The thresholds used to recognize the ICV were a mean internal CT attenuation of less than -124 HU and a volume of greater than 1.5 cm(3). The proposed algorithm successfully recognized the ICV and eliminated it in some cases. This result is clinically important because, by reducing the frequency of a common cause of false-positive detections, this algorithm may improve the efficiency of physicians who use CAD.

Adult↗

PubMedDate not supplied

SIRS-SS: a system for simulating IR/Raman spectra. 2. Procedures and performance.

This paper is devoted to the description of procedures used in our IR/RAMAN spectrum simulation system, based on substructure/subspectrum correlations established between linked databases. The search is performed in the following order: small molecules/specific fragments/atom centered FRELs (FREL: FRagment centered on an Environment which is Limited)/bond focused FRELs. Comparative study with several reported methods has been carried out to show good performance of this software both for IR and RAMAN spectrum simulation.

Journal Article↗

PubMedDate not supplied

Superstructure searching algorithm for generic reaction retrieval.

Chemical reaction knowledge is usually summarized and retrieved by chemists from references, journals, and reaction databases. To rigorously extract chemical reaction knowledge from large data sets, computer algorithms become much more important. This paper presents a new approach, superstructure searching (SSS) algorithm, for generic reaction retrieval. The algorithm considers all known reaction patterns from the targeted structure and assigns synthetic routes for new chemical compounds. This algorithm consists of screening, atom-by-atom comparison, and computation of R-groups' similarity.

Journal Article↗