Biomedical subjects

Yin Fang

Publications and source records attributed to Yin Fang.

5 recordsLinked to original sources

PubMed2026

Knowledge-guided contextual gene set analysis with large language models.

MOTIVATION: Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. RESULTS: We introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis. AVAILABILITY AND IMPLEMENTATION: The demo website is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/cGSA/, while the data and code can be accessed at https://github.com/ncbi-nlp/cGSA.

Large Language Models↗

PubMed2025

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models.

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. To address this limitation, we introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 manually curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis generation.

Journal Article↗

PubMed2006

Prolonged cognitive planning time, elevated cognitive effort, and relationship to coordination and motor control following stroke.

Understanding cortical function can provide accurately targeted interventions after stroke. Initially, stroke survivors had prolonged cognitive planning time and elevated cognitive effort, highly correlated with motor control impairments. Exploratory results suggest that neurorehabilitation, accurately targeted to dyscoordination, weakness, and dysfunctional task component execution, can improve cognitive processes controlling motor function.

Adaptation, Physiological↗

PubMed2004

Distinct brain activation patterns for human maximal voluntary eccentric and concentric muscle actions.

Eccentric muscle contractions generate greater force at a lower level of activation and subject muscles to more severe damage than do concentric actions. A recent investigation has revealed that electroencephalogram (EEG)-derived movement-related cortical potential (MRCP) is greater and occurs earlier for controlling human eccentric than concentric submaximal muscle contractions. However, whether the central nervous system (CNS) control signals for high-intensity or maximal-effort eccentric movements differ from those for concentric actions is unknown. The purpose of this study was to determine whether the MRCP signals differ between the two types of maximal-effort contractions. Eight volunteers performed 40 maximal voluntary eccentric and 40 maximal voluntary concentric elbow flexor contractions on a Kin-Com isokinetic dynamometer. Scalp EEG signals (62 channels) were measured along with force, joint angle, and electromyographic (EMG) signals of the performing muscles. MRCP-based two-dimensional brain maps were created to illustrate spatial and temporal distributions of the MRCP signals. Although the level of elbow flexor muscle activity was lower during eccentric than concentric movements, MRCP-indicated cortical activation was greater both in amplitude and area dimension for the eccentric task. Detailed comparisons of individual electrode signals suggested that eccentric movements needed a significantly longer time for early preparation and a significantly greater magnitude of cortical activity for later movement execution. The extra preparation time and higher amplitude of activation may reflect CNS activities that account for the higher risk of injury, higher degree of movement difficulty, and unique motor unit activation pattern associated with maximal-level eccentric muscle actions.

Adult↗

PubMed2004

Altered central nervous system signal during motor performance in chronic fatigue syndrome.

OBJECTIVE: The purpose of this study was to determine whether brain activity of chronic fatigue syndrome (CFS) patients during voluntary motor actions differs from that of healthy individuals. METHODS: Eight CFS patients and 8 age- and gender-matched healthy volunteers performed isometric handgrip contractions at 50% maximal voluntary contraction level. They first performed 50 contractions with a 10 s rest between adjacent trials--'Non-Fatigue' (NFT) task. Subsequently, the same number of contractions was performed with only a 5 s rest between trials--'Fatigue' (FT) task. Fifty-eight channels of surface EEG were recorded simultaneously from the scalp. Spectrum analysis was performed to estimate power of EEG frequency in different tasks. Motor activity-related cortical potential (MRCP) was derived by triggered averaging of EEG signals associated with the muscle contractions. RESULTS: Major findings include: (i) Motor performance of the CFS patients was poorer than the controls. (ii) Relative power of EEG theta frequency band (4-8 Hz) during performing the NFT and FT tasks was significantly greater in the CFS than control group (P < 0.05). (iii) The amplitude of MRCP negative potential (NP) for the combined NFT and FT tasks was higher in the CFS than control group (P < 0.05) (iv) Within the CFS group, the NP was greater for the FT than NFT task (P<0.01), whereas no such difference between the two tasks was found in the control group. CONCLUSIONS: These results clearly show that CFS involves altered central nervous system signals in controlling voluntary muscle activities, especially when the activities induce fatigue. SIGNIFICANCE: Physical activity-induced EEG signal changes may serve as physiological markers for more objective diagnosis of CFS.

Adult↗