Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer.

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network.

Lactic acid bacteria antimicrobial peptides (LABAMPs) are a class of active polypeptide produced during the metabolic process of lactic acid bacteria, which can inhibit or kill pathogenic bacteria or spoilage bacteria in food. LABAMPs have broad application in important practical fields closely related to human beings, such as food production, efficient agricultural planting, and so on. However, screening for antimicrobial peptides by biological experiment researchers is time-consuming and laborious. Therefore, it is urgent to develop a model to predict LABAMPs. In this work, we design a graph convolutional neural network framework for identifying of LABAMPs. We build heterogeneous graph based on amino acids, tripeptide and their relationships and learn weights of a graph convolutional network (GCN). Our GCN iteratively completes the learning of embedded words and sequence weights in the graph under the supervision of inputting sequence labels. We applied 10-fold cross-validation experiment to two training datasets and acquired accuracy of 0.9163 and 0.9379 respectively. They are higher that of other machine learning and GNN algorithms. In an independent test dataset, accuracy of two datasets is 0.9130 and 0.9291, which are 1.08% and 1.57% higher than the best methods of other online webservers.

Understanding chain folding morphology of semicrystalline polymers based on a rod-coil multiblock model.

We employ a rod-coil multiblock molecular chain model to investigate chain folding behavior, which is a significant characteristic in semicrystalline polymers, by using the method of self-consistent field theory (SCFT). Polymer chains with different conformations in crystalline and amorphous regions are described by rigid rod chains and flexible Gaussian chains, respectively. At present, we concentrate on the thermodynamic behaviors of polymer semi-crystals after the formation of the initial lamellar crystals. A new mechanism for lamellar thickening is proposed to realize that the end of lamellar thickening depends on the crystallinity degree. In other words, it is impossible for lamellae to develop into extended-chain crystals by means of lamellar thickening if crystallinity is limited to a certain degree. We further discuss the competition between crystalline and amorphous regions and its influence on crystallization behaviors, such as the formation of double lamellae, chain tilt, the anomalies and adjacent re-entry. The synergistic influences of the driving force of crystallization, interfacial energy and crystallinity degree on chain folding behavior are also investigated when the density anomalies in amorphous regions are excluded. Our model demonstrates advantages in accurately describing the mesoscopic layered structures of semicrystalline polymers based upon a microscopic chain model and provides at least a semi-quantitative thermodynamic picture for chain folding.

Emergence of ordered network mesophases in kinetic pathways of order-order transition for linear ABC triblock terpolymers.

Applying the string method to the self-consistent field theory (SCFT) of ABC linear triblock copolymers, we developed a new strategy to design kinetic pathways for the formation of stable or metastable network mesophases in order-order transition (OOT) processes. The design principle regarding the kinetic pathways between distinct mesophases is based on the matching relationships of both domain spacing and dominant Fourier components of the density distributions. The results suggest that complex ordered network mesophases, such as alternating diamond (DA) and alternating plumber's nightmare (PA) could be obtained in kinetic pathways between simple phases covering lamellae, cylinders and spheres. By virtue of the minimal free energy pathway (MEP) obtained, we could acquire the epitaxial relationship and phase transition mechanism. Furthermore, we managed to regulate the MEP by changing the block composition to adjust packing frustration. Two new metastable networks, core-shell five-pronged and six-pronged morphologies, were found in the kinetic pathways, further demonstrating the regulating mechanism. The results will contribute to a better understanding of the kinetic relationship between simple phases and complex networks, thus providing a platform for soft materials design via the OOT route and guiding experimental procedures to fabricate ordered network mesophases.