Deep Learning for Plant Species Classification Using Leaf Vein Morphometric.

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naïve-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.

A genetic programming approach to oral cancer prognosis.

BACKGROUND: The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis. METHOD: GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP. RESULT: The result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis. DISCUSSION: Some of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.

[Construction of combined site-directed random mutation libraries of recombinant human lymphotoxin].

To construct the combined site-directed random mutation library of recombinant human Lymphotoxin (rhLT) for in vitro molecular evolution study, and to study the structure and function relationship. The random point mutations at the sites of 46,106 and 130 were individually generated by overlap PCR amplification with the random nucleotide primers. The three point mutations were combined and cloned into pMD-18T vector to construct the combined mutation library. DNA sequencing was used to evaluate the diversity and randomness of the mutation sites. The combined mutation library was re-engineered, inserted in prokaryotic expression vector pBV220, transformed and expressed in Escherichia coli strain DH5alpha. The biological activity of some of the mutants was tested in 1929 mouse fibroblast cells. As much as 1.5 x 10(5) clones were obtained, which represents 4.5 times of the complete mutation libraries at 99% confidence. Sequencing 50 clones revealed no obvious bias in the nucleotide and amino acid mutations at the sites. Among the 30 expressed samples underwent for the bioassay, 70% (21 samples) were inactive, 23.3% (7 samples) had lower activity than rhLT, the remaining 6.7% (2 samples) had higher activity than rhLT. The combined site-directed random mutation library of rhLT has been constructed successfully. In combination with phase display, the library is ready for in vitro molecular evolution study.