Abed Alkhateeb

Identifying differentially expressed transcripts associated with prostate cancer progression using RNA-Seq and machine learning techniques

Background: Prostate cancer is complicated by a high level of unexplained variability in the aggressiveness of newly diagnosed disease. Given that this is one of the most prevalent cancers worldwide, finding biomarkers to effectively stratify high risk patient populations is a vital next step in improving survival rates and quality of life after treatment. Materials and Methods: In this study, we selected a dataset consisting of 106 prostate cancer samples, which represent various stages of prostate cancer and developed by RNA-Seq technology. Our objective is to identify differentially expressed transcripts associated with prostate cancer progression using pair-wise stage comparisons. Results: Using machine learning techniques, we identified 44 transcripts that are correlated to different stages of progression. Expression of an identified transcript, USP13, is reduced in stage T3 in comparison with stage T2c, a pattern also observed in breast cancer tumourigenesis. We also identified another differentially expressed transcript, PTGFR, which has also been reported to be involved in prostate cancer progression and has also been linked to breast, ovarian and renal cancers. Conclusions: The results support the use of RNA-Seq along with machine learning techniques as an essential tool in identifying potential biomarkers for prostate cancer progression. Further studies elucidating the biochemical role of identified transcripts in vitro are crucial in validating the use of these biomarkers in the prediction of disease progression and development of effective therapeutic strategies.

Obtaining biomarkers in cancer progression from outliers of time-series clusters

Studying the expression of transcripts throughout the various stages of prostate cancer may provide insight into the factors that influence the progression of the disease. Moreover, it may also reveal outlier transcripts, which have different trends than the majority of the transcripts. In this study, we use a time-series profile hierarchical clustering method to separate dissimilar groups of aligned transcripts that have maximum distance with the other group expression patterns throughout the various stages/sub-stages of prostate cancer progression. The isolated outliers can serve as biomarkers in analyzing different stages/sub-stages. This paper suggests that the combination of proper clustering, distance function and index validation for clusters are suitable model to find a pattern of trending for transcript abundance throughout different prostate cancer stages/sub-stages. The stages/sub-stages represent the time points, and the growth of the transcript abundance throughout those time points are cubic spline interpolated. The trending throughout those stages can lead to understanding the relationships among the transcripts and provide a better analysis of prostate cancer development through stages.

Machine Learning Model for Identifying Gene Biomarkers for Breast Cancer Treatment Survival

Studying the breast cancer survival genes information will help to enhance the treatment and save more patents life by identifying the genes biomarker to recommend the proper treatment type. That is why it is now a great challenge for researchers to have more research on breast cancer specially with the great enhancement in the fields of bioinformatics, data mining, and machine learning techniques which were a new revolution in the cancer treatment. A dataset contains the survival information and treatments methods for 1980 female breast cancer patient is used for building the prediction model, the gene expression are the features of the learning model [1], where the combination of the survival and treatments information are the classes. A hierarchal model that consists of hybrid feature selection and classification method are utilized to differentiate a class from the rest of the classes. The results show that a few number of gene biomarkers (gene signature) at each node which can determine the class with accuracy around 99% for survival living / deceased based on treatments which is vital to ensure that the patients will have the best potential response to a specific therapy. This signatures will be used as a predictor of survival in breast cancer.

Outlier Genes as Biomarkers of Breast Cancer Survivability in Time-Series Data

Studying gene expression through various time intervals of breast cancer survival may provide new insights into the recovery from the disease. In this work, we propose a hierarchical clustering method to separate dissimilar groups of gene time-series profiles, which have the furthest distances from the rest of the profiles throughout different time intervals. The isolated outliers can be used as potential biomarkers of Breast Cancer survivability. Gene expressions throughout those time points are cubic spline interpolated to create a trending profile for each gene. After universally aligning the profiles to minimize the vertical area between each pair of profiles, we cluster the genes using hierarchical clustering based on minimized vertical distances [1]. An appropriate number of clusters was chosen based on the profile alignment and agglomerative clustering (PAAC) index as well as visual observations of the clusters. Our study suggests that the combination of proper clustering, distance function and index validation for clusters is a suitable model to identify genes as informative biomarkers of breast cancer survivability.

A Machine Learning Model for Discovery of Protein Isoforms as Biomarkers

We developed a new tool that can identify open reading frames (ORFs) for a given transcript and reconstruct protein isoforms using RNA-Seq data. Moreover, we use a modified version of the measure of abundance Fragments Per Kilobase of transcript per Million mapped reads (FPKM), aka adaptive FPKM (AFPKM), which in addition to using information about the length of the transcript, it takes into the account the actual length of the ORF. Using this new expression measure, we were able to identify 10 protein isoforms that were differentially expressed in stage 2 versus further stages of prostate cancer. Using identified protein isoforms, we were able to obtain more than 97% accuracy for discriminating stage 2 from subsequent stages of prostate cancer.

Cost-sensitive classification on class-balanced ensembles for imbalanced non-coding RNA data

Many bioinformatics data sets have class-imbalanced data, where the number of samples in each class is not equal. Since most of data sets contain usual versus unusual cases, e.g. cancer versus normal or miRNAs versus other non-coding RNA, where the minority class with the least number of samples is the interesting class that contains the unusual cases. The learning models based on the standard classifiers, such as the support vector machine (SVM), random forest and k-NN are usually biased towards the majority class, which means that the classifier is most likely to predict the samples from the interesting class inaccurately. Thus, handling class-imbalanced data set has gained the researchers interests recently. A combination of proper feature selection, a cost-sensitive classifier and ensembling based on random forest method (BCE-CSC-RF) is proposed to handle the class-imbalanced data. Random class-balanced ensembles are built individually. Then, each ensemble is used as a training pool to classify the rest of out-bagged samples. Samples in each ensemble will be classified using class-sensitive classifier that incorporates random forest. The sample will be classified by selecting the most often class has been voted-for in all samples appearances in all the formed ensembles. A set of performance measurements including a geometric measurement suggests that the model can improve the classification of the minority class samples.

ZSeq 2.0: A fully automatic preprocessing method for next generation sequencing data

Preprocessing is a critical step in next generation sequencing (NGS) data analysis, since any error or artifact in library preparation and the sequencing process can affect subsequent steps, leading to possibly erroneous biological conclusions. In this work, we propose ZSeq 2.0, a fully automatic NGS preprocessing method, which combines the strength of the original ZSeq method with a free of parameters scheme that automatically detects and filters out low complexity and highly biased regions, without any need for parameter adjustment. We estimate parameters by applying dynamic penalty rates to high and low GC-content sequences. We also use a labeling rule method to detect outlier sequences that have very low NUS. Some other preprocessing features have been added to ZSeq2.0, including adapter detection and low-quality nucleotides trimming at each side of the sequence. ZSeq2.0 is publicly available and can be downloaded from http://sourceforge.net/p/ZSeq/wiki/Home/.