A comparative analysis of soft computing techniques for gene prediction.

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided.

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnostic tool that accurately detects glucose metabolism in the brain of AD patients. MATERIAL AND METHODS: In this work, we have developed a deep neural network using FDG-PET to discriminate Alzheimer's disease subjects from stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (pMCI), and cognitively normal (CN) cohorts. A total of 83 FDG-PET scans are collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 21 subjects with CN, 21 subjects with sMCI, 21 subjects with pMCI, and 20 subjects with AD. RESULTS: The method has achieved remarkable accuracy rates of 99.31% for CN vs. AD, 99.88% for CN vs. MCI, 99.54% for AD vs. MCI, and 96.81% for pMCI vs. sMCI. Based on the experimental results. CONCLUSION: The results show that the proposed method has a significant generalisation ability as well as good performance in predicting the conversion of MCI to AD even in the absence of direct information. FDG-PET is a well-known biomarker for the identification of Alzheimer's disease using transfer learning.

Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.

DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.

PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine learning methods. From epigenetic studies, it has been demonstrated that there is a close association between aging and DNA methylation. Most of the existing studies focused on healthy samples, whereas diseases may have a significant impact on human age. Therefore, in this article, an age prediction model is proposed using DNA methylation biomarkers for healthy and diseased samples. METHODS: The dataset contains 454 healthy samples and 400 diseased samples from publicly available sources with age (1-89 years old). Six CpG sites are identified from this data having a high correlation with age using Pearson's correlation coefficient. In this work, the age prediction model is developed using four different machine learning techniques, namely, Multiple Linear Regression, Support Vector Regression, Gradient Boosting Regression, and Random Forest Regression. Separate models are designed for healthy and diseased data. The data are split randomly into 80 : 20 ratios for training and testing, respectively. RESULTS: Among all the techniques, the model designed using Random Forest Regression shows the best performance, and Gradient Boosting Regression is the second best model. In the case of healthy samples, the model achieved a MAD of 2.51 years for training data and 4.85 for testing data. Also, for diseased samples, a MAD of 3.83 years is obtained for training and 9.53 years for testing. CONCLUSION: These results showed that the proposed model can predict age for healthy and diseased samples.

Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures.

Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.

Coding roles of long non-coding RNAs in breast cancer: Emerging molecular diagnostic biomarkers and potential therapeutic targets with special reference to chemotherapy resistance.

Dysregulation of epigenetic mechanisms have been depicted in several pathological consequence such as cancer. Different modes of epigenetic regulation (DNA methylation (hypomethylation or hypermethylation of promotor), histone modifications, abnormal expression of microRNAs (miRNAs), long non-coding RNAs, and small nucleolar RNAs), are discovered. Particularly, lncRNAs are known to exert pivot roles in different types of cancer including breast cancer. LncRNAs with oncogenic and tumour suppressive potential are reported. Differentially expressed lncRNAs contribute a remarkable role in the development of primary and acquired resistance for radiotherapy, endocrine therapy, immunotherapy, and targeted therapy. A wide range of molecular subtype specific lncRNAs have been assessed in breast cancer research. A number of studies have also shown that lncRNAs may be clinically used as non-invasive diagnostic biomarkers for early detection of breast cancer. Such molecular biomarkers have also been found in cancer stem cells of breast tumours. The objectives of the present review are to summarize the important roles of oncogenic and tumour suppressive lncRNAs for the early diagnosis of breast cancer, metastatic potential, and chemotherapy resistance across the molecular subtypes.

Intrinsic and extrinsic pathways of apoptosis: Role in cancer development and prognosis.

Apoptosis, also named programmed cell death, is a fundament process required for morphogenetic homeostasis during early development and in pathophysiological conditions. It is come into existence in 1972 by work of Kerr, Wyllie and Currie and later on investigated during the research on development of the C. elegans. Trigger by several stimuli, apoptosis is necessary during the embryonic development and aging as homeostatic mechanism to control the cell population and also play a key role as defense mechanism against the immune responses and elimination of damaged cells. Cancer, a genetic disease, is a growing burden on the health and economy of both developing and developed countries. Every year there is tremendously increasing in the number of new cancer cases and mortality rate. Although, there is a significant improvement have been made in biotechnological and bioinformatic fields however, the therapeutic advantages and cancer etiology is still under explored. Several studies determined the deregulation of different apoptotic components during the cancer development and progression. Apoptosis relies on activation of distinct signaling pathways that are often deregulated in cancer. Thus, exploring the single or more than one apoptotic component underlying their expression in carcinogenesis could help to track the disease progression. Current book chapter will provide the several evidences supporting the use of different apoptotic components as prognosis and prediction markers in various human cancer types.

Oncogenic and Tumor Suppressive Components of the Cell Cycle in Breast Cancer Progression and Prognosis.

Cancer, a disease of inappropriate cell proliferation, is strongly interconnected with the cell cycle. All cancers consist of an abnormal accumulation of neoplastic cells, which are propagated toward uncontrolled cell division and proliferation in response to mitogenic signals. Mitogenic stimuli include genetic and epigenetic changes in cell cycle regulatory genes and other genes which regulate the cell cycle. This suggests that multiple, distinct pathways of genetic alterations lead to cancer development. Products of both oncogenes (including cyclin-dependent kinase (CDKs) and cyclins) and tumor suppressor genes (including cyclin-dependent kinase inhibitors) regulate cell cycle machinery and promote or suppress cell cycle progression, respectively. The identification of cyclins and CDKs help to explain and understand the molecular mechanisms of cell cycle machinery. During breast cancer tumorigenesis, cyclins A, B, C, D1, and E; cyclin-dependent kinase (CDKs); and CDK-inhibitor proteins p16, p21, p27, and p53 are known to play significant roles in cell cycle control and are tightly regulated in normal breast epithelial cells. Following mitogenic stimuli, these components are deregulated, which promotes neoplastic transformation of breast epithelial cells. Multiple studies implicate the roles of both types of components-oncogenic CDKs and cyclins, along with tumor-suppressing cyclin-dependent inhibitors-in breast cancer initiation and progression. Numerous clinical studies have confirmed that there is a prognostic significance for screening for these described components, regarding patient outcomes and their responses to therapy. The aim of this review article is to summarize the roles of oncogenic and tumor-suppressive components of the cell cycle in breast cancer progression and prognosis.

Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma.

Accounting for nine out of ten kidney cancers, kidney renal cell carcinoma (KIRC) is by far the most common type of kidney cancer. In view of limited and ineffective available therapies, understanding the genetic basis of disease becomes important for better diagnosis and treatment. The present studies are based on a single type of genomic data. These studies do not consider interactions between genomic data types and their underlying biological relationships in the disease. However, the current availability of multiple genomic data and the possibility of combining it have facilitated a better understanding of the cancer's characterization. But high dimensionality and the existence of complex interactions (within and between genomic data types) are the two main challenges of integrative methods to analyze cancer effectively. In this paper, we propose a method to build an integrative model based on Bayesian model averaging procedure for improved prediction of clinical outcome in cancer survival. The proposed method initially uses dimensionality reduction techniques to generate low-dimensional latent features for the predictive models and then incorporates interactions between them. It defines the latent features using principal components and their sparse version. It compares the predictive performance of models based on these two latent features on real data. These models also validate several ccRCC-specific cancer biomarkers previously reported in the literature. Applied on kidney renal cell carcinoma (KIRC) dataset of The Cancer Genome Atlas (TCGA), the method achieves better prediction with sparse principal components model by including latent feature interactions as compared to without including them.

Global sequence features based translation initiation site prediction in human genomic sequences.

Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.

Oncogenic and Tumor-Suppressive Roles of MicroRNAs with Special Reference to Apoptosis: Molecular Mechanisms and Therapeutic Potential.

MicroRNAs (miRNAs) are the non-coding class of minute RNA molecules that negatively control post-transcriptional regulation of various functional genes. These miRNAs are transcribed from the loci present in the introns of functional or protein-coding genes, exons of non-coding genes, or even in the 3'-untranslated region (3'-UTR). They have potential to modulate the stability or translational efficiency of a variety of target RNA [messenger RNA (mRNA)]. The regulatory function of miRNAs has been elucidated in several pathological conditions, including neurological (Alzheimer's disease and Parkinson's disease) and cardiovascular conditions, along with cancer. Importantly, miRNA identification in cancer progression and invasion has evolved as an incipient era in cancer treatment. Several studies have shown the influence of miRNAs on various cancer processes, including apoptosis, invasion, metastasis and angiogenesis. In particular, apoptosis induction in tumor cells through miRNA has been extensively studied. The biphasic mode (up- and down-regulation) of miRNA expression in apoptosis and other cancer processes has already been determined. The findings of these studies could be utilized to develop potential therapeutic strategies for the management of various cancers. The present review critically describes the oncogenic and tumor suppressor role of miRNAs in apoptosis and other cancer processes, therapy resistance, and use of their presence in the body fluids as biomarkers.

Role of DNA methylation in human age prediction.

DNA methylation (DNAm) is a fundamental biochemical modification which occurs over the lifespan of an individual and it is a significant component in the aging process. Methylation level of CpG sites plays an important role in finding the age of a person. Different approaches were used to extract age-related CpG (AR-CpG) sites for developing age-prediction models because the level of methylation is directly related to age for some CpG sites. Several age prediction models have been developed over the past few years and most of these models were based on regression techniques. This paper reviews and analyzes age-prediction models developed using methylated data. The first section introduces the problem of aging in humans. In the next section, the concept of DNA methylation is described in detail. Thereafter, DNA methylation based age-prediction methods are discussed. In addition to this, a summary of all these methods is also provided. Finally, some limitations of the current research along with future directions are given.

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.