Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection.

The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively).

Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more suitable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods.

In contrast to RNA-seq analysis, which has various standard methods, no standard methods for identifying differentially methylated cytosines (DMCs) exist. To identify DMCs, we tested principal component analysis and tensor decomposition-based unsupervised feature extraction with optimized standard deviation, which has been shown to be effective for differentially expressed gene (DEG) identification. The proposed method outperformed certain conventional methods, including those that assume beta-binomial distribution for methylation as the proposed method does not require this, especially when applied to methylation profiles measured using high throughput sequencing. DMCs identified by the proposed method also significantly overlapped with various functional sites, including known differentially methylated regions, enhancers, and DNase I hypersensitive sites. The proposed method was applied to data sets retrieved from The Cancer Genome Atlas to identify DMCs using American Joint Committee on Cancer staging system edition labels. This suggests that the proposed method is a promising standard method for identifying DMCs.

Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools.

Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus Can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection.

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

The risk of tuberculosis infection in 410 Saudipatients receiving adalimumab therapy.

BACKGROUND: Adalimumab is a fully humanized monoclonal antibody inhibitor of tumor necrosis factor-a used to treat various autoimmune disorders. Adalimumab poses a risk for tuberculosis (TB) infection, especially in countries where TB is endemic. OBJECTIVE: Determine the rate of TB infection after adalimumab therapy in Saudi Arabia. DESIGN: Medical record review. SETTINGS: Tertiary care center in Riyadh. PATIENTS AND METHODS: Demographic and clinical data were retrieved from the electronic healthcare records of all patients who received adalimumab treatment from 2015 to 2019. MAIN OUTCOME MEASURES: Occurrence of TB after adalimumab therapy. SAMPLE SIZE: 410 patients (median ([QR] age, 37 [28], range 4-81 years), 40% males RESULTS: Rheumatoid arthritis was the most frequent indication (n=153, 37%). The patients were followed for a mean of 36 (8.9) months. No case of TB infection or reactivation was observed. An inter-feron-gamma release assay (IGRA) was requested in 353/391 (90.3%) patients, prior to initiating therapy. The IGRA was positive in 26 cases (6.6%). The IGRA-positive patients received isoniazid prophylactically. Bacterial infectious complications of adalimumab therapy occurred in 12 (2.9%) patients. Urinary tract infection was the most frequent complication (culture requested in 48 patients, positive in 8). CONCLUSION: Adalimumab treatment was not associated with a risk of TB disease or TB reactivation in our cohort over the follow-up observation period. No TB reactivation occurred with adalimumab therapy when TB prophylaxis was used. The positive IGRA rate in patients on adalimumab treatment was low (7%). LIMITATIONS: Single center and one geographical area in Saudi Arabia. CONFLICT OF INTEREST: None.

Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data.

The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.

A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction.

BACKGROUND: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. METHOD: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. RESULTS: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. CONCLUSIONS: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

Clinical intelligence: New machine learning techniques for predicting clinical drug response.

Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical drug response correctly, clinicians are able to understand patient-to-patient differences in drug sensitivity outcomes, which in turn results in lesser time spent and lower cost associated with identifying effective drug candidates. Although technological advances in high-throughput drug screening in cells led to the generation of a substantial amount of relevant data, the analysis of such data would be a challenging task. There is a critical need for advanced machine learning (ML) algorithms to generate accurate predictions of clinical drug response. A major goal of this work is to provide advanced ML tools to data analysts, who would in turn build prediction calculators to be incorporated into intelligent clinical decision support systems. Such innovative tools could be used to enhance patient-care, among other uses. To achieve this goal, we develop new ML techniques, including a transfer learning approach coupled with or without a boosting technique. Experimental results on real clinical data pertaining to breast cancer, multiple myeloma, and triple-negative cancer patients demonstrate the effectiveness and superiority of the proposed approaches compared to baseline approaches, including existing transfer learning methods.

Boosting support vector machines for cancer discrimination tasks.

Cancer is a complex disease that is caused by rapid alteration of genes. Prediction of the state of cancer in advance contributes to a better understanding of its mechanism and improves the cancer therapy process. For example, predicting the malignancy of tumors in advance can prevent the development of cancer through the early treatment and clinical management of tumor progression. Despite generation of extensive clinical data obtained from the high-throughput technologies, it is necessary to develop machine learning algorithms to guide the prediction process. In the study, we utilize boosting and develop three computational methods to increase the performance of support vector machines (SVM). The aforementioned methods improve the performance over existing state-of-the-art algorithms, including SVM and xgboost. We evaluate the proposed boosting approach relative to the existing algorithms by using several gene expression data related to oral cancer, breast cancer, pheochromocytomas and paragangliomas, bladder cancer, and gastric cancer. The reported results using several performance measures indicate that algorithms employing the proposed approach outperform algorithms employing the baseline approach.

A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction.

Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training set. Third, we train machine learning algorithms using both the target training set and the aligned examples. We evaluate the performance of our approach against baseline approaches using the Area Under the receiver operating characteristic (ROC) Curve (AUC) on real clinical trial datasets pertaining to multiple myeloma, nonsmall cell lung cancer, triple-negative breast cancer, and breast cancer. Experimental results show that our approach is better than the baseline approaches in terms of performance and statistical significance.

A link prediction approach to cancer drug sensitivity prediction.

BACKGROUND: Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. RESULTS: In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. CONCLUSIONS: We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Evaluation of Cytotoxic and Genotoxic Effects of Euphorbia Triaculeata Forssk. Extract

Objective: To evaluate the cytotoxic and genotoxic activity of Euphorbia triaculeata Forssk. plant extract from Jazan region, Saudi Arabia, in an in vitro cancer model, which could be beneficial in anticancer therapy against human breast cancer cell line (MCF-7), prostate cell line (PC-3), human hepatocellular carcinoma cell line (HEPG2) and normal breast epithelial cell line (MCF-10A). The human foreskin fibroblast cell line, (Hs68), was also included in the cell panel. Doxorubicin and 5-Flurouracil, broad-spectrum anticancer drugs, were used as the positive control. Methods: Cytotoxicity of Euphorbia triaculeata plant extract was investigated by employing MTT assay and the genotoxicity was assessed by using comet assay. Results: Both toxicity tests exhibited significant toxicity results. In the comet assay, the Euphorbia triaculeata extract exhibited genotoxic effects against MCF-7 DNA and PC 3 but not on HEPG2 cell lines in a time-dependent manner by increasing the mean percentage of DNA damage. Euphorbia triaculeata extract showed significant toxicity against cancer cells. Comparison with positive control signifies that cytotoxicity exhibited by methanol extract might have moderate activity. Conclusion: The present work confirmed the cytotoxicity and genotoxicity of Euphorbia triaculeata plant. However, the observed toxicity of this plant extract needs to be confirmed by additional studies. Based on our results, further examination of the potential anticancer properties of Euphorbia triaculeata plant species and the identification of the active ingredients of these extracts is warranted.

Learning approaches to improve prediction of drug sensitivity in breast cancer patients.

Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment. In this paper, we present three learning approaches to improve the prediction of breast cancer patients' response to chemotherapy drug: the instance selection approach, the oversampling approach, and the hybrid approach. We evaluate the performance of our approaches and compare them against the baseline approach using the Area Under the ROC Curve (AUC) on clinical trial data, in addition to testing the stability of the approaches. Our experimental results show the stability of our approaches giving the highest AUC with statistical significance.

Bilateral acute angle closure glaucoma after hyperopic LASIK correction.

Acute angle closure glaucoma is unexpected complication following laser in situ keratomileusis (LASIK). We are reporting a 49-years-old lady that was presented to the emergency department with acute glaucoma in both eyes soon after LASIK correction. Diagnosis was made on detailed clinical history and examination, slit lamp examination, intraocular pressure measurement and gonioscopy. Laser iridotomy in both eyes succeeded in controlling the attack and normalizing the intraocular pressure (IOP) more than 6 months of follow-up. Prophylactic laser iridotomy is essential for narrow angle patients before LASIK surgery if refractive laser surgery is indicated.