Search

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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.

Our purpose was to evaluate the usefulness of the germination vs. the X-ray test in determining the initial viability of seeds of eight wild species ( Salvia spinosa , Salvia aegyptiaca , Ochradenus baccatus , Ochradenus arabicus , Suaeda aegyptiaca , Suaeda vermiculata , Prosopisfarcta and Panicumturgidum ) from Saudi Arabia. Several days were required to determine viability of all eight species via germination tests, while immediate results on filled/viable seeds were obtained with the X-ray test. Seeds of all the species, except Sa.aegyptiaca , showed high viability in both the germination (98-70% at 25/15 °C, 93-66% at 35/25 °C) and X-ray (100-75%) test. Furthermore, there was general agreement between the germination (10% at 25/15 °C and 8% at 35/25 °C) and X-ray (5%) tests that seed viability of Sa.aegyptiaca was very low, and X-ray analysis revealed that this was due to poor embryo development. Seeds of P.farcta have physical dormancy, which was broken by scarification in concentrated sulfuric acid (10 min), and they exhibited high viability in both the germination (98% at 25/15 °C and 93% at 35/25 °C) and X-ray (98%) test. Most of the nongerminated seeds of the eight species except those of Sa.aegyptiaca were alive as judged by the tetrazolium test (TZ). Thus, for the eight species examined, the X-ray test was a good and rapid predictor of seed viability.

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.

Tetrazolo[1,5-a]quinoline derivatives bearing in the 4-position various thiazolidinone 3a-c, 5a-c and 7a-c, thiazinone 8a,b, thiazoline 9a-d and thiadiazoline 10a,b moieties have been synthesized and evaluated for anti-inflammatory activity and antimicrobial properties. The synthetic routes involved the reaction of tetrazolo[1,5-a]quinoline-4-carboxaldehyde 1 with amines and hydrazines to give the corresponding aniles 2a-c and hydrazones 4a-c respectively The latter compounds when treated with thioglycolic acid, furnished the corresponding thiazolidinone derivatives 3a-c and 5a-c. Moreover; thiosemicarbazone 6a-c derivatives were subsequently cyclized by various reagents giving rise the title compounds. Some of the products proved to possess potent anti-inflammatory and antimicrobial properties.