01434nas a2200157   4500008004100000022001600041245007800057210006900135260000800204300002900212490000700241520087200248100002601120700002101146856010901167       2018                        eng d  a 1471-4922 00aPerspectives on leishmania species and stage-specific adaptive mechanisms0 aPerspectives on leishmania species and stagespecific adaptive me  cDEC  a10.1016/j.pt.2018.09.0040 v343 aThe hurdles in drug and vaccine development pipelines for leishmaniasis, a complex, multifaceted disease, are largely due to the digenetic lifecycle, differential clinical manifestations of the parasite, and the incomplete understanding of its adaptations within its hosts. Here, we discuss the distinct computational and experimental techniques employed to identify the species and stage-specific adaptive mechanisms at different levels of biological organization, the progress made so far, and limitations in comprehending leishmaniasis as a systems biology disease. Based on the available perspectives, we also provide suggestions and requirements to tackle the growing challenges for bridging the genotype with the phenotype. A systems perspective can be instrumental in understanding the complexities of the disease and can provide insights for targeted control.1 aSubramanian, Abhishek1 aSarkar, Ram, Rup  uhttp://library.ncl.res.in/content/perspectives-leishmania-species-and-stage-specific-adaptive-mechanisms02330nas a2200265   4500008004100000022001400041245012500055210006900180260000800249300001400257490000700271520136700278653004601645653001201691653003401703653003001737653005201767653002101819100001901840700001801859700001801877700001901895700002101914856012901935       2020                        eng d  a1747-027700aPrediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: an AI-based approach0 aPrediction of survival rate and effect of drugs on cancer patien  cSEP  a1005-10190 v963 a<p>The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.</p>
10agene expression and copy number variation10agliomas10agrade and survival prediction10amachine learning strategy10asignificant gene prediction and effect of drugs10asomatic mutation1 aSaurabh, Rochi1 aNandi, Sutanu1 aSinha, Noopur1 aShukla, Mudita1 aSarkar, Ram, Rup  uhttp://library.ncl.res.in/content/prediction-survival-rate-and-effect-drugs-cancer-patients-somatic-mutations-genes-ai-based