Identification of trans-2-cis-8-Matricaria-ester from the Essential Oil of Erigeron multiradiatus and Evaluation of Its Antileishmanial Potential by in Vitro and in Silico Approaches.

The essential oil (EO) composition of the aerial parts of Erigeron multiradiatus (Lindl.ex DC.) Benth growing wild in the central Himalayan region of Uttarakhand, India, was analyzed by capillary gas chromatography with a flame ionization detector and gas chromatography-mass spectrometry. A sum of 12 constituents was identified, representing 97.81% of the oil composition. The oil was composed mainly of oxygenated monoterpenes (88.95%), sesquiterpene hydrocarbons (5.61%), oxygenated sesquiterpenes (3.05%), and monoterpene hydrocarbons (0.20%). Major constituents identified were trans-2-cis-8-matricaria-ester (77.79%), cis-lachnophyllum ester (11.04%), zingiberene (4.43%), and spathulenol (1.59%). Further, the leishmanicidal effect of EO and the purified compound trans-2-cis-8-matricaria-ester has been investigated against Leishmania donovani promastigotes and intracellular amastigotes. EO and trans-2-cis-8-matricaria-ester were safer for the hamster peritoneal macrophage and lethal to promastigotes and intracellular amastigotes at different concentrations. Further, using an in silico approach, these four compounds were tested against 10 major proteins of L. donovani associated with its virulence. Out of them, only trans-2-cis-8-matricaria-ester was found to be effective against the four target proteins, namely, l-asparaginase-1-like protein, metacaspase 2, metacaspase 1, and DNA topoisomerase II of L. donovani. The results indicate that EO contains trans-2-cis-8-matricaria-ester as a major component and showed antileishmanial activity which may facilitate discovery of new lead molecules for developing herbal medicines against visceral leishmaniasis.

Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.

We develop a pipeline to mine complex drug interactions by combining different similarities and interaction types (molecular, structural, phenotypic, genomic etc). Our goal is to learn an optimal kernel from these heterogeneous similarities in a supervised manner. We formulate an extensible framework that can easily integrate new interaction types into a rich model. The core of our pipeline features a novel kernel-learning approach that tunes the weights of the heterogeneous similarities, and fuses them into a Similarity-based Kernel for Identifying Drug-Drug interactions and Discovery, or SKID3. Experimental evaluation on the DrugBank database shows that SKID3 effectively combines similarities generated from chemical reaction pathways (which generally improve precision) and molecular and structural fingerprints (which generally improve recall) into a single kernel that gets the best of both worlds, and consequently demonstrates the best performance.

Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.

Parkinson's, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinsons Progression Markers Initiative(PPMI) study as input and classifies them into one of two classes: PD(Parkinson's disease) and HC(Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson's disease with the involvement of domain expert during the feature selection process. We evaluate our approach on 1194 patients acquired from Parkinsons Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.