Search engines running on scientific liter ature have been widely used by life sci entists to find publications related to their research. However, existing search en gines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowl edge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that fac tual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively im ply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in exist ing search engines on facilitating life sci ences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of back ground documents, to support efficient ex ploration of structured factual knowledge in the unstructured literature. It also pro vides functionalities for finding distinctive entities for given entity types, and gener ating hypothetical facts to assist literature-based knowledge discovery (e.g., drug tar get prediction).